├── .github └── workflows │ ├── Deploy.yml │ └── test-deploy.yml ├── .gitignore ├── LICENSE ├── README.md ├── babel.config.js ├── blog ├── 2019-04-05-first-blog-post.md ├── 2022-04-05-welcome │ ├── docusaurus-plushie-banner.jpeg │ └── index.md └── authors.yml ├── docs ├── AI4Science │ ├── HPC.md │ ├── Integrals.md │ ├── SciML.md │ ├── _category_.json │ ├── autodiff.md │ ├── bayesianInference.md │ ├── machinelearning.md │ ├── numericalanalysis.md │ ├── optcontrol.md │ ├── optimization.md │ ├── probablisticmachinelearning.md │ └── tensorcomputation.md ├── DataScience │ ├── R分享｜100 个统计学和 R 语言学习资源网站.md │ ├── _category_.json │ ├── datascience.md │ └── 【精心整理】2022年各专业领域全网最新最顶级的R语言新书.md ├── DataSources │ ├── DataIOtools.md │ ├── EpiData.md │ ├── JuliaDatasets.md │ ├── MLData.md │ ├── Rdatasets.md │ └── _category_.json ├── DataVisualization │ ├── _category_.json │ └── dataviz.md ├── EpiRepos │ ├── EpidemicRepos.md │ └── _category_.json ├── HowToResearch │ ├── Exhibition.md │ ├── PaperWriting.md │ ├── References.md │ └── _category_.json ├── UsefulTools │ ├── VideoTools.md │ ├── _category_.json │ └── browertools.md └── intro.md ├── docusaurus.config.js ├── package-lock.json ├── package.json ├── sidebars.js ├── src ├── components │ └── HomepageFeatures │ │ ├── index.js │ │ └── styles.module.css ├── css │ └── custom.css └── pages │ ├── index.js │ ├── index.module.css │ └── packagecollections.md └── static ├── .nojekyll └── img ├── avatar.jpg ├── docusaurus.png ├── logo.svg ├── mathepia.ico ├── tutorial ├── docsVersionDropdown.png └── localeDropdown.png ├── undraw_docusaurus_mountain.svg ├── undraw_docusaurus_react.svg └── undraw_docusaurus_tree.svg /.github/workflows/Deploy.yml: -------------------------------------------------------------------------------- 1 | name: Deploy to GitHub Pages 2 | 3 | on: 4 | push: 5 | branches: 6 | - master 7 | # Review gh actions docs if you want to further define triggers, paths, etc 8 | # https://docs.github.com/en/actions/using-workflows/workflow-syntax-for-github-actions#on 9 | 10 | jobs: 11 | deploy: 12 | name: Deploy to GitHub Pages 13 | runs-on: ubuntu-latest 14 | steps: 15 | - uses: actions/checkout@v2 16 | - uses: actions/setup-node@v3 17 | with: 18 | node-version: 16.x 19 | cache: npm 20 | 21 | - name: Install dependencies 22 | run: npm ci 23 | - name: Build website 24 | run: npm run build 25 | 26 | # Popular action to deploy to GitHub Pages: 27 | # Docs: https://github.com/peaceiris/actions-gh-pages#%EF%B8%8F-docusaurus 28 | - name: Deploy to GitHub Pages 29 | uses: peaceiris/actions-gh-pages@v3 30 | with: 31 | github_token: ${{ secrets.GITHUB_TOKEN }} 32 | # Build output to publish to the `gh-pages` branch: 33 | publish_dir: ./build 34 | # The following lines assign commit authorship to the official 35 | # GH-Actions bot for deploys to `gh-pages` branch: 36 | # https://github.com/actions/checkout/issues/13#issuecomment-724415212 37 | # The GH actions bot is used by default if you didn't specify the two fields. 38 | # You can swap them out with your own user credentials. 39 | user_name: Song921012 40 | user_email: aidishage@gmail.com -------------------------------------------------------------------------------- /.github/workflows/test-deploy.yml: -------------------------------------------------------------------------------- 1 | name: Test deployment 2 | 3 | on: 4 | pull_request: 5 | branches: 6 | - master 7 | # Review gh actions docs if you want to further define triggers, paths, etc 8 | # https://docs.github.com/en/actions/using-workflows/workflow-syntax-for-github-actions#on 9 | 10 | jobs: 11 | test-deploy: 12 | name: Test deployment 13 | runs-on: ubuntu-latest 14 | steps: 15 | - uses: actions/checkout@v2 16 | - uses: actions/setup-node@v3 17 | with: 18 | node-version: 16.x 19 | cache: npm 20 | 21 | - name: Install dependencies 22 | run: npm ci 23 | - name: Test build website 24 | run: npm run build -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Dependencies 2 | /node_modules 3 | 4 | # Production 5 | /build 6 | 7 | # Generated files 8 | .docusaurus 9 | .cache-loader 10 | 11 | # Misc 12 | .DS_Store 13 | .env.local 14 | .env.development.local 15 | .env.test.local 16 | .env.production.local 17 | 18 | npm-debug.log* 19 | yarn-debug.log* 20 | yarn-error.log* 21 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2021 Pengfei Song 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /babel.config.js: -------------------------------------------------------------------------------- 1 | module.exports = { 2 | presets: [require.resolve('@docusaurus/core/lib/babel/preset')], 3 | }; 4 | -------------------------------------------------------------------------------- /blog/2019-04-05-first-blog-post.md: -------------------------------------------------------------------------------- 1 | --- 2 | slug: first-blog-post 3 | title: First Blog Post 4 | authors: pengfei 5 | tags: [welcome, review] 6 | --- 7 | 8 | 9 | Test -------------------------------------------------------------------------------- /blog/2022-04-05-welcome/docusaurus-plushie-banner.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JuliaEpi/MathEpiDeepLearning/e667795cd42ca9ae009880fedfc3db1008d3e6c4/blog/2022-04-05-welcome/docusaurus-plushie-banner.jpeg -------------------------------------------------------------------------------- /blog/2022-04-05-welcome/index.md: -------------------------------------------------------------------------------- 1 | --- 2 | slug: welcome 3 | title: Welcome 4 | authors: pengfei 5 | tags: [welcome,review] 6 | --- 7 | 8 | [Docusaurus blogging features](https://docusaurus.io/docs/blog) are powered by the [blog plugin](https://docusaurus.io/docs/api/plugins/@docusaurus/plugin-content-blog). 9 | 10 | Simply add Markdown files (or folders) to the `blog` directory. 11 | 12 | Regular blog authors can be added to `authors.yml`. 13 | 14 | The blog post date can be extracted from filenames, such as: 15 | 16 | - `2019-05-30-welcome.md` 17 | - `2019-05-30-welcome/index.md` 18 | 19 | A blog post folder can be convenient to co-locate blog post images: 20 | 21 | ![Docusaurus Plushie](./docusaurus-plushie-banner.jpeg) 22 | 23 | The blog supports tags as well! 24 | 25 | **And if you don't want a blog**: just delete this directory, and use `blog: false` in your Docusaurus config. 26 | -------------------------------------------------------------------------------- /blog/authors.yml: -------------------------------------------------------------------------------- 1 | pengfei: 2 | name: Pengfei Song 3 | title: Maintainer of Mathepia 4 | url: https://song921012.github.io/ 5 | image_url: https://github.com/song921012.png 6 | -------------------------------------------------------------------------------- /docs/AI4Science/HPC.md: -------------------------------------------------------------------------------- 1 | --- 2 | sidebar_position: 4 3 | --- 4 | 5 | ## HPC 6 | 7 | ### Platforms, CPU, GPU and TPU 8 | 9 | Julia GPU organization 10 | 11 | [JuliaGPU](https://github.com/JuliaGPU) 12 | 13 | [JuliaParallel](https://github.com/JuliaParallel) 14 | 15 | multithread[JuliaSIMD](https://github.com/JuliaSIMD) 16 | 17 | [JuliaAstroSim/ParallelOperations.jl: Basic parallel algorithms for Julia](https://github.com/JuliaAstroSim/ParallelOperations.jl) 18 | 19 | 20 | [banyan-team/banyan-julia: A suite of libraries for processing big data with familiar Julia APIs.](https://github.com/banyan-team/banyan-julia) 21 | 22 | Python: 23 | 24 | [tonybaloney/Pyjion: Pyjion - A JIT for Python based upon CoreCLR](https://github.com/tonybaloney/Pyjion) 25 | 26 | [numba/numba: NumPy aware dynamic Python compiler using LLVM](https://github.com/numba/numba) 27 | -------------------------------------------------------------------------------- /docs/AI4Science/Integrals.md: -------------------------------------------------------------------------------- 1 | --- 2 | sidebar_position: 3 3 | --- 4 | 5 | 6 | 7 | ## Quadrature 8 | 9 | Learn One equals learn many 10 | 11 | Review[SciML/Quadrature.jl: A common interface for quadrature and numerical integration for the SciML scientific machine learning organization](https://github.com/SciML/Quadrature.jl) 12 | 13 | [SciML/QuasiMonteCarlo.jl: Lightweight and easy generation of quasi-Monte Carlo sequences with a ton of different methods on one API for easy parameter exploration in scientific machine learning (SciML)](https://github.com/SciML/QuasiMonteCarlo.jl) 14 | 15 | [SciML/SymbolicNumericIntegration.jl](https://github.com/SciML/SymbolicNumericIntegration.jl) 16 | 17 | Julia: 18 | 19 | [JuliaMath/QuadGK.jl: adaptive 1d numerical Gauss–Kronrod integration in Julia](https://github.com/JuliaMath/QuadGK.jl) 20 | 21 | [JuliaMath/HCubature.jl: pure-Julia multidimensional h-adaptive integration](https://github.com/JuliaMath/HCubature.jl) 22 | 23 | [JuliaMath/Cubature.jl: One- and multi-dimensional adaptive integration routines for the Julia language](https://github.com/JuliaMath/Cubature.jl) 24 | 25 | [giordano/Cuba.jl: Library for multidimensional numerical integration with four independent algorithms: Vegas, Suave, Divonne, and Cuhre.](https://github.com/giordano/Cuba.jl) 26 | 27 | [JuliaApproximation/FastGaussQuadrature.jl: Julia package for Gaussian quadrature](https://github.com/JuliaApproximation/FastGaussQuadrature.jl) 28 | 29 | [JuliaApproximation/ApproxFun.jl: Julia package for function approximation](https://github.com/JuliaApproximation/ApproxFun.jl) 30 | 31 | [machakann/DoubleExponentialFormulas.jl: One-dimensional numerical integration using the double exponential formula](https://github.com/machakann/DoubleExponentialFormulas.jl) 32 | 33 | [JuliaApproximation/SingularIntegralEquations.jl: Julia package for solving singular integral equations](https://github.com/JuliaApproximation/SingularIntegralEquations.jl) 34 | 35 | [JuliaGNI/GeometricIntegrators.jl: Geometric Numerical Integration in Julia](https://github.com/JuliaGNI/GeometricIntegrators.jl) 36 | 37 | [stochastics-uni-luebeck/LevyArea.jl: Iterated stochastic integrals in Julia.](https://github.com/stochastics-uni-luebeck/LevyArea.jl) 38 | 39 | #### Bayesian Methods 40 | 41 | Julia: 42 | 43 | [ranjanan/MonteCarloIntegration.jl: A package for multi-dimensional integration using monte carlo methods](https://github.com/ranjanan/MonteCarloIntegration.jl) 44 | 45 | [theogf/BayesianQuadrature.jl: Is there anything we can't make Bayesian?](https://github.com/theogf/BayesianQuadrature.jl) 46 | 47 | [s-baumann/BayesianIntegral.jl: Bayesian Integration of functions](https://github.com/s-baumann/BayesianIntegral.jl) 48 | 49 | python： 50 | 51 | [Emukit | Emukit is a highly adaptable Python toolkit for enriching decision making under uncertainty.](https://emukit.github.io/) 52 | 53 | [ProbNum — probnum 0.1 documentation](https://probnum.readthedocs.io/en/latest/) 54 | 55 | #### Expectations calculation 56 | 57 | [QuantEcon/Expectations.jl: Expectation operators for Distributions.jl objects](https://github.com/QuantEcon/Expectations.jl) 58 | -------------------------------------------------------------------------------- /docs/AI4Science/SciML.md: -------------------------------------------------------------------------------- 1 | --- 2 | sidebar_position: 10 3 | --- 4 | 5 | ## 3.8. Scientific Machine Learning (Differential Equation and ML) 6 | 7 | [Zymrael/awesome-neural-ode: A collection of resources regarding the interplay between differential equations, deep learning, dynamical systems, control and numerical methods.](https://github.com/Zymrael/awesome-neural-ode) 8 | 9 | [massastrello/awesome-implicit-neural-models](https://github.com/massastrello/awesome-implicit-neural-models) 10 | 11 | [High-Dimensional Partial Differential Equations - Deep PDE](http://deeppde.org/intro/) 12 | 13 | ### 3.8.1. Universal Differential Equations. (Neural differential equations) 14 | 15 | Julia: 16 | 17 | [SciML/DiffEqFlux.jl: Universal neural differential equations with O(1) backprop, GPUs, and stiff+non-stiff DE solvers, demonstrating scientific machine learning (SciML) and physics-informed machine learning methods](https://github.com/SciML/DiffEqFlux.jl) 18 | 19 | [avik-pal/FastDEQ.jl: Deep Equilibrium Networks (but faster!!!)](https://github.com/avik-pal/FastDEQ.jl) 20 | 21 | UDE with Gaussion Process[Crown421/GPDiffEq.jl](https://github.com/Crown421/GPDiffEq.jl) 22 | 23 | Python: 24 | 25 | [DiffEqML/torchdyn: A PyTorch based library for all things neural differential equations and implicit neural models.](https://github.com/DiffEqML/torchdyn) 26 | 27 | [rtqichen/torchdiffeq: Differentiable ODE solvers with full GPU support and O(1)-memory backpropagation.](https://github.com/rtqichen/torchdiffeq) 28 | 29 | [patrick-kidger/diffrax at zzun.app](https://github.com/patrick-kidger/diffrax?ref=zzun.app) 30 | 31 | ### 3.8.2. Physical Informed Neural Netwworks 32 | 33 | [Predictive Intelligence Lab](https://github.com/PredictiveIntelligenceLab) 34 | 35 | Julia: 36 | 37 | [SciML/NeuralPDE.jl: Physics-Informed Neural Networks (PINN) and Deep BSDE Solvers of Differential Equations for Scientific Machine Learning (SciML) accelerated simulation](https://github.com/SciML/NeuralPDE.jl) 38 | 39 | Python: 40 | 41 | [lululxvi/deepxde: Deep learning library for solving differential equations and more](https://github.com/lululxvi/deepxde) 42 | 43 | [sciann/sciann: Deep learning for Engineers - Physics Informed Deep Learning](https://github.com/sciann/sciann) 44 | 45 | ### 3.8.3. Neural Operator 46 | 47 | Julia: 48 | 49 | [foldfelis/NeuralOperators.jl: learning the solution operator for partial differential equations in pure Julia.](https://github.com/foldfelis/NeuralOperators.jl) 50 | 51 | [CliMA/OperatorFlux.jl: Operator layers for Flux.jl](https://github.com/CliMA/OperatorFlux.jl) 52 | 53 | [brekmeuris/DrMZ.jl: Deep renormalized Mori-Zwanzig (DrMZ) Julia package.](https://github.com/brekmeuris/DrMZ.jl) 54 | 55 | ### Implicit Layers 56 | [facebookresearch/theseus: A library for differentiable nonlinear optimization](https://github.com/facebookresearch/theseus) 57 | 58 | ## 3.9. Data Driven Methods (Equation Searching Methods) 59 | 60 | Julia package including SINDy, Symbolic Regression, DMD 61 | 62 | [SciML/DataDrivenDiffEq.jl: Data driven modeling and automated discovery of dynamical systems for the SciML Scientific Machine Learning organization](https://github.com/SciML/DataDrivenDiffEq.jl) 63 | 64 | [nmheim/NeuralArithmetic.jl: Collection of layers that can perform arithmetic operations](https://github.com/nmheim/NeuralArithmetic.jl) 65 | 66 | ### 3.9.1. Symbolic Regression 67 | 68 | [cavalab/srbench: A living benchmark framework for symbolic regression](https://github.com/cavalab/srbench) 69 | 70 | Python: 71 | 72 | [nasa/bingo](https://github.com/nasa/bingo) 73 | 74 | [trevorstephens/gplearn: Genetic Programming in Python, with a scikit-learn inspired API](https://github.com/trevorstephens/gplearn) 75 | 76 | [MilesCranmer/PySR: Simple, fast, and parallelized symbolic regression in Python/Julia via regularized evolution and simulated annealing](https://github.com/MilesCranmer/PySR) 77 | 78 | Julia: 79 | 80 | [MilesCranmer/SymbolicRegression.jl: Distributed High-Performance symbolic regression in Julia](https://github.com/MilesCranmer/SymbolicRegression.jl) 81 | 82 | [sisl/ExprOptimization.jl: Algorithms for optimization of Julia expressions](https://github.com/sisl/ExprOptimization.jl) 83 | 84 | ### 3.9.2. SINDy (Sparse Identification of Nonlinear Dynamics from Data) 85 | 86 | [dynamicslab/pysindy: A package for the sparse identification of nonlinear dynamical systems from data](https://github.com/dynamicslab/pysindy) 87 | 88 | [dynamicslab/modified-SINDy: Example code for paper: Automatic Differentiation to Simultaneously Identify Nonlinear Dynamics and Extract Noise Probability Distributions from Data](https://github.com/dynamicslab/modified-SINDy) 89 | 90 | ### 3.9.3. DMD (Dynamic Mode Decomposition) 91 | 92 | [mathLab/PyDMD: Python Dynamic Mode Decomposition](https://github.com/mathLab/PyDMD) 93 | 94 | [foldfelis/NeuralOperators.jl: learning the solution operator for partial differential equations in pure Julia.](https://github.com/foldfelis/NeuralOperators.jl) 95 | -------------------------------------------------------------------------------- /docs/AI4Science/_category_.json: -------------------------------------------------------------------------------- 1 | { 2 | "label": "Tools for AI4Science", 3 | "position": 7 4 | } 5 | -------------------------------------------------------------------------------- /docs/AI4Science/autodiff.md: -------------------------------------------------------------------------------- 1 | --- 2 | sidebar_position: 2 3 | --- 4 | 5 | 6 | ## Auto Differentiation 7 | 8 | ### 3.1.1. Auto Differentiation 9 | 10 | [SciML/DiffEqSensitivity.jl: A component of the DiffEq ecosystem for enabling sensitivity analysis for scientific machine learning (SciML). Optimize-then-discretize, discretize-then-optimize, and more for ODEs, SDEs, DDEs, DAEs, etc.](https://github.com/SciML/DiffEqSensitivity.jl) 11 | 12 | [EnzymeAD/Enzyme.jl: Julia bindings for the Enzyme automatic differentiator](https://github.com/EnzymeAD/Enzyme.jl) 13 | 14 | Julia: 15 | 16 | [FluxML/Zygote.jl: Intimate Affection Auditor](https://github.com/FluxML/Zygote.jl) 17 | 18 | JuliaDiffEqFlux organization 19 | 20 | [JuliaDiff](https://github.com/JuliaDiff) 21 | 22 | [JuliaDiff/ForwardDiff.jl: Forward Mode Automatic Differentiation for Julia](https://github.com/JuliaDiff/ForwardDiff.jl) 23 | 24 | [JuliaDiff/ReverseDiff.jl: Reverse Mode Automatic Differentiation for Julia](https://github.com/JuliaDiff/ReverseDiff.jl) 25 | 26 | [JuliaDiff/AbstractDifferentiation.jl: An abstract interface for automatic differentiation.](https://github.com/JuliaDiff/AbstractDifferentiation.jl) 27 | 28 | [JuliaDiff/TaylorSeries.jl: A julia package for Taylor polynomial expansions in one and several independent variables.](https://github.com/JuliaDiff/TaylorSeries.jl) 29 | 30 | [kailaix/ADCME.jl: Automatic Differentiation Library for Computational and Mathematical Engineering](https://github.com/kailaix/ADCME.jl) 31 | 32 | [chakravala/Leibniz.jl: Tensor algebra utility library](https://github.com/chakravala/Leibniz.jl) 33 | 34 | [briochemc/F1Method.jl: F-1 method](https://github.com/briochemc/F1Method.jl) 35 | 36 | Python: 37 | 38 | [google/jax: Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more](https://github.com/google/jax) 39 | 40 | [pytorch/pytorch: Tensors and Dynamic neural networks in Python with strong GPU acceleration](https://github.com/pytorch/pytorch) 41 | 42 | [tensorflow/tensorflow: An Open Source Machine Learning Framework for Everyone](https://github.com/tensorflow/tensorflow) 43 | 44 | Similar to SciMLSensitivity.jl[AMICI-dev/AMICI: Advanced Multilanguage Interface to CVODES and IDAS](https://github.com/AMICI-dev/AMICI) 45 | 46 | #### Auto Difference 47 | 48 | Julia: 49 | 50 | [SciML/DiffEqOperators.jl: Linear operators for discretizations of differential equations and scientific machine learning (SciML)](https://github.com/SciML/DiffEqOperators.jl) 51 | 52 | [QuantEcon/SimpleDifferentialOperators.jl: Library for simple upwind finite differences](https://github.com/QuantEcon/SimpleDifferentialOperators.jl) 53 | 54 | Python: 55 | 56 | [maroba/findiff: Python package for numerical derivatives and partial differential equations in any number of dimensions.](https://github.com/maroba/findiff) 57 | 58 | [PyLops/pylops: PyLops – A Linear-Operator Library for Python](https://github.com/PyLops/pylops) 59 | 60 | #### Differential Optimization (Conditional gradients) 61 | 62 | Julia: 63 | 64 | [ZIB-IOL/FrankWolfe.jl: Julia implementation for various Frank-Wolfe and Conditional Gradient variants](https://github.com/ZIB-IOL/FrankWolfe.jl) 65 | 66 | [jump-dev/DiffOpt.jl: Differentiating convex optimization programs w.r.t. program parameters](https://github.com/jump-dev/DiffOpt.jl) 67 | 68 | [gdalle/ImplicitDifferentiation.jl: Automatic differentiation of implicit functions](https://github.com/gdalle/ImplicitDifferentiation.jl) 69 | 70 | [matbesancon/MathOptSetDistances.jl: Distances to sets for MathOptInterface](https://github.com/matbesancon/MathOptSetDistances.jl) 71 | 72 | [axelparmentier/InferOpt.jl: Combinatorial optimization layers for machine learning pipelines](https://github.com/axelparmentier/InferOpt.jl) 73 | 74 | python: 75 | 76 | [cvxgrp/cvxpylayers: Differentiable convex optimization layers](https://github.com/cvxgrp/cvxpylayers) 77 | 78 | #### Subgradient, Condition, Projected, Proximal gradients 79 | 80 | Julia: 81 | 82 | Proximal: 83 | 84 | [JuliaFirstOrder/ProximalOperators.jl: Proximal operators for nonsmooth optimization in Julia](https://github.com/JuliaFirstOrder/ProximalOperators.jl) 85 | 86 | [JuliaFirstOrder/ProximalAlgorithms.jl: Proximal algorithms for nonsmooth optimization in Julia](https://github.com/JuliaFirstOrder/ProximalAlgorithms.jl) 87 | 88 | [JuliaSmoothOptimizers/ShiftedProximalOperators.jl: Proximal operators for use with RegularizedOptimization](https://github.com/JuliaSmoothOptimizers/ShiftedProximalOperators.jl) 89 | 90 | [Prox Repository](http://proximity-operator.net/scalarfunctions.html) 91 | 92 | Review[PyLops/pyproximal: PyProximal – Proximal Operators and Algorithms in Python](https://github.com/PyLops/pyproximal) 93 | 94 | Condition Gradient: 95 | 96 | [ZIB-IOL/FrankWolfe.jl: Julia implementation for various Frank-Wolfe and Conditional Gradient variants](https://github.com/ZIB-IOL/FrankWolfe.jl) 97 | 98 | #### Derivatives of Special Functions 99 | [cgeoga/BesselK.jl: An AD-compatible modified second-kind Bessel function.](https://github.com/cgeoga/BesselK.jl) 100 | -------------------------------------------------------------------------------- /docs/AI4Science/bayesianInference.md: -------------------------------------------------------------------------------- 1 | --- 2 | sidebar_position: 6 3 | --- 4 | 5 | ## 3.4. Bayesian Inference 6 | 7 | [StatisticalRethinkingJulia](https://github.com/StatisticalRethinkingJulia) 8 | 9 | [StanJulia](https://github.com/StanJulia) 10 | 11 | Julia: 12 | 13 | [The Turing Language](https://github.com/TuringLang) 14 | 15 | [cscherrer/Soss.jl: Probabilistic programming via source rewriting](https://github.com/cscherrer/Soss.jl) 16 | 17 | [probcomp/Gen.jl: A general-purpose probabilistic programming system with programmable inference](https://github.com/probcomp/Gen.jl) 18 | 19 | [Laboratory of Applied Mathematical Programming and Statistics](https://github.com/LAMPSPUC) 20 | 21 | [BIASlab](https://github.com/biaslab) 22 | 23 | [FRBNY-DSGE/DSGE.jl: Solve and estimate Dynamic Stochastic General Equilibrium models (including the New York Fed DSGE)](https://github.com/FRBNY-DSGE/DSGE.jl) 24 | 25 | [StatisticalRethinkingJulia/StatisticalRethinking.jl: Julia package with selected functions in the R package `rethinking`. Used in the SR2... projects.](https://github.com/StatisticalRethinkingJulia/StatisticalRethinking.jl) 26 | 27 | Python: 28 | 29 | [pymc-devs/pymc: Probabilistic Programming in Python: Bayesian Modeling and Probabilistic Machine Learning with Aesara](https://github.com/pymc-devs/pymc) 30 | 31 | [pints-team/pints: Probabilistic Inference on Noisy Time Series](https://github.com/pints-team/pints) 32 | 33 | [pyro-ppl/pyro: Deep universal probabilistic programming with Python and PyTorch](https://github.com/pyro-ppl/pyro) 34 | 35 | [pyro-ppl/numpyro: Probabilistic programming with NumPy powered by JAX for autograd and JIT compilation to GPU/TPU/CPU.](https://github.com/pyro-ppl/numpyro) 36 | 37 | [blackjax-devs/blackjax: BlackJAX is a sampling library designed for ease of use, speed and modularity.](https://github.com/blackjax-devs/blackjax) 38 | 39 | [tensorflow/probability: Probabilistic reasoning and statistical analysis in TensorFlow](https://github.com/tensorflow/probability) 40 | 41 | [google/edward2: A simple probabilistic programming language.](https://github.com/google/edward2) 42 | 43 | [thu-ml/zhusuan: A probabilistic programming library for Bayesian deep learning, generative models, based on Tensorflow](https://github.com/thu-ml/zhusuan) 44 | 45 | [jmschrei/pomegranate: Fast, flexible and easy to use probabilistic modelling in Python.](https://github.com/jmschrei/pomegranate) 46 | 47 | [csynbiosysIBioEUoE/BOMBs.jl: Repository for the Julia BOMBS package](https://github.com/csynbiosysIBioEUoE/BOMBs.jl) 48 | 49 | ### 3.4.1. MCMC 50 | 51 | Methods like HMC, SGLD are Covered by above-mentioned packages. 52 | 53 | Julia: 54 | 55 | [mauro3/KissMCMC.jl: Keep it simple, stupid, MCMC](https://github.com/mauro3/KissMCMC.jl) 56 | 57 | Nice[scheidan/BarkerMCMC.jl: gradient based MCMC sampler](https://github.com/scheidan/BarkerMCMC.jl) 58 | 59 | [BigBayes/SGMCMC.jl: Stochastic Gradient Markov Chain Monte Carlo and Optimisation](https://github.com/BigBayes/SGMCMC.jl) 60 | 61 | [tpapp/DynamicHMC.jl: Implementation of robust dynamic Hamiltonian Monte Carlo methods (NUTS) in Julia.](https://github.com/tpapp/DynamicHMC.jl) 62 | 63 | [madsjulia/AffineInvariantMCMC.jl: Affine Invariant Markov Chain Monte Carlo (MCMC) Ensemble sampler](https://github.com/madsjulia/AffineInvariantMCMC.jl) 64 | 65 | [TuringLang/EllipticalSliceSampling.jl: Julia implementation of elliptical slice sampling.](https://github.com/TuringLang/EllipticalSliceSampling.jl) 66 | 67 | Nested Sampling[TuringLang/NestedSamplers.jl: Implementations of single and multi-ellipsoid nested sampling](https://github.com/TuringLang/NestedSamplers.jl) 68 | 69 | [bat/UltraNest.jl: Julia wrapper for UltraNest: advanced nested sampling for model comparison and parameter estimation](https://github.com/bat/UltraNest.jl) 70 | 71 | [itsdfish/DifferentialEvolutionMCMC.jl: A Julia package for Differential Evolution MCMC](https://github.com/itsdfish/DifferentialEvolutionMCMC.jl) 72 | 73 | Python: 74 | 75 | [AdamCobb/hamiltorch: PyTorch-based library for Riemannian Manifold Hamiltonian Monte Carlo (RMHMC) and inference in Bayesian neural networks](https://github.com/AdamCobb/hamiltorch) 76 | 77 | Review[jeremiecoullon/SGMCMCJax: Lightweight library of stochastic gradient MCMC algorithms written in JAX.](https://github.com/jeremiecoullon/SGMCMCJax) 78 | 79 | Nested Sampling[joshspeagle/dynesty: Dynamic Nested Sampling package for computing Bayesian posteriors and evidences](https://github.com/joshspeagle/dynesty) 80 | 81 | [JohannesBuchner/UltraNest: Fit and compare complex models reliably and rapidly. Advanced nested sampling.](https://github.com/JohannesBuchner/UltraNest) 82 | 83 | [ruqizhang/csgmcmc: Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning](https://github.com/ruqizhang/csgmcmc) 84 | 85 | [Custom Tensorflow optimizer cSGLD (Stochastic Langevin Dynamics) in TF2: correct update ops? · Issue #2469 · tensorflow/addons](https://github.com/tensorflow/addons/issues/2469) 86 | 87 | ### 3.4.2. Approximate Bayesian Computation (ABC) 88 | 89 | Also called likelihood free or simulation based methods 90 | 91 | Review[sbi-benchmark/sbibm: Simulation-based inference benchmark](https://github.com/sbi-benchmark/sbibm) 92 | 93 | Julia: (few) 94 | 95 | [JuliaApproxInference](https://github.com/JuliaApproxInference) 96 | 97 | [tanhevg/GpABC.jl](https://github.com/tanhevg/GpABC.jl) 98 | 99 | [marcjwilliams1/ApproxBayes.jl: Approximate Bayesian Computation (ABC) algorithms for likelihood free inference in julia](https://github.com/marcjwilliams1/ApproxBayes.jl) 100 | 101 | [francescoalemanno/KissABC.jl: Pure julia implementation of Multiple Affine Invariant Sampling for efficient Approximate Bayesian Computation](https://github.com/francescoalemanno/KissABC.jl) 102 | 103 | Python: 104 | 105 | [elfi-dev/elfi: ELFI - Engine for Likelihood-Free Inference](https://github.com/elfi-dev/elfi) 106 | 107 | [eth-cscs/abcpy: ABCpy package](https://github.com/eth-cscs/abcpy) 108 | 109 | [pints-team/pints: Probabilistic Inference on Noisy Time Series](https://github.com/pints-team/pints) 110 | 111 | [mackelab/sbi: Simulation-based inference in PyTorch](https://github.com/mackelab/sbi) 112 | 113 | [ICB-DCM/pyABC: distributed, likelihood-free inference](https://github.com/ICB-DCM/pyABC) 114 | 115 | [diyabc/abcranger: ABC random forests for model choice and parameter estimation, pure C++ implementation](https://github.com/diyabc/abcranger) 116 | 117 | ### 3.4.3. Data Assimilation (SMC, particles filter) 118 | 119 | Julia: 120 | 121 | [Alexander-Barth/DataAssim.jl: Implementation of various ensemble Kalman Filter data assimilation methods in Julia](https://github.com/Alexander-Barth/DataAssim.jl) 122 | 123 | [baggepinnen/LowLevelParticleFilters.jl: Simple particle/kalman filtering, smoothing and parameter estimation](https://github.com/baggepinnen/LowLevelParticleFilters.jl) 124 | 125 | [JuliaGNSS/KalmanFilters.jl: Various Kalman Filters: KF, UKF, AUKF and their Square root variant](https://github.com/JuliaGNSS/KalmanFilters.jl) 126 | 127 | [CliMA/EnsembleKalmanProcesses.jl: Implements Optimization and approximate uncertainty quantification algorithms, Ensemble Kalman Inversion, and Ensemble Kalman Processes.](https://github.com/CliMA/EnsembleKalmanProcesses.jl) 128 | 129 | [FRBNY-DSGE/StateSpaceRoutines.jl: Package implementing common state-space routines.](https://github.com/FRBNY-DSGE/StateSpaceRoutines.jl) 130 | 131 | [simsurace/FeedbackParticleFilters.jl: A Julia package that provides (feedback) particle filters for nonlinear stochastic filtering and data assimilation problems](https://github.com/simsurace/FeedbackParticleFilters.jl) 132 | 133 | [mjb3/DiscretePOMP.jl: Bayesian inference for Discrete state-space Partially Observed Markov Processes in Julia. See the docs:](https://github.com/mjb3/DiscretePOMP.jl) 134 | 135 | Python: 136 | 137 | [nchopin/particles: Sequential Monte Carlo in python](https://github.com/nchopin/particles) 138 | 139 | [rlabbe/filterpy: Python Kalman filtering and optimal estimation library. Implements Kalman filter, particle filter, Extended Kalman filter, Unscented Kalman filter, g-h (alpha-beta), least squares, H Infinity, smoothers, and more. Has companion book 'Kalman and Bayesian Filters in Python'.](https://github.com/rlabbe/filterpy) 140 | 141 | [tingiskhan/pyfilter: Particle filtering and sequential parameter inference in Python](https://github.com/tingiskhan/pyfilter) 142 | 143 | ### 3.4.4. Variational Inference 144 | 145 | SVGD[Search · Stein Variational Gradient Descent](https://github.com/search?q=Stein+Variational+Gradient+Descent)Also see pyro, Stein method part 146 | 147 | [Red-Portal/KLpqVI.jl](https://github.com/Red-Portal/KLpqVI.jl) 148 | 149 | Julia: 150 | 151 | [bat/MGVI.jl: Metric Gaussian Variational Inference](https://github.com/bat/MGVI.jl) 152 | 153 | [TuringLang/AdvancedVI.jl: A library for variational Bayesian methods in Julia](https://github.com/TuringLang/AdvancedVI.jl) 154 | 155 | [ngiann/ApproximateVI.jl: Approximate variational inference in Julia](https://github.com/ngiann/ApproximateVI.jl) 156 | 157 | Python: 158 | 159 | ### 3.4.5. Gaussion, non-Gaussion and Kernel 160 | 161 | Julia: 162 | 163 | [Gaussian Processes for Machine Learning in Julia](https://github.com/JuliaGaussianProcesses) 164 | 165 | [Laboratory of Applied Mathematical Programming and Statistics](https://github.com/LAMPSPUC) 166 | 167 | [JuliaRobotics](https://github.com/JuliaRobotics) 168 | 169 | [JuliaStats/KernelDensity.jl: Kernel density estimators for Julia](https://github.com/JuliaStats/KernelDensity.jl) 170 | 171 | [JuliaRobotics/KernelDensityEstimate.jl: Kernel Density Estimate with product approximation using multiscale Gibbs sampling](https://github.com/JuliaRobotics/KernelDensityEstimate.jl) 172 | 173 | [theogf/AugmentedGaussianProcesses.jl: Gaussian Process package based on data augmentation, sparsity and natural gradients](https://github.com/theogf/AugmentedGaussianProcesses.jl) 174 | 175 | [JuliaGaussianProcesses/TemporalGPs.jl: Fast inference for Gaussian processes in problems involving time](https://github.com/JuliaGaussianProcesses/TemporalGPs.jl) 176 | 177 | [aterenin/SparseGaussianProcesses.jl: A Julia implementation of sparse Gaussian processes via path-wise doubly stochastic variational inference.](https://github.com/aterenin/SparseGaussianProcesses.jl) 178 | 179 | [PieterjanRobbe/GaussianRandomFields.jl: A package for Gaussian random field generation in Julia](https://github.com/PieterjanRobbe/GaussianRandomFields.jl) 180 | 181 | [JuliaGaussianProcesses/Stheno.jl: Probabilistic Programming with Gaussian processes in Julia](https://github.com/JuliaGaussianProcesses/Stheno.jl) 182 | 183 | [STOR-i/GaussianProcesses.jl: A Julia package for Gaussian Processes](https://github.com/STOR-i/GaussianProcesses.jl) 184 | 185 | Python: 186 | 187 | [cornellius-gp/gpytorch: A highly efficient and modular implementation of Gaussian Processes in PyTorch](https://github.com/cornellius-gp/gpytorch) 188 | 189 | [GPflow/GPflow: Gaussian processes in TensorFlow](https://github.com/GPflow/GPflow) 190 | 191 | [SheffieldML/GPy: Gaussian processes framework in python](https://github.com/SheffieldML/GPy) 192 | 193 | ### 3.4.6. Bayesian Optimization 194 | 195 | Julia: 196 | 197 | [SciML/Surrogates.jl: Surrogate modeling and optimization for scientific machine learning (SciML)](https://github.com/SciML/Surrogates.jl) 198 | 199 | [jbrea/BayesianOptimization.jl: Bayesian optimization for Julia](https://github.com/jbrea/BayesianOptimization.jl) 200 | 201 | [baggepinnen/Hyperopt.jl: Hyperparameter optimization in Julia.](https://github.com/baggepinnen/Hyperopt.jl) 202 | 203 | Python: 204 | 205 | [fmfn/BayesianOptimization: A Python implementation of global optimization with gaussian processes.](https://github.com/fmfn/BayesianOptimization) 206 | 207 | [pytorch/botorch: Bayesian optimization in PyTorch](https://github.com/pytorch/botorch) 208 | 209 | [optuna/optuna: A hyperparameter optimization framework](https://github.com/optuna/optuna) 210 | 211 | [huawei-noah/HEBO: Bayesian optimisation library developped by Huawei Noah's Ark Library](https://github.com/huawei-noah/HEBO) 212 | 213 | ### 3.4.7. Information theory 214 | 215 | Julia: 216 | entropy and kldivengence for distributions or vectors can be seen in Distributions.jl 217 | 218 | KL divergence for functions[RafaelArutjunjan/InformationGeometry.jl: Methods for computational information geometry](https://github.com/RafaelArutjunjan/InformationGeometry.jl) 219 | 220 | not maintained[kzahedi/Shannon.jl: Entropy, Mutual Information, KL-Divergence related to Shannon's information theory and functions to binarize data](https://github.com/kzahedi/Shannon.jl) 221 | 222 | [gragusa/Divergences.jl: A Julia package for evaluation of divergences between distributions](https://github.com/gragusa/Divergences.jl) 223 | 224 | [Tchanders/InformationMeasures.jl: Entropy, mutual information and higher order measures from information theory, with various estimators and discretisation methods.](https://github.com/Tchanders/InformationMeasures.jl) 225 | 226 | [JuliaDynamics/TransferEntropy.jl: Transfer entropy (conditional mutual information) estimators for the Julia language](https://github.com/JuliaDynamics/TransferEntropy.jl) 227 | 228 | [cynddl/Discreet.jl: A Julia package to estimate discrete entropy and mutual information](https://github.com/cynddl/Discreet.jl) 229 | 230 | ### 3.4.8. Uncertainty 231 | 232 | Uncertainty propogation 233 | 234 | Julia: 235 | 236 | [JuliaPhysics/Measurements.jl: Error propagation calculator and library for physical measurements. It supports real and complex numbers with uncertainty, arbitrary precision calculations, operations with arrays, and numerical integration.](https://github.com/JuliaPhysics/Measurements.jl) 237 | 238 | [baggepinnen/MonteCarloMeasurements.jl: Propagation of distributions by Monte-Carlo sampling: Real number types with uncertainty represented by samples.](https://github.com/baggepinnen/MonteCarloMeasurements.jl) 239 | 240 | Review[Uncertainty Programming, Generalized Uncertainty Quantification](https://book.sciml.ai/notes/19/) 241 | 242 | [AnderGray/MomentArithmetic.jl: Rigorous moment propagation with partial information about moments and dependencies in Julia](https://github.com/AnderGray/MomentArithmetic.jl) 243 | 244 | [mschauer/Mitosis.jl: Automatic probabilistic programming for scientific machine learning and dynamical models](https://github.com/mschauer/Mitosis.jl) 245 | 246 | Good[JuliaReach/RangeEnclosures.jl: A Julia package to compute range enclosures of real-valued functions.](https://github.com/JuliaReach/RangeEnclosures.jl) 247 | 248 | Python 249 | 250 | [uncertainty-toolbox/uncertainty-toolbox: A python toolbox for predictive uncertainty quantification, calibration, metrics, and visualization](https://github.com/uncertainty-toolbox/uncertainty-toolbox) 251 | 252 | [pyro-ppl/funsor: Functional tensors for probabilistic programming](https://github.com/pyro-ppl/funsor) 253 | 254 | [lebigot/uncertainties: Transparent calculations with uncertainties on the quantities involved (aka "error propagation"); calculation of derivatives.](https://github.com/lebigot/uncertainties/) 255 | 256 | ### 3.4.9. Casual 257 | 258 | [zenna/Omega.jl: Causal, Higher-Order, Probabilistic Programming](https://github.com/zenna/Omega.jl) 259 | 260 | [mschauer/CausalInference.jl: Causal inference, graphical models and structure learning with the PC algorithm.](https://github.com/mschauer/CausalInference.jl) 261 | 262 | [JuliaDynamics/CausalityTools.jl: Algorithms for causal inference and the detection of dynamical coupling from time series, and for approximation of the transfer operator and invariant measures.](https://github.com/JuliaDynamics/CausalityTools.jl) 263 | 264 | python 265 | 266 | Review: [rguo12/awesome-causality-algorithms: An index of algorithms for learning causality with data](https://github.com/rguo12/awesome-causality-algorithms) 267 | 268 | ### 3.4.10. Sampling 269 | 270 | [MrUrq/LatinHypercubeSampling.jl: Julia package for the creation of optimised Latin Hypercube Sampling Plans](https://github.com/MrUrq/LatinHypercubeSampling.jl) 271 | 272 | [SciML/QuasiMonteCarlo.jl: Lightweight and easy generation of quasi-Monte Carlo sequences with a ton of different methods on one API for easy parameter exploration in scientific machine learning (SciML)](https://github.com/SciML/QuasiMonteCarlo.jl) 273 | 274 | ### 3.4.11 Message Passing 275 | 276 | Julia: 277 | 278 | [biaslab/ReactiveMP.jl: Julia package for automatic Bayesian inference on a factor graph with reactive message passing](https://github.com/biaslab/ReactiveMP.jl) 279 | 280 | [biaslab/ForneyLab.jl: Julia package for automatically generating Bayesian inference algorithms through message passing on Forney-style factor graphs.](https://github.com/biaslab/ForneyLab.jl) 281 | -------------------------------------------------------------------------------- /docs/AI4Science/machinelearning.md: -------------------------------------------------------------------------------- 1 | --- 2 | sidebar_position: 8 3 | --- 4 | 5 | ## Machine Learning and Deep Learning 6 | 7 | Python: 8 | 9 | Survey [ritchieng/the-incredible-pytorch at pythonrepo.com](https://github.com/ritchieng/the-incredible-pytorch?ref=pythonrepo.com#GANsVAEsandAEs) 10 | 11 | ### 3.5.1. Machine Learning 12 | 13 | Julia: MLJ is enough 14 | 15 | [alan-turing-institute/MLJ.jl: A Julia machine learning framework](https://github.com/alan-turing-institute/MLJ.jl) 16 | 17 | [JuliaML](https://github.com/JuliaML) 18 | 19 | [JuliaAI](https://github.com/JuliaAI) 20 | 21 | [Evovest/EvoTrees.jl: Boosted trees in Julia](https://github.com/Evovest/EvoTrees.jl) 22 | 23 | Dimention Reduction:[madeleineudell/LowRankModels.jl: LowRankModels.jl is a julia package for modeling and fitting generalized low rank models.](https://github.com/madeleineudell/LowRankModels.jl) 24 | 25 | [JuliaStats/MultivariateStats.jl: A Julia package for multivariate statistics and data analysis (e.g. dimension reduction)](https://github.com/JuliaStats/MultivariateStats.jl) 26 | 27 | Linear Regression[JuliaAI/MLJLinearModels.jl: Generalized Linear Regressions Models (penalized regressions, robust regressions, ...)](https://github.com/JuliaAI/MLJLinearModels.jl) 28 | 29 | [gerdm/pknn.jl: Probabilistic k-nearest neighbours](https://github.com/gerdm/pknn.jl) 30 | 31 | [IBM/AutoMLPipeline.jl: A package that makes it trivial to create and evaluate machine learning pipeline architectures.](https://github.com/IBM/AutoMLPipeline.jl) 32 | 33 | Python: 34 | 35 | [scikit-learn: machine learning in Python — scikit-learn 1.0.1 documentation](https://scikit-learn.org/stable/) 36 | 37 | [DistrictDataLabs/yellowbrick: Visual analysis and diagnostic tools to facilitate machine learning model selection.](https://github.com/DistrictDataLabs/yellowbrick) 38 | 39 | [automl/auto-sklearn: Automated Machine Learning with scikit-learn](https://github.com/automl/auto-sklearn) 40 | 41 | [h2oai/h2o-3: H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.](https://github.com/h2oai/h2o-3) 42 | 43 | [pycaret/pycaret: An open-source, low-code machine learning library in Python](https://github.com/pycaret/pycaret) 44 | 45 | [nubank/fklearn: fklearn: Functional Machine Learning](https://github.com/nubank/fklearn) 46 | 47 | [wecarsoniv/augmented-pca: Repository for the AugmentedPCA Python package.](https://github.com/wecarsoniv/augmented-pca) 48 | 49 | Data Generation 50 | 51 | [snorkel-team/snorkel: A system for quickly generating training data with weak supervision](https://github.com/snorkel-team/snorkel) 52 | 53 | [lk-geimfari/mimesis: Mimesis is a high-performance fake data generator for Python, which provides data for a variety of purposes in a variety of languages.](https://github.com/lk-geimfari/mimesis) 54 | 55 | ### Deep Learning 56 | 57 | Julia: Flux and Knet 58 | 59 | [FluxML/Flux.jl: Relax! Flux is the ML library that doesn't make you tensor](https://github.com/FluxML/Flux.jl) 60 | 61 | [sdobber/FluxArchitectures.jl: Complex neural network examples for Flux.jl](https://github.com/sdobber/FluxArchitectures.jl) 62 | 63 | [denizyuret/Knet.jl: Koç University deep learning framework.](https://github.com/denizyuret/Knet.jl) 64 | 65 | Python: Jax, Pytorch, Tensorflow 66 | 67 | Review[n2cholas/awesome-jax: JAX - A curated list of resources https://github.com/google/jax](https://github.com/n2cholas/awesome-jax) 68 | 69 | [google/jax: Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more](https://github.com/google/jax) 70 | 71 | [pytorch/pytorch: Tensors and Dynamic neural networks in Python with strong GPU acceleration](https://github.com/pytorch/pytorch) 72 | 73 | [tensorflow/tensorflow: An Open Source Machine Learning Framework for Everyone](https://github.com/tensorflow/tensorflow) 74 | 75 | [catalyst-team/catalyst: Accelerated deep learning R&D](https://github.com/catalyst-team/catalyst) 76 | 77 | [murufeng/awesome_lightweight_networks: MobileNetV1-V2，MobileNeXt，GhostNet，AdderNet，ShuffleNetV1-V2，Mobile+ViT etc. ⭐⭐⭐⭐⭐](https://github.com/murufeng/awesome_lightweight_networks) 78 | 79 | Review: [Chen-Cai-OSU/awesome-equivariant-network: Paper list for equivariant neural network](https://github.com/Chen-Cai-OSU/awesome-equivariant-network) 80 | 81 | ### 3.5.3. Reinforce Learning 82 | 83 | Julia: 84 | 85 | [JuliaPOMDP](https://github.com/JuliaPOMDP) 86 | 87 | [JuliaReinforcementLearning](https://github.com/JuliaReinforcementLearning) 88 | 89 | Python: 90 | 91 | [ray-project/ray: An open source framework that provides a simple, universal API for building distributed applications. Ray is packaged with RLlib, a scalable reinforcement learning library, and Tune, a scalable hyperparameter tuning library.](https://github.com/ray-project/ray) 92 | 93 | [tensorlayer/tensorlayer: Deep Learning and Reinforcement Learning Library for Scientists and Engineers 🔥](https://github.com/tensorlayer/tensorlayer) 94 | 95 | [pfnet/pfrl: PFRL: a PyTorch-based deep reinforcement learning library](https://github.com/pfnet/pfrl) 96 | 97 | [thu-ml/tianshou: An elegant PyTorch deep reinforcement learning library.](https://github.com/thu-ml/tianshou) 98 | 99 | ### 3.5.4. GNN 100 | 101 | Julia: 102 | 103 | [CarloLucibello/GraphNeuralNetworks.jl: Graph Neural Networks in Julia](https://github.com/CarloLucibello/GraphNeuralNetworks.jl) 104 | 105 | [FluxML/GeometricFlux.jl: Geometric Deep Learning for Flux](https://github.com/FluxML/GeometricFlux.jl) 106 | 107 | Python: 108 | 109 | [pyg-team/pytorch_geometric: Graph Neural Network Library for PyTorch](https://github.com/pyg-team/pytorch_geometric) 110 | 111 | [benedekrozemberczki/pytorch_geometric_temporal: PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models (CIKM 2021)](https://github.com/benedekrozemberczki/pytorch_geometric_temporal) 112 | 113 | [dmlc/dgl: Python package built to ease deep learning on graph, on top of existing DL frameworks.](https://github.com/dmlc/dgl) 114 | 115 | [THUDM/cogdl: CogDL: An Extensive Toolkit for Deep Learning on Graphs](https://github.com/THUDM/cogdl) 116 | 117 | ### 3.5.5. Transformer 118 | 119 | Julia: 120 | 121 | [chengchingwen/Transformers.jl: Julia Implementation of Transformer models](https://github.com/chengchingwen/Transformers.jl) 122 | 123 | Python: 124 | 125 | [huggingface/transformers: 🤗 Transformers: State-of-the-art Natural Language Processing for Pytorch, TensorFlow, and JAX.](https://github.com/huggingface/transformers) 126 | 127 | ### 3.5.6. Transfer Learning 128 | 129 | Survey[jindongwang/transferlearning: Transfer learning / domain adaptation / domain generalization / multi-task learning etc. papers, codes. datasets, applications, tutorials.-迁移学习](https://github.com/jindongwang/transferlearning) 130 | 131 | ### 3.5.7. Neural Tangent 132 | 133 | Python: 134 | 135 | [google/neural-tangents: Fast and Easy Infinite Neural Networks in Python](https://github.com/google/neural-tangents) 136 | 137 | ### 3.5.8. Visulization 138 | 139 | Python: 140 | 141 | [ashishpatel26/Tools-to-Design-or-Visualize-Architecture-of-Neural-Network: Tools to Design or Visualize Architecture of Neural Network](https://github.com/ashishpatel26/Tools-to-Design-or-Visualize-Architecture-of-Neural-Network) 142 | 143 | [julrog/nn_vis: A project for processing neural networks and rendering to gain insights on the architecture and parameters of a model through a decluttered representation.](https://github.com/julrog/nn_vis) 144 | 145 | PowerPoints[dair-ai/ml-visuals: 🎨 ML Visuals contains figures and templates which you can reuse and customize to improve your scientific writing.](https://github.com/dair-ai/ml-visuals) 146 | 147 | ### Semi-supervised Learning 148 | 149 | Python: 150 | 151 | [TorchSSL/TorchSSL: A PyTorch-based library for semi-supervised learning (NeurIPS'21)](https://github.com/TorchSSL/TorchSSL) 152 | -------------------------------------------------------------------------------- /docs/AI4Science/numericalanalysis.md: -------------------------------------------------------------------------------- 1 | --- 2 | sidebar_position: 7 3 | --- 4 | 5 | # Numerical Algebra 6 | 7 | [JuliaAlgebra](https://github.com/JuliaAlgebra) 8 | 9 | # SciML 10 | 11 | [SciML/SciMLDocs](https://github.com/SciML/SciMLDocs) 12 | 13 | Julia: 14 | 15 | All you need is the following organization (My Idol Prof. Christopher Rackauckas): 16 | 17 | [SciML Open Source Scientific Machine Learning](https://github.com/SciML) 18 | 19 | Including agent based models 20 | [JuliaDynamics](https://github.com/JuliaDynamics) 21 | 22 | [BioJulia](https://github.com/BioJulia) 23 | 24 | [nathanaelbosch/ProbNumDiffEq.jl: Probabilistic ODE Solvers via Bayesian Filtering and Smoothing](https://github.com/nathanaelbosch/ProbNumDiffEq.jl) 25 | 26 | [PerezHz/TaylorIntegration.jl: ODE integration using Taylor's method, and more, in Julia](https://github.com/PerezHz/TaylorIntegration.jl) 27 | 28 | [gideonsimpson/BasicMD.jl: A collection of basic routines for Molecular Dynamics simulations implemented in Julia](https://github.com/gideonsimpson/BasicMD.jl) 29 | 30 | Probablistic Numerical Methods: 31 | 32 | Julia: 33 | 34 | [nathanaelbosch/ProbNumDiffEq.jl: Probabilistic ODE Solvers via Bayesian Filtering and Smoothing](https://github.com/nathanaelbosch/ProbNumDiffEq.jl) 35 | 36 | Python: 37 | 38 | [ProbNum — probnum 0.1 documentation](http://www.probabilistic-numerics.org/en/latest/) 39 | 40 | C++: 41 | 42 | [odeint](http://headmyshoulder.github.io/odeint-v2/) 43 | 44 | [LLNL/sundials: Official development repository for SUNDIALS - a SUite of Nonlinear and DIfferential/ALgebraic equation Solvers. Pull requests are welcome for bug fixes and minor changes.](https://github.com/LLNL/sundials) 45 | 46 | ### 3.7.1. Partial differential equation 47 | 48 | [Partial Differential Equation (PDE) Solvers Overview · SciML](https://docs.sciml.ai/dev/highlevels/partial_differential_equation_solvers/) 49 | 50 | Survey[JuliaPDE/SurveyofPDEPackages: Survey of the packages of the Julia ecosystem for solving partial differential equations](https://github.com/JuliaPDE/SurveyofPDEPackages) 51 | 52 | [SciML/DiffEqOperators.jl: Linear operators for discretizations of differential equations and scientific machine learning (SciML)](https://github.com/SciML/DiffEqOperators.jl) 53 | 54 | [vavrines/Kinetic.jl: Universal modeling and simulation of fluid dynamics upon machine learning](https://github.com/vavrines/Kinetic.jl) 55 | 56 | [Gridap](https://github.com/gridap) 57 | 58 | [kailaix/AdFem.jl: Innovative, efficient, and computational-graph-based finite element simulator for inverse modeling](https://github.com/kailaix/AdFem.jl) 59 | 60 | [SciML/ExponentialUtilities.jl: Utility functions for exponential integrators for the SciML scientific machine learning ecosystem](https://github.com/SciML/ExponentialUtilities.jl) 61 | 62 | good[trixi-framework/Trixi.jl: Trixi.jl: Adaptive high-order numerical simulations of hyperbolic PDEs in Julia](https://github.com/trixi-framework/Trixi.jl) 63 | 64 | [JuliaIBM](https://github.com/JuliaIBPM) 65 | 66 | [ranocha/SummationByPartsOperators.jl: A Julia library of summation-by-parts (SBP) operators used in finite difference, Fourier pseudospectral, continuous Galerkin, and discontinuous Galerkin methods to get provably stable semidiscretizations, paying special attention to boundary conditions.](https://github.com/ranocha/SummationByPartsOperators.jl) 67 | 68 | [Ferrite-FEM/Ferrite.jl: Finite element toolbox for Julia](https://github.com/Ferrite-FEM/Ferrite.jl) 69 | 70 | [JuliaFEM](https://github.com/JuliaFEM) 71 | 72 | pseudospectral[FourierFlows/FourierFlows.jl: Tools for building fast, hackable, pseudospectral partial differential equation solvers on periodic domains](https://github.com/FourierFlows/FourierFlows.jl) 73 | 74 | Python： 75 | 76 | [DedalusProject/dedalus: A flexible framework for solving PDEs with modern spectral methods.](https://github.com/DedalusProject/dedalus) 77 | 78 | [FEniCS Project](https://github.com/FEniCS) 79 | 80 | #### Integral Differential Equation 81 | 82 | [TSGut/SparseVolterraExamples.jl: A number of examples built on the method described in https://arxiv.org/abs/2005.06081 for solving nonlinear and integro-differential Volterra equations](https://github.com/TSGut/SparseVolterraExamples.jl) 83 | 84 | [JoshKarpel/idesolver: A general-purpose numerical integro-differential equation solver](https://github.com/JoshKarpel/idesolver) 85 | 86 | [vitesempl/RK-IDE-Julia: Julia package for solving Differential Equations with Discrete and Distributed delays](https://github.com/vitesempl/RK-IDE-Julia) 87 | 88 | ### 3.7.2 Fractional Differential and Calculus 89 | 90 | Julia 91 | 92 | [SciFracX](https://github.com/SciFracX) 93 | 94 | [SciFracX/FractionalDiffEq.jl: FractionalDiffEq.jl: A Julia package aiming at solving Fractional Differential Equations using high performance numerical methods](https://github.com/SciFracX/FractionalDiffEq.jl) 95 | 96 | [SciFracX/FractionalSystems.jl: Fractional order modeling and analysis in Julia.](https://github.com/SciFracX/FractionalSystems.jl) 97 | 98 | [SciFracX/FractionalCalculus.jl: FractionalCalculus.jl: A Julia package for high performance, fast convergence and high precision numerical fractional calculus computing.](https://github.com/SciFracX/FractionalCalculus.jl) 99 | 100 | [SciFracX/FractionalTransforms.jl: FractionalTransforms.jl: A Julia package aiming at providing fractional order transforms with high performance.](https://github.com/SciFracX/FractionalTransforms.jl) 101 | 102 | [JuliaTurkuDataScience/FdeSolver.jl: FdeSolver.jl: A Julia package for the numerical solution of fractional differential equations (FDEs) as well as systems of equations.](https://github.com/JuliaTurkuDataScience/FdeSolver.jl) 103 | 104 | ## 3.10. Model Evaluation 105 | 106 | ### 3.10.1. Structure Idendification 107 | 108 | Julia: 109 | 110 | [SciML/StructuralIdentifiability.jl](https://github.com/SciML/StructuralIdentifiability.jl) 111 | 112 | [alexeyovchinnikov/SIAN-Julia: Implementation of SIAN in Julia](https://github.com/alexeyovchinnikov/SIAN-Julia) 113 | 114 | ### 3.10.2. Global Sensitivity Anylysis 115 | 116 | Julia: 117 | 118 | [lrennels/GlobalSensitivityAnalysis.jl: Julia implementations of global sensitivity analysis methods.](https://github.com/lrennels/GlobalSensitivityAnalysis.jl) 119 | 120 | [SciML/GlobalSensitivity.jl](https://github.com/SciML/GlobalSensitivity.jl) 121 | 122 | [SciML/DiffEqSensitivity.jl: A component of the DiffEq ecosystem for enabling sensitivity analysis for scientific machine learning (SciML). Optimize-then-discretize, discretize-then-optimize, and more for ODEs, SDEs, DDEs, DAEs, etc.](https://github.com/SciML/DiffEqSensitivity.jl) 123 | 124 | Python: 125 | 126 | [SALib/SALib: Sensitivity Analysis Library in Python. Contains Sobol, Morris, FAST, and other methods.](https://github.com/SALib/SALib) 127 | 128 | R: 129 | 130 | sensitivity 131 | 132 | fast 133 | 134 | sensobol 135 | -------------------------------------------------------------------------------- /docs/AI4Science/optcontrol.md: -------------------------------------------------------------------------------- 1 | --- 2 | sidebar_position: 7 3 | --- 4 | 5 | 6 | Some information are from [jkoendev/optimal-control-literature-software: List of literature and software for optimal control and numerical optimization.](https://github.com/jkoendev/optimal-control-literature-software) 7 | 8 | # List of software packages for optimal control 9 | 10 | ### Survey papers 11 | 12 | * F. Topputo and C. Zhang, “Survey of Direct Transcription for Low-Thrust Space Trajectory Optimization with Applications,” Abstract and Applied Analysis, vol. 2014, Article ID 851720, 15 pages, 2014. [[edited](https://www.hindawi.com/journals/aaa/2014/851720/) 13 | 14 | ## Software 15 | Nice[Hans D. Mittelmann](http://plato.asu.edu/) 16 | 17 | [mintOC](https://mintoc.de/index.php/Main_Page) 18 | 19 | (good) Python, Matlab, C[acados/acados: Fast and embedded solvers for nonlinear optimal control](https://github.com/acados/acados) 20 | 21 | (pass) AMPL with TACO extension (commercial) 22 | 23 | (pass) Forces (commercial) 24 | 25 | (pass) gPROMS (commercial) 26 | 27 | (pass) Mujoco, domain specific for robotics/contact, simulator (commercial) 28 | 29 | (pass) Optimica, Dymola (commercial) 30 | 31 | (pass) PROPT (commercial) 32 | 33 | ### Python 34 | 35 | (good)[BYU-PRISM/GEKKO: GEKKO Python for Machine Learning and Dynamic Optimization](https://github.com/BYU-PRISM/GEKKO) 36 | 37 | (good)[casadi/casadi: CasADi is a symbolic framework for numeric optimization implementing automatic differentiation in forward and reverse modes on sparse matrix-valued computational graphs. It supports self-contained C-code generation and interfaces state-of-the-art codes such as SUNDIALS, IPOPT etc. It can be used from C++, Python or Matlab/Octave.](https://github.com/casadi/casadi) 38 | 39 | [ethz-adrl/control-toolbox: The Control Toolbox - An Open-Source C++ Library for Robotics, Optimal and Model Predictive Control](https://github.com/ethz-adrl/control-toolbox) 40 | 41 | [Dynamic Optimization with pyomo.DAE — Pyomo 6.4.1 documentation](https://pyomo.readthedocs.io/en/stable/modeling_extensions/dae.html?highlight=optimal%20control) 42 | 43 | [OpenMDAO/dymos: Open Source Optimization of Dynamic Multidisciplinary Systems](https://github.com/OpenMDAO/dymos) 44 | 45 | [Shunichi09/PythonLinearNonlinearControl: PythonLinearNonLinearControl is a library implementing the linear and nonlinear control theories in python.](https://github.com/Shunichi09/PythonLinearNonlinearControl) 46 | 47 | [Examples — opty 1.2.0.dev0 documentation](https://opty.readthedocs.io/en/latest/examples.html) 48 | 49 | 50 | [wanxinjin/Pontryagin-Differentiable-Programming: A unified end-to-end learning and control framework that is able to learn a (neural) control objective function, dynamics equation, control policy, or/and optimal trajectory in a control system.](https://github.com/wanxinjin/Pontryagin-Differentiable-Programming) 51 | 52 | 53 | ### C++ 54 | Good[PSOPT/psopt: PSOPT Optimal Control Software](https://github.com/PSOPT/psopt) 55 | 56 | 57 | Very Good[Download – Bocop – The optimal control solver](https://www.bocop.org/download/) 58 | 59 | [loco-3d/crocoddyl: Crocoddyl is an optimal control library for robot control under contact sequence. Its solver is based on various efficient Differential Dynamic Programming (DDP)-like algorithms](https://github.com/loco-3d/crocoddyl) 60 | 61 | 62 | towr, domain specific for legged robots [[github](https://github.com/ethz-adrl/towr)] 63 | ### Julia 64 | [infiniteopt/InfiniteOpt.jl: An intuitive modeling interface for infinite-dimensional optimization problems.](https://github.com/infiniteopt/InfiniteOpt.jl) 65 | 66 | [RoboticExplorationLab/TrajectoryOptimization.jl: A fast trajectory optimization library written in Julia](https://github.com/RoboticExplorationLab/TrajectoryOptimization.jl) 67 | 68 | [FHoltorf/MarkovBounds.jl: A Julia package for the computation of hard, theoretically guaranteed bounds on the moments of jump-diffusion processes with polynomial data](https://github.com/FHoltorf/MarkovBounds.jl) 69 | 70 | [odow/SDDP.jl: Stochastic Dual Dynamic Programming in Julia](https://github.com/odow/SDDP.jl) 71 | 72 | [ai4energy/OptControl.jl: A tool to solve optimal control problem](https://github.com/ai4energy/OptControl.jl) 73 | 74 | [thowell/DirectTrajectoryOptimization.jl: A Julia package for constrained trajectory optimization using direct methods.](https://github.com/thowell/DirectTrajectoryOptimization.jl) 75 | 76 | [baggepinnen/DifferentialDynamicProgramming.jl: A package for solving Differential Dynamic Programming and trajectory optimization problems.](https://github.com/baggepinnen/DifferentialDynamicProgramming.jl) 77 | 78 | ### Matlab 79 | ICLOCS2 [[github](https://github.com/ImperialCollegeLondon/ICLOCS/)] [[web](http://www.ee.ic.ac.uk/ICLOCS/)] 80 | 81 | DIDO 82 | 83 | GPOCS2[Home | GPOPS-II - Next-Generation Optimal Control Software](https://www.gpops2.com/) 84 | 85 | [Everglow0214/The_Adaptive_Dynamic_Programming_Toolbox](https://github.com/Everglow0214/The_Adaptive_Dynamic_Programming_Toolbox) 86 | 87 | [nurkanovic/nosnoc: NOSNOC is an open source software package for NOnSmooth Numerical Optimal Control.](https://github.com/nurkanovic/nosnoc) 88 | 89 | [OpenOCL/OpenOCL: Open Optimal Control Library for Matlab. Trajectory Optimization and non-linear Model Predictive Control (MPC) toolbox.](https://github.com/OpenOCL/OpenOCL) 90 | 91 | [MatthewPeterKelly/OptimTraj: A trajectory optimization library for Matlab](https://github.com/MatthewPeterKelly/OptimTraj) 92 | 93 | [acado/acado: ACADO Toolkit is a software environment and algorithm collection for automatic control and dynamic optimization. It provides a general framework for using a great variety of algorithms for direct optimal control, including model predictive control, state and parameter estimation and robust optimization.](https://github.com/acado/acado) 94 | 95 | ## Automatic differentiation 96 | 97 | * CasADi [[github](https://github.com/casadi/casadi)] [[web](https://web.casadi.org/)] 98 | * CppAD [[github](https://github.com/coin-or/CppAD)] 99 | * CppADCodeGen [[github](https://github.com/joaoleal/CppADCodeGen)] 100 | * JuliaDiff [[github](https://github.com/JuliaDiff/)] [[web](http://www.juliadiff.org/)] 101 | 102 | 103 | 104 | 105 | 106 | ## 3.3. Optimal Control 107 | 108 | [eleurent/phd-bibliography: References on Optimal Control, Reinforcement Learning and Motion Planning](https://github.com/eleurent/phd-bibliography) 109 | 110 | [mintOC](https://mintoc.de/index.php/Main_Page) 111 | 112 | Julia: Jump + InfiniteOpt 113 | 114 | Jump is powerfull!!! 115 | 116 | [jump-dev/JuMP.jl: Modeling language for Mathematical Optimization (linear, mixed-integer, conic, semidefinite, nonlinear)](https://github.com/jump-dev/JuMP.jl) 117 | 118 | InfiniteOpt is powerfull!!! 119 | 120 | [pulsipher/InfiniteOpt.jl: An intuitive modeling interface for infinite-dimensional optimization problems.](https://github.com/pulsipher/InfiniteOpt.jl) 121 | 122 | GAMS unified software[GAMS Documentation Center](https://www.gams.com/latest/docs/index.html) 123 | 124 | [GAMS-dev/gams.jl: A MathOptInterface Optimizer to solve JuMP models using GAMS](https://github.com/GAMS-dev/gams.jl) 125 | 126 | Matlab: Yalmip unified[YALMIP](https://yalmip.github.io/) 127 | 128 | Python: unified[Pyomo/pyomo: An object-oriented algebraic modeling language in Python for structured optimization problems.](https://github.com/Pyomo/pyomo) 129 | 130 | [Solver Manuals](https://www.gams.com/latest/docs/S_MAIN.html) 131 | 132 | Julia: 133 | 134 | [martinbiel/StochasticPrograms.jl: Julia package for formulating and analyzing stochastic recourse models.](https://github.com/martinbiel/StochasticPrograms.jl) 135 | 136 | [odow/SDDP.jl: Stochastic Dual Dynamic Programming in Julia](https://github.com/odow/SDDP.jl) 137 | 138 | [PSORLab/EAGO.jl: A development environment for robust and global optimization](https://github.com/PSORLab/EAGO.jl) 139 | 140 | [JuliaSmoothOptimizers/PDENLPModels.jl: A NLPModel API for optimization problems with PDE-constraints](https://github.com/JuliaSmoothOptimizers/PDENLPModels.jl) 141 | 142 | [JuliaControl](https://github.com/JuliaControl) 143 | 144 | [JuliaMPC/NLOptControl.jl: nonlinear control optimization tool](https://github.com/JuliaMPC/NLOptControl.jl) 145 | 146 | Python: 147 | 148 | casadi is powerful! 149 | 150 | [python-control/python-control: The Python Control Systems Library is a Python module that implements basic operations for analysis and design of feedback control systems.](https://github.com/python-control/python-control) 151 | 152 | [Shunichi09/PythonLinearNonlinearControl: PythonLinearNonLinearControl is a library implementing the linear and nonlinear control theories in python.](https://github.com/Shunichi09/PythonLinearNonlinearControl) 153 | 154 | Matlab: 155 | 156 | [OpenOCL/OpenOCL: Open Optimal Control Library for Matlab. Trajectory Optimization and non-linear Model Predictive Control (MPC) toolbox.](https://github.com/OpenOCL/OpenOCL) 157 | 158 | [jkoendev/optimal-control-literature-software: List of literature and software for optimal control and numerical optimization.](https://github.com/jkoendev/optimal-control-literature-software) 159 | -------------------------------------------------------------------------------- /docs/AI4Science/optimization.md: -------------------------------------------------------------------------------- 1 | --- 2 | sidebar_position: 5 3 | --- 4 | 5 | ## Optimization 6 | 7 | An "learn one equals learn all" Julia Package 8 | 9 | [SciML/GalacticOptim.jl: Local, global, and beyond optimization for scientific machine learning (SciML)](https://github.com/SciML/GalacticOptim.jl) 10 | 11 | Opt Organization: 12 | 13 | [JuliaOpt](https://github.com/JuliaOpt) 14 | 15 | [JuliaNLSolvers](https://github.com/JuliaNLSolvers) 16 | 17 | [Process Systems and Operations Research Laboratory](https://github.com/PSORLab) 18 | 19 | [JuliaNLSolvers/Optim.jl: Optimization functions for Julia](https://github.com/JuliaNLSolvers/Optim.jl) 20 | 21 | [JuliaOpt/NLopt.jl: Package to call the NLopt nonlinear-optimization library from the Julia language](https://github.com/JuliaOpt/NLopt.jl) 22 | 23 | [robertfeldt/BlackBoxOptim.jl: Black-box optimization for Julia](https://github.com/robertfeldt/BlackBoxOptim.jl) 24 | 25 | [jump-dev/MathOptInterface.jl: An abstraction layer for mathematical optimization solvers.](https://github.com/jump-dev/MathOptInterface.jl) 26 | 27 | [osqp/OSQP.jl: Julia interface for OSQP: The Operator Splitting QP Solver](https://github.com/osqp/OSQP.jl) 28 | 29 | [PSR](https://github.com/psrenergy) 30 | 31 | [tpapp/MultistartOptimization.jl: Multistart optimization methods in Julia.](https://github.com/tpapp/MultistartOptimization.jl) 32 | 33 | [bbopt/NOMAD.jl: Julia interface to the NOMAD blackbox optimization software](https://github.com/bbopt/NOMAD.jl) 34 | 35 | [JuliaFirstOrder](https://github.com/JuliaFirstOrder) 36 | 37 | [NicolasL-S/SpeedMapping.jl: General fixed point mapping acceleration and optimization in Julia](https://github.com/NicolasL-S/SpeedMapping.jl) 38 | 39 | [JuliaManifolds/Manopt.jl: Optimization on Manifolds in Julia](https://github.com/JuliaManifolds/Manopt.jl) 40 | 41 | MPEC: [chkwon/Complementarity.jl: provides a modeling interface for mixed complementarity problems (MCP) and math programs with equilibrium problems (MPEC) via JuMP](https://github.com/chkwon/Complementarity.jl) 42 | 43 | Open Optimizers[Download – COIN-OR: Computational Infrastructure for Operations Research](file:///F:/Zotero/Zotero/storage/RJ96NPGG/downloading.html) 44 | 45 | ### 3.2.1. Metaheuristic 46 | 47 | Julia: 48 | 49 | [jmejia8/Metaheuristics.jl: High performance metaheuristics for optimization purely coded in Julia.](https://github.com/jmejia8/Metaheuristics.jl) 50 | 51 | [ac-tuwien/MHLib.jl: MHLib.jl - A Toolbox for Metaheuristics and Hybrid Optimization Methods in Julia](https://github.com/ac-tuwien/MHLib.jl) 52 | 53 | Python: 54 | 55 | [guofei9987/scikit-opt: Genetic Algorithm, Particle Swarm Optimization, Simulated Annealing, Ant Colony Optimization Algorithm,Immune Algorithm, Artificial Fish Swarm Algorithm, Differential Evolution and TSP(Traveling salesman)](https://github.com/guofei9987/scikit-opt) 56 | 57 | [scikit-optimize/scikit-optimize: Sequential model-based optimization with a `scipy.optimize` interface](https://github.com/scikit-optimize/scikit-optimize) 58 | 59 | [ac-tuwien/pymhlib: pymhlib - A Toolbox for Metaheuristics and Hybrid Optimization Methods](https://github.com/ac-tuwien/pymhlib) 60 | 61 | [cvxpy/cvxpy: A Python-embedded modeling language for convex optimization problems.](https://github.com/cvxpy/cvxpy) 62 | 63 | [coin-or/pulp: A python Linear Programming API](https://github.com/coin-or/pulp) 64 | 65 | ### 3.2.2. Evolution Stragegy 66 | 67 | Julia: 68 | 69 | [wildart/Evolutionary.jl: Evolutionary & genetic algorithms for Julia](https://github.com/wildart/Evolutionary.jl) 70 | 71 | [d9w/Cambrian.jl: An Evolutionary Computation framework](https://github.com/d9w/Cambrian.jl) 72 | 73 | [jbrea/CMAEvolutionStrategy.jl](https://github.com/jbrea/CMAEvolutionStrategy.jl) 74 | 75 | [AStupidBear/GCMAES.jl: Gradient-based Covariance Matrix Adaptation Evolutionary Strategy for Real Blackbox Optimization](https://github.com/AStupidBear/GCMAES.jl) 76 | 77 | [itsdfish/DifferentialEvolutionMCMC.jl: A Julia package for Differential Evolution MCMC](https://github.com/itsdfish/DifferentialEvolutionMCMC.jl) 78 | 79 | ### 3.2.3. Genetic Algorithms 80 | 81 | Julia: 82 | 83 | [d9w/CartesianGeneticProgramming.jl: Cartesian Genetic Programming for Julia](https://github.com/d9w/CartesianGeneticProgramming.jl) 84 | 85 | [WestleyArgentum/GeneticAlgorithms.jl: A lightweight framework for writing genetic algorithms in Julia](https://github.com/WestleyArgentum/GeneticAlgorithms.jl) 86 | 87 | Python: 88 | 89 | [trevorstephens/gplearn: Genetic Programming in Python, with a scikit-learn inspired API](https://github.com/trevorstephens/gplearn) 90 | 91 | ### 3.2.4. Nonconvex 92 | 93 | Julia: 94 | 95 | [JuliaNonconvex/Nonconvex.jl: Toolbox for non-convex constrained optimization.](https://github.com/JuliaNonconvex/Nonconvex.jl) 96 | 97 | ### 3.2.5. First Order Methods 98 | 99 | Proximal 100 | [OPTEC](https://github.com/kul-optec) 101 | 102 | [kul-optec/CIAOAlgorithms.jl: Coordinate and Incremental Aggregated Optimization Algorithms](https://github.com/kul-optec/CIAOAlgorithms.jl) 103 | 104 | ### 3.2.6. Second Order Methods 105 | 106 | [Search · stochastic quasi-newton](https://github.com/search?o=desc&q=stochastic+quasi-newton&s=updated&type=Repositories) 107 | 108 | Good[hiroyuki-kasai/SGDLibrary: MATLAB/Octave library for stochastic optimization algorithms: Version 1.0.20](https://github.com/hiroyuki-kasai/SGDLibrary) 109 | 110 | [gowerrobert/StochOpt.jl: A suite of stochastic optimization methods for solving the empirical risk minimization problem.](https://github.com/gowerrobert/StochOpt.jl) 111 | 112 | [pcmoritz/slbfgs: Stochastic LBFGS](https://github.com/pcmoritz/slbfgs) 113 | -------------------------------------------------------------------------------- /docs/AI4Science/probablisticmachinelearning.md: -------------------------------------------------------------------------------- 1 | --- 2 | sidebar_position: 9 3 | --- 4 | 5 | ## 3.6. Probablistic Machine Learning and Deep Learning 6 | 7 | Julia: 8 | 9 | [mcosovic/FactorGraph.jl: The FactorGraph package provides the set of different functions to perform inference over the factor graph with continuous or discrete random variables using the belief propagation algorithm.](https://github.com/mcosovic/FactorGraph.jl) 10 | 11 | [stefan-m-lenz/BoltzmannMachines.jl: A Julia package for training and evaluating multimodal deep Boltzmann machines](https://github.com/stefan-m-lenz/BoltzmannMachines.jl) 12 | 13 | [BIASlab](https://github.com/biaslab) 14 | 15 | [biaslab/ReactiveMP.jl: Julia package for automatic Bayesian inference on a factor graph with reactive message passing](https://github.com/biaslab/ReactiveMP.jl) 16 | 17 | [mroavi/JunctionTrees.jl: A metaprogramming-based implementation of the junction tree algorithm.](https://github.com/mroavi/JunctionTrees.jl) 18 | 19 | [pat-alt/LaplaceRedux.jl: Small library for using Laplace Redux with Flux Neural Networks for effortless Bayesian Deep Learning.](https://github.com/pat-alt/LaplaceRedux.jl) 20 | 21 | Python: 22 | 23 | [Probabilistic machine learning](https://github.com/probml) 24 | 25 | [thu-ml/zhusuan: A probabilistic programming library for Bayesian deep learning, generative models, based on Tensorflow](https://github.com/thu-ml/zhusuan) 26 | 27 | [OATML/bdl-benchmarks: Bayesian Deep Learning Benchmarks](https://github.com/OATML/bdl-benchmarks) 28 | 29 | [pgmpy/pgmpy: Python Library for learning (Structure and Parameter) and inference (Probabilistic and Causal) in Bayesian Networks.](https://github.com/pgmpy/pgmpy) 30 | 31 | [scikit-learn-contrib/imbalanced-learn: A Python Package to Tackle the Curse of Imbalanced Datasets in Machine Learning](https://github.com/scikit-learn-contrib/imbalanced-learn) 32 | 33 | ### 3.6.1. GAN 34 | 35 | Julia: 36 | 37 | Python: 38 | 39 | [torchgan/torchgan: Research Framework for easy and efficient training of GANs based on Pytorch](file:///F:/Zotero/Zotero/storage/DMJ4DGLN/torchgan.html) 40 | 41 | [kwotsin/mimicry: [CVPR 2020 Workshop] A PyTorch GAN library that reproduces research results for popular GANs.](https://github.com/kwotsin/mimicry) 42 | 43 | ### 3.6.2. Normilization Flows 44 | 45 | Julia: 46 | 47 | [TuringLang/Bijectors.jl: Implementation of normalising flows and constrained random variable transformations](https://github.com/TuringLang/Bijectors.jl) 48 | 49 | [slimgroup/InvertibleNetworks.jl: A Julia framework for invertible neural networks](https://github.com/slimgroup/InvertibleNetworks.jl) 50 | 51 | FFJord is impleted in DiffEqFlux.jl 52 | 53 | Python: 54 | 55 | Survey[janosh/awesome-normalizing-flows: A list of awesome resources on normalizing flows.](https://github.com/janosh/awesome-normalizing-flows) 56 | 57 | [RameenAbdal/StyleFlow: StyleFlow: Attribute-conditioned Exploration of StyleGAN-generated Images using Conditional Continuous Normalizing Flows (ACM TOG 2021)](https://github.com/RameenAbdal/StyleFlow) 58 | 59 | ### 3.6.3. VAE 60 | 61 | Julia: 62 | 63 | Python: 64 | 65 | [Variational Autoencoders — Pyro Tutorials 1.7.0 documentation](https://pyro.ai/examples/vae.html) 66 | 67 | [AntixK/PyTorch-VAE: A Collection of Variational Autoencoders (VAE) in PyTorch.](https://github.com/AntixK/PyTorch-VAE) 68 | 69 | [timsainb/tensorflow2-generative-models: Implementations of a number of generative models in Tensorflow 2. GAN, VAE, Seq2Seq, VAEGAN, GAIA, Spectrogram Inversion. Everything is self contained in a jupyter notebook for easy export to colab.](https://github.com/timsainb/tensorflow2-generative-models) 70 | 71 | [altosaar/variational-autoencoder: Variational autoencoder implemented in tensorflow and pytorch (including inverse autoregressive flow)](https://github.com/altosaar/variational-autoencoder) 72 | 73 | [subinium/Pytorch-AutoEncoders at pythonrepo.com](https://github.com/subinium/Pytorch-AutoEncoders?ref=pythonrepo.com) 74 | 75 | [Ritvik19/pyradox-generative at pythonrepo.com](https://github.com/Ritvik19/pyradox-generative?ref=pythonrepo.com) 76 | 77 | ### 3.6.4 BNN 78 | 79 | [JavierAntoran/Bayesian-Neural-Networks: Pytorch implementations of Bayes By Backprop, MC Dropout, SGLD, the Local Reparametrization Trick, KF-Laplace, SG-HMC and more](https://github.com/JavierAntoran/Bayesian-Neural-Networks) 80 | 81 | [RajDandekar/MSML21_BayesianNODE](https://github.com/RajDandekar/MSML21_BayesianNODE) 82 | 83 | [bayesian-neural-networks · GitHub Topics](https://github.com/topics/bayesian-neural-networks) 84 | 85 | ### 3.6.5 Diffusion-Models 86 | 87 | [heejkoo/Awesome-Diffusion-Models: A collection of resources and papers on Diffusion Models and Score-based Models, a darkhorse in the field of Generative Models](https://github.com/heejkoo/Awesome-Diffusion-Models) 88 | 89 | 90 | 91 | ## 3.11. Optimal Transportation 92 | 93 | Julia: 94 | 95 | [Optimal transport in Julia](https://github.com/JuliaOptimalTransport) 96 | 97 | [JuliaOptimalTransport/OptimalTransport.jl: Optimal transport algorithms for Julia](https://github.com/JuliaOptimalTransport/OptimalTransport.jl) 98 | 99 | [JuliaOptimalTransport/ExactOptimalTransport.jl: Solving unregularized optimal transport problems with Julia](https://github.com/JuliaOptimalTransport/ExactOptimalTransport.jl) 100 | 101 | Python: 102 | 103 | [PythonOT/POT: POT : Python Optimal Transport](https://github.com/PythonOT/POT) 104 | 105 | [ott-jax/ott](https://github.com/ott-jax/ott) 106 | -------------------------------------------------------------------------------- /docs/AI4Science/tensorcomputation.md: -------------------------------------------------------------------------------- 1 | --- 2 | sidebar_position: 1 3 | --- 4 | # Descriptions 5 | 6 | - Tensor and matrix computation 7 | - Linear Solvers 8 | - Nonlinear Solvers 9 | - Matrix Equations 10 | - HPC 11 | 12 | 13 | # MKL Labpack 14 | 15 | # PETSc and SLEPc 16 | 17 | [PETSc 3.17 — PETSc 3.17.3 documentation](https://petsc.org/release/) 18 | 19 | Scalable Library for Eigenvalue Problem Computations[SLEPc / slepc · GitLab](https://gitlab.com/slepc/slepc) 20 | 21 | # suitesparse 22 | [suitesparse : a suite of sparse matrix software](https://people.engr.tamu.edu/davis/suitesparse.html) 23 | 24 | # Pardiso and MKLPardiso 25 | 26 | 27 | # HSL 28 | [HSL Mathematical Software Library](https://www.hsl.rl.ac.uk/) 29 | 30 | 31 | # MUMPS 32 | [MUMPS : a parallel sparse direct solver](http://mumps.enseeiht.fr/) 33 | 34 | # cuSolvers 35 | [cuSOLVER :: CUDA Toolkit Documentation](https://docs.nvidia.com/cuda/cusolver/index.html) 36 | 37 | 38 | ### 3.1.3. Matrix and Tensor computation 39 | 40 | Matrix organization 41 | 42 | [JuliaArrays](https://github.com/JuliaArrays) 43 | 44 | - [JuliaArrays/StaticArrays.jl: Statically sized arrays for Julia](https://github.com/JuliaArrays/StaticArrays.jl) 45 | 46 | - [JuliaArrays/ArrayInterface.jl: Designs for new Base array interface primitives, used widely through scientific machine learning (SciML) and other organizations](https://github.com/JuliaArrays/ArrayInterface.jl) 47 | 48 | - [JuliaArrays/StructArrays.jl: Efficient implementation of struct arrays in Julia](https://github.com/JuliaArrays/StructArrays.jl) 49 | 50 | - [JuliaArrays/LazyArrays.jl: Lazy arrays and linear algebra in Julia](https://github.com/JuliaArrays/LazyArrays.jl) 51 | - [JuliaArrays/AxisArrays.jl: Performant arrays where each dimension can have a named axis with values](https://github.com/JuliaArrays/AxisArrays.jl) 52 | - [JuliaArrays/OffsetArrays.jl: Fortran-like arrays with arbitrary, zero or negative starting indices.](https://github.com/JuliaArrays/OffsetArrays.jl) 53 | - [JuliaArrays/BlockArrays.jl: BlockArrays for Julia](https://github.com/JuliaArrays/BlockArrays.jl) 54 | - [JuliaArrays/ArraysOfArrays.jl: Efficient storage and handling of nested arrays in Julia](https://github.com/JuliaArrays/ArraysOfArrays.jl) 55 | - [JuliaArrays/InfiniteArrays.jl: A Julia package for representing infinite-dimensional arrays](https://github.com/JuliaArrays/InfiniteArrays.jl) 56 | - [JuliaArrays/FillArrays.jl: Julia package for lazily representing matrices filled with a single entry](https://github.com/JuliaArrays/FillArrays.jl) 57 | 58 | [JuliaMatrices](https://github.com/JuliaMatrices) 59 | 60 | - [JuliaMatrices/BandedMatrices.jl: A Julia package for representing banded matrices](https://github.com/JuliaMatrices/BandedMatrices.jl) 61 | 62 | - [JuliaMatrices/BlockBandedMatrices.jl: A Julia package for representing block-banded matrices and banded-block-banded matrices](https://github.com/JuliaMatrices/BlockBandedMatrices.jl) 63 | - [JuliaMatrices/SpecialMatrices.jl: Julia package for working with special matrix types.](https://github.com/JuliaMatrices/SpecialMatrices.jl) 64 | - [JuliaMatrices/InfiniteLinearAlgebra.jl: A Julia repository for linear algebra with infinite matrices](https://github.com/JuliaMatrices/InfiniteLinearAlgebra.jl) 65 | 66 | [RalphAS](https://github.com/RalphAS) 67 | 68 | Good[JuliaLinearAlgebra](https://github.com/JuliaLinearAlgebra) 69 | 70 | [JuliaSparse](https://github.com/JuliaSparse) 71 | 72 | [JuliaLang/SparseArrays.jl: SparseArrays.jl is a Julia stdlib](https://github.com/JuliaLang/SparseArrays.jl) 73 | 74 | [SciML/LabelledArrays.jl: Arrays which also have a label for each element for easy scientific machine learning (SciML)](https://github.com/SciML/LabelledArrays.jl) 75 | 76 | [SciML/RecursiveArrayTools.jl: Tools for easily handling objects like arrays of arrays and deeper nestings in scientific machine learning (SciML) and other applications](https://github.com/SciML/RecursiveArrayTools.jl) 77 | 78 | Python: 79 | 80 | numpy 81 | 82 | numba 83 | 84 | [scikit-hep/awkward-1.0: Manipulate JSON-like data with NumPy-like idioms.](https://github.com/scikit-hep/awkward-1.0) 85 | 86 | #### Special Matrix and Arrays 87 | 88 | [JuliaMatrices/SpecialMatrices.jl: Julia package for working with special matrix types.](https://github.com/JuliaMatrices/SpecialMatrices.jl) 89 | 90 | [SciML/LabelledArrays.jl: Arrays which also have a label for each element for easy scientific machine learning (SciML)](https://github.com/SciML/LabelledArrays.jl) 91 | 92 | #### Computation 93 | 94 | BLAS and LAPACK[JuliaLinearAlgebra/MKL.jl: Intel MKL linear algebra backend for Julia](https://github.com/JuliaLinearAlgebra/MKL.jl) 95 | 96 | [mcabbott/Tullio.jl: ⅀](https://github.com/mcabbott/Tullio.jl) 97 | 98 | [JuliaLinearAlgebra/Octavian.jl: Multi-threaded BLAS-like library that provides pure Julia matrix multiplication](https://github.com/JuliaLinearAlgebra/Octavian.jl) 99 | 100 | [JuliaGPU/GemmKernels.jl: Flexible and performant GEMM kernels in Julia](https://github.com/JuliaGPU/GemmKernels.jl) 101 | 102 | [MasonProtter/Gaius.jl: Divide and Conquer Linear Algebra](https://github.com/MasonProtter/Gaius.jl) 103 | 104 | #### Eigenvalues and Solvers 105 | 106 | Eig[nep-pack/NonlinearEigenproblems.jl: Nonlinear eigenvalue problems in Julia: Iterative methods and benchmarks](https://github.com/nep-pack/NonlinearEigenproblems.jl) 107 | 108 | Solver[SciML/LinearSolve.jl: LinearSolve.jl: High-Performance Unified Linear Solvers](https://github.com/SciML/LinearSolve.jl) 109 | 110 | Julia: 111 | 112 | Eig: 113 | [JuliaLinearAlgebra/Arpack.jl: Julia Wrappers for the arpack-ng Fortran library](https://github.com/JuliaLinearAlgebra/Arpack.jl) 114 | 115 | [dgleich/GenericArpack.jl: A pure Julia translation of the Arpack library for eigenvalues and eigenvectors but for any numeric types. (Symmetric only right now)](https://github.com/dgleich/GenericArpack.jl) 116 | 117 | [JuliaLinearAlgebra/ArnoldiMethod.jl: Implicitly Restarted Arnoldi Method, natively in Julia](https://github.com/JuliaLinearAlgebra/ArnoldiMethod.jl) 118 | 119 | [Jutho/KrylovKit.jl: Krylov methods for linear problems, eigenvalues, singular values and matrix functions](https://github.com/Jutho/KrylovKit.jl) 120 | 121 | [pablosanjose/QuadEig.jl: Julia implementation of the `quadeig` algorithm for the solution of quadratic matrix pencils](https://github.com/pablosanjose/QuadEig.jl) 122 | 123 | [JuliaApproximation/SpectralMeasures.jl: Julia package for finding the spectral measure of structured self adjoint operators](https://github.com/JuliaApproximation/SpectralMeasures.jl) 124 | 125 | [dgleich/GenericArpack.jl: A pure Julia translation of the Arpack library for eigenvalues and eigenvectors but for any numeric types. (Symmetric only right now)](https://github.com/dgleich/GenericArpack.jl) 126 | 127 | Solver: 128 | 129 | [JuliaInv/KrylovMethods.jl: Simple and fast Julia implementation of Krylov subspace methods for linear systems.](https://github.com/JuliaInv/KrylovMethods.jl) 130 | 131 | [JuliaSmoothOptimizers/Krylov.jl: A Julia Basket of Hand-Picked Krylov Methods](https://github.com/JuliaSmoothOptimizers/Krylov.jl) 132 | 133 | Eig Too[JuliaLinearAlgebra/IterativeSolvers.jl: Iterative algorithms for solving linear systems, eigensystems, and singular value problems](https://github.com/JuliaLinearAlgebra/IterativeSolvers.jl) 134 | 135 | [tjdiamandis/RandomizedPreconditioners.jl](https://github.com/tjdiamandis/RandomizedPreconditioners.jl) 136 | 137 | [JuliaLinearAlgebra/RecursiveFactorization.jl](https://github.com/JuliaLinearAlgebra/RecursiveFactorization.jl) 138 | 139 | Spectral methods 140 | 141 | [JuliaApproximation/SpectralMeasures.jl: Julia package for finding the spectral measure of structured self adjoint operators](https://github.com/JuliaApproximation/SpectralMeasures.jl) 142 | 143 | [tpapp/SpectralKit.jl: Building blocks of spectral methods for Julia.](https://github.com/tpapp/SpectralKit.jl) 144 | 145 | [markmbaum/BasicInterpolators.jl: Basic (+chebyshev) interpolation recipes in Julia](https://github.com/markmbaum/BasicInterpolators.jl) 146 | 147 | Spasrse Slover 148 | 149 | Sparse[JuliaSparse/Pardiso.jl: Calling the PARDISO library from Julia](https://github.com/JuliaSparse/Pardiso.jl) 150 | 151 | Sparse[JuliaSparse/MKLSparse.jl: Make available to Julia the sparse functionality in MKL](https://github.com/JuliaSparse/MKLSparse.jl) 152 | 153 | Sparse[JuliaLang/SuiteSparse.jl: Development of SuiteSparse.jl, which ships as part of the Julia standard library.](https://github.com/JuliaLang/SuiteSparse.jl) 154 | 155 | Python: 156 | 157 | [scipy.sparse.linalg.eigs — SciPy v1.7.1 Manual](https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.linalg.eigs.html?highlight=scipy%20sparse%20linalg%20eigs#scipy.sparse.linalg.eigs) 158 | 159 | #### Maps and Operators 160 | 161 | [Jutho/LinearMaps.jl: A Julia package for defining and working with linear maps, also known as linear transformations or linear operators acting on vectors. The only requirement for a LinearMap is that it can act on a vector (by multiplication) efficiently.](https://github.com/Jutho/LinearMaps.jl) 162 | 163 | [emmt/LazyAlgebra.jl: A Julia package to extend the notion of vectors and matrices](https://github.com/emmt/LazyAlgebra.jl) 164 | 165 | [JuliaSmoothOptimizers/LinearOperators.jl: Linear Operators for Julia](https://github.com/JuliaSmoothOptimizers/LinearOperators.jl) 166 | 167 | [kul-optec/AbstractOperators.jl: Abstract operators for large scale optimization in Julia](https://github.com/kul-optec/AbstractOperators.jl) 168 | 169 | [matthieugomez/InfinitesimalGenerators.jl: A set of tools to work with Markov Processes](https://github.com/matthieugomez/InfinitesimalGenerators.jl) 170 | 171 | [ranocha/SummationByPartsOperators.jl: A Julia library of summation-by-parts (SBP) operators used in finite difference, Fourier pseudospectral, continuous Galerkin, and discontinuous Galerkin methods to get provably stable semidiscretizations, paying special attention to boundary conditions.](https://github.com/ranocha/SummationByPartsOperators.jl) 172 | 173 | [hakkelt/FunctionOperators.jl: Julia package that allows writing code close to mathematical notation memory-efficiently.](https://github.com/hakkelt/FunctionOperators.jl) 174 | 175 | [JuliaApproximation/ApproxFun.jl: Julia package for function approximation](https://github.com/JuliaApproximation/ApproxFun.jl) 176 | 177 | #### Matrxi Equations 178 | 179 | [andreasvarga/MatrixEquations.jl: Solution of Lyapunov, Sylvester and Riccati matrix equations using Julia](https://github.com/andreasvarga/MatrixEquations.jl) 180 | 181 | #### Kronecker-based algebra 182 | 183 | [MichielStock/Kronecker.jl: A general-purpose toolbox for efficient Kronecker-based algebra.](https://github.com/MichielStock/Kronecker.jl) 184 | -------------------------------------------------------------------------------- /docs/DataScience/R分享｜100 个统计学和 R 语言学习资源网站.md: -------------------------------------------------------------------------------- 1 | # R分享｜100 个统计学和 R 语言学习资源网站 2 | 3 | > 原文：统计学 & R学习资源 4 | 5 | 点击下方公众号，回复资料分享，收获惊喜 6 | ------------------- 7 | 8 | 简介 9 | 10 | ----- 11 | 12 | 原文：统计学 & R学习资源 13 | 14 | 作者：CoffeeCat\[1\] 15 | 16 | 转载于：Coffee学生物统计的地方\[2\] 17 | 18 | > 注：有些链接需要**科学上网/较硬的英文阅读能力**才能愉快地体验知识/技术带来的快感。如果公众号阅读体验不佳，可以在**文末原文链接**跳转。 19 | 20 | 1.个人主页、博客、社区、论坛 21 | --------------- 22 | 23 | 北大李东风\[3\] 24 | 25 | 中科大张伟平\[4\] 26 | 27 | 谢益辉(人称谢大大)\[5\]：统计之都论坛\[6\]创始人(与之有关的统计之都\[7\]) 28 | 29 | 统计学资源链接大全\[8\]：知名 **统计系**、**统计学会**、**统计组织**、**统计软件**、**统计期刊**的官网（该老师的主页\[9\]） 30 | 31 | 斯坦福大学统计系：Trevor Hastie\[10\]、Jerome H. Friedman\[11\]、Rob Tibshirani\[12\] 32 | 33 | 顾凯\[13\]：统计分析师；R、SAS、医学统计博主 34 | 35 | revolutionanalytics\[14\]：一个R社区(Revolution Analytics开发了Revolution R，后来被微软收购) 36 | 37 | r-bloggers\[15\]：R博客 38 | 39 | Statistics How To\[16\]：统计学与SPSS, Minitab, Excel 40 | 41 | Statistical Modeling, Causal Inference, and Social Science\[17\]：哥大统计“统计建模，因果推论和社会科学” 42 | 43 | Error Statistics Philosophy\[18\]：统计哲学家Deborah G. Mayo 44 | 45 | Simply Statistics\[19\]：三位生物统计专家的Jeff Leek\[20\], Roger Peng\[21\], Rafa Irizarry\[22\]的博客 46 | 47 | FLOWINGDATA\[23\]：分析、数据可视化(付费) 48 | 49 | Statistics by Jim\[24\]：使统计更直观 50 | 51 | 2.电子书、课程 52 | -------- 53 | 54 | Library Genesis\[25\]：外文电子书大全。结合亚马逊\[26\]、Routledge\[27\](Chapman \\& Hall/CRC Texts in Statistical Science\[28\]、Chapman \\& Hall/CRC Biostatistics Series\[29\])、Springer\[30\](Springer Statistics\[31\])、Elsevier\[32\]、Oxford University Press\[33\](Probability \\& Statistics\[34\])、Cambridge University Press\[35\](Statistics and probability\[36\])……几乎可以找到你想要的一切。 55 | 56 | 电子书From Bookdown\[37\]: 57 | ----------------------- 58 | 59 | **链接网页上方许多按钮是可以按的，请自行探索** 60 | 61 | 数据科学中的R语言\[38\]：非常全面的R教程 62 | 63 | R语言忍者秘籍\[39\]：谢大大的R教程 64 | 65 | 现代统计图形\[40\]：谢大大R可视化的佳作 66 | 67 | Statistics Handbook\[41\]：R语言统计分析小册子（有类似的中文的：薛毅老师的《统计建模与Ｒ软件》） 68 | 69 | R for Data Science\[42\]：COPSS奖得主、RStudio首席科学家Hadley Wickham\[43\]的倾力之作，学习tidyverse\[44\]重要语法的不二之选 70 | 71 | Advanced R\[45\]：Hadley Wickham\[46\]的提高R语言编程技能(本书的习题解答\[47\]) 72 | 73 | R Graphics Cookbook\[48\]：R基础绘图圣经 74 | 75 | Data Visualization with R\[49\]：R语言实战的作者的另一个作品 76 | 77 | R Gallery Book\[50\]：The R Graph Gallery\[51\]的完整指南 78 | 79 | Beyond Multiple Linear Regression\[52\]：回归分析的拓展：广义线性模型和分层模型 80 | 81 | Applied longitudinal data analysis in brms and the tidyverse\[53\]：纵向数据分析 82 | 83 | Interpretable Machine Learning\[54\]：可解释机器学习 84 | 85 | 现代应用统计与R语言\[55\]：顾名思义 86 | 87 | R语言教程\[56\]：同上 88 | 89 | 统计计算\[57\]：同上 90 | 91 | 零基础学R语言\[58\]：同上 92 | 93 | Rmd权威指南\[59\]：by谢大大 94 | 95 | Rmd中文指南\[60\]：这本似乎还未完待续 96 | 97 | blogdown\[61\]：谢大大用R写博客 98 | 99 | bookdown\[62\]：谢大大用R写书 100 | 101 | 电子书、在线课程、教程 102 | ----------- 103 | 104 | 生物统计手册：Handbook of Biological Statistics\[63\] 以及它的R陪同：An R Companion for the Handbook of Biological Statistics\[64\] 105 | 106 | 部分免费的数据科学课程：DataCamp\[65\]、Dataquest\[66\]、Datanovia\[67\] 107 | 108 | Biomedical Data Science\[68\]：生物医学数据科学 109 | 110 | Introduction to Econometrics with R\[69\]：R语言计量经济学导论（量：第四声） 111 | 112 | Forecasting: Principles and Practice (3rd ed)\[70\]：旨在全面介绍预测方法 113 | 114 | **以下两本是统计学习圣经：** 115 | 116 | An Introduction to Statistical Learning\\(1 ed.\\)\[71\]：ISLR第一版(2021年夏季出第二版：官网\[72\]) 117 | 118 | The Elements of Statistical Learning\[73\]：ESL官网 119 | 120 | 3.R Packages 121 | ------------ 122 | 123 | Awesome R\[74\]：优秀的R包和资料 124 | 125 | tidyverse\[75\]、tidymodels\[76\]：分别代表数据分析、统计模型的一套流程 126 | 127 | ggplot2\[77\] & its 82 extensions\[78\]：可视化领域的少林 128 | 129 | shiny\[79\]：交互、可视化、分析平台(它的画廊\[80\]) 130 | 131 | plotly\[81\]：可视化另一佳作 132 | 133 | htmlwidgets for R\[82\]：126个HTML图形插件 134 | 135 | R任务视图\[83\]：包含了四十多个热门主题，每个主题下面都有几十个包供你选择 136 | 137 | xaringan\[84\]：谢大大用R写ppt英文模板\[85\]、中文模板\[86\] 138 | 139 | R数据集：R自带的datesets\[87\] package、更全的Rdatasets\[88\]（不是package，只是含有dataset的package的信息） 140 | 141 | 4.Others 142 | -------- 143 | 144 | R官方文档\[89\]、R贡献文档\[90\] 145 | 146 | timeline-of-statistics.pdf\[91\]：简明统计学史(by ASA) 147 | 148 | RStudio的cheatsheet\[92\]：快速回顾一些R包的基本语法(支持邮件订阅；鼓励大家参与到该网址中的中文翻译项目；当然除了由RStudio发布的cheatsheet，还有其他机构也会发布，比如DataCamp的cheatsheet\[93\]，其中还有Python的) 149 | 150 | **帮助自学：** 151 | 152 | UCB统计系推荐阅读清单\[94\] 153 | 154 | ASA的统计学本科课程大纲\[95\] 155 | 156 | **阅读材料：** 157 | 158 | Statistical Science Conversations\[96\]：IMS的与一百多位统计学家的访谈专栏 159 | 160 | How R Helps Airbnb Make the Most of its Data\[97\] 161 | 162 | Why Is It Called That Way\\?\\! – Origin and Meaning of R Package Names\[98\]：一些R包名称的由来 163 | 164 | Tidy Data\[99\]：by Hadley Wickham 165 | 166 | 未完待续. 167 | 168 | ### 参考资料 169 | 170 | \[1\] 171 | 172 | CoffeeCat: _https://www.zhihu.com/people/CoffeeCat2000_ 173 | 174 | \[2\] 175 | 176 | Coffee学生物统计的地方: _https://www.zhihu.com/column/c\_1242033096192262144_ 177 | 178 | \[3\] 179 | 180 | 北大李东风: _https://link.zhihu.com/?target=https%3A//www.math.pku.edu.cn/teachers/lidf/_ 181 | 182 | \[4\] 183 | 184 | 中科大张伟平: _https://link.zhihu.com/?target=http%3A//staff.ustc.edu.cn/~zwp/teach.htm_ 185 | 186 | \[5\] 187 | 188 | 谢益辉: _https://link.zhihu.com/?target=https%3A//yihui.org/_ 189 | 190 | \[6\] 191 | 192 | 统计之都论坛: _https://link.zhihu.com/?target=https%3A//d.cosx.org/_ 193 | 194 | \[7\] 195 | 196 | 统计之都: _https://link.zhihu.com/?target=https%3A//cosx.org/_ 197 | 198 | \[8\] 199 | 200 | 统计学资源链接大全: _https://link.zhihu.com/?target=http%3A//staff.ustc.edu.cn/~ynyang/stat-resources.html_ 201 | 202 | \[9\] 203 | 204 | 该老师的主页: _https://link.zhihu.com/?target=http%3A//staff.ustc.edu.cn/~ynyang_ 205 | 206 | \[10\] 207 | 208 | Trevor Hastie: _https://link.zhihu.com/?target=http%3A//www-stat.stanford.edu/~hastie/_ 209 | 210 | \[11\] 211 | 212 | Jerome H. Friedman: _https://link.zhihu.com/?target=http%3A//statweb.stanford.edu/~jhf/_ 213 | 214 | \[12\] 215 | 216 | Rob Tibshirani: _https://link.zhihu.com/?target=http%3A//statweb.stanford.edu/~tibs/_ 217 | 218 | \[13\] 219 | 220 | 顾凯: _https://link.zhihu.com/?target=https%3A//www.bioinfo-scrounger.com/_ 221 | 222 | \[14\] 223 | 224 | revolutionanalytics: _https://link.zhihu.com/?target=https%3A//blog.revolutionanalytics.com/_ 225 | 226 | \[15\] 227 | 228 | r-bloggers: _https://link.zhihu.com/?target=https%3A//www.r-bloggers.com/_ 229 | 230 | \[16\] 231 | 232 | Statistics How To: _https://link.zhihu.com/?target=https%3A//www.statisticshowto.com/_ 233 | 234 | \[17\] 235 | 236 | Statistical Modeling, Causal Inference, and Social Science: _https://link.zhihu.com/?target=https%3A//statmodeling.stat.columbia.edu/_ 237 | 238 | \[18\] 239 | 240 | Error Statistics Philosophy: _https://link.zhihu.com/?target=https%3A//errorstatistics.com/_ 241 | 242 | \[19\] 243 | 244 | Simply Statistics: _https://link.zhihu.com/?target=https%3A//simplystatistics.org/_ 245 | 246 | \[20\] 247 | 248 | Jeff Leek: _https://link.zhihu.com/?target=http%3A//www.biostat.jhsph.edu/~jleek/research.html_ 249 | 250 | \[21\] 251 | 252 | Roger Peng: _https://link.zhihu.com/?target=http%3A//www.biostat.jhsph.edu/~rpeng/_ 253 | 254 | \[22\] 255 | 256 | Rafa Irizarry: _https://link.zhihu.com/?target=http%3A//rafalab.dfci.harvard.edu/_ 257 | 258 | \[23\] 259 | 260 | FLOWINGDATA: _https://link.zhihu.com/?target=https%3A//flowingdata.com/_ 261 | 262 | \[24\] 263 | 264 | Statistics by Jim: _https://link.zhihu.com/?target=https%3A//statisticsbyjim.com/_ 265 | 266 | \[25\] 267 | 268 | Library Genesis: _https://link.zhihu.com/?target=http%3A//libgen.rs/_ 269 | 270 | \[26\] 271 | 272 | 亚马逊: _https://link.zhihu.com/?target=http%3A//amazon.com/_ 273 | 274 | \[27\] 275 | 276 | Routledge: _https://link.zhihu.com/?target=https%3A//www.routledge.com/_ 277 | 278 | \[28\] 279 | 280 | Chapman & Hall/CRC Texts in Statistical Science: _https://link.zhihu.com/?target=https%3A//www.routledge.com/Chapman--HallCRC-Texts-in-Statistical-Science/book-series/CHTEXSTASCI_ 281 | 282 | \[29\] 283 | 284 | Chapman & Hall/CRC Biostatistics Series: _https://link.zhihu.com/?target=https%3A//www.routledge.com/Chapman--HallCRC-Biostatistics-Series/book-series/CHBIOSTATIS_ 285 | 286 | \[30\] 287 | 288 | Springer: _https://link.zhihu.com/?target=https%3A//www.springer.com/_ 289 | 290 | \[31\] 291 | 292 | Springer Statistics: _https://link.zhihu.com/?target=https%3A//www.springer.com/gp/statistics_ 293 | 294 | \[32\] 295 | 296 | Elsevier: _https://link.zhihu.com/?target=https%3A//www.elsevier.com/_ 297 | 298 | \[33\] 299 | 300 | Oxford University Press: _https://link.zhihu.com/?target=https%3A//global.oup.com/academic/%3Fcc%3Dus%26lang%3Den%26_ 301 | 302 | \[34\] 303 | 304 | Probability & Statistics: _https://link.zhihu.com/?target=https%3A//global.oup.com/academic/category/science-and-mathematics/mathematics/probability-and-statistics/%3Fcc%3Dus%26lang%3Den%26_ 305 | 306 | \[35\] 307 | 308 | Cambridge University Press: _https://link.zhihu.com/?target=https%3A//www.cambridge.org/cn/academic_ 309 | 310 | \[36\] 311 | 312 | Statistics and probability: _https://link.zhihu.com/?target=https%3A//www.cambridge.org/cn/academic/subjects/statistics-probability/_ 313 | 314 | \[37\] 315 | 316 | Bookdown: _https://link.zhihu.com/?target=https%3A//bookdown.org/home/archive/_ 317 | 318 | \[38\] 319 | 320 | 数据科学中的R语言: _https://link.zhihu.com/?target=https%3A//bookdown.org/wangminjie/R4DS/_ 321 | 322 | \[39\] 323 | 324 | R语言忍者秘籍: _https://link.zhihu.com/?target=https%3A//bookdown.org/yihui/r-ninja/_ 325 | 326 | \[40\] 327 | 328 | 现代统计图形: _https://link.zhihu.com/?target=https%3A//bookdown.org/xiangyun/msg/_ 329 | 330 | \[41\] 331 | 332 | Statistics Handbook: _https://link.zhihu.com/?target=https%3A//bookdown.org/mpfoley1973/statistics/_ 333 | 334 | \[42\] 335 | 336 | R for Data Science: _https://link.zhihu.com/?target=https%3A//bookdown.org/roy\_schumacher/r4ds/_ 337 | 338 | \[43\] 339 | 340 | Hadley Wickham: _https://link.zhihu.com/?target=http%3A//hadley.nz/_ 341 | 342 | \[44\] 343 | 344 | tidyverse: _https://link.zhihu.com/?target=https%3A//www.tidyverse.org/packages/_ 345 | 346 | \[45\] 347 | 348 | Advanced R: _https://link.zhihu.com/?target=https%3A//adv-r.hadley.nz/_ 349 | 350 | \[46\] 351 | 352 | Hadley Wickham: _https://link.zhihu.com/?target=http%3A//hadley.nz/_ 353 | 354 | \[47\] 355 | 356 | 习题解答: _https://link.zhihu.com/?target=https%3A//advanced-r-solutions.rbind.io/_ 357 | 358 | \[48\] 359 | 360 | R Graphics Cookbook: _https://link.zhihu.com/?target=https%3A//r-graphics.org/_ 361 | 362 | \[49\] 363 | 364 | Data Visualization with R: _https://link.zhihu.com/?target=https%3A//rkabacoff.github.io/datavis/_ 365 | 366 | \[50\] 367 | 368 | R Gallery Book: _https://link.zhihu.com/?target=https%3A//bookdown.org/content/b298e479-b1ab-49fa-b83d-a57c2b034d49/_ 369 | 370 | \[51\] 371 | 372 | The R Graph Gallery: _https://link.zhihu.com/?target=https%3A//www.r-graph-gallery.com/_ 373 | 374 | \[52\] 375 | 376 | Beyond Multiple Linear Regression: _https://link.zhihu.com/?target=https%3A//bookdown.org/roback/bookdown-BeyondMLR/_ 377 | 378 | \[53\] 379 | 380 | Applied longitudinal data analysis in brms and the tidyverse: _https://link.zhihu.com/?target=https%3A//bookdown.org/content/ef0b28f7-8bdf-4ba7-ae2c-bc2b1f012283/_ 381 | 382 | \[54\] 383 | 384 | Interpretable Machine Learning: _https://link.zhihu.com/?target=https%3A//christophm.github.io/interpretable-ml-book/_ 385 | 386 | \[55\] 387 | 388 | 现代应用统计与R语言: _https://link.zhihu.com/?target=https%3A//bookdown.org/xiangyun/masr/_ 389 | 390 | \[56\] 391 | 392 | R语言教程: _https://link.zhihu.com/?target=https%3A//www.math.pku.edu.cn/teachers/lidf/docs/Rbook/html/\_Rbook/index.html_ 393 | 394 | \[57\] 395 | 396 | 统计计算: _https://link.zhihu.com/?target=https%3A//www.math.pku.edu.cn/teachers/lidf/docs/statcomp/html/\_statcompbook/index.html_ 397 | 398 | \[58\] 399 | 400 | 零基础学R语言: _https://link.zhihu.com/?target=https%3A//bookdown.org/qiyuandong/intro\_r/_ 401 | 402 | \[59\] 403 | 404 | Rmd权威指南: _https://link.zhihu.com/?target=https%3A//bookdown.org/yihui/rmarkdown/_ 405 | 406 | \[60\] 407 | 408 | Rmd中文指南: _https://link.zhihu.com/?target=https%3A//bookdown.org/qiushi/rmarkdown-guide/_ 409 | 410 | \[61\] 411 | 412 | blogdown: _https://link.zhihu.com/?target=https%3A//bookdown.org/yihui/blogdown/_ 413 | 414 | \[62\] 415 | 416 | bookdown: _https://link.zhihu.com/?target=https%3A//bookdown.org/home/about/_ 417 | 418 | \[63\] 419 | 420 | Handbook of Biological Statistics: _https://link.zhihu.com/?target=http%3A//www.biostathandbook.com/_ 421 | 422 | \[64\] 423 | 424 | An R Companion for the Handbook of Biological Statistics: _https://link.zhihu.com/?target=https%3A//rcompanion.org/rcompanion/index.html_ 425 | 426 | \[65\] 427 | 428 | DataCamp: _https://zhuanlan.zhihu.com/p/366590161/www.datacamp.com_ 429 | 430 | \[66\] 431 | 432 | Dataquest: _https://link.zhihu.com/?target=https%3A//www.dataquest.io/_ 433 | 434 | \[67\] 435 | 436 | Datanovia: _https://link.zhihu.com/?target=https%3A//www.datanovia.com/en/_ 437 | 438 | \[68\] 439 | 440 | Biomedical Data Science: _https://link.zhihu.com/?target=http%3A//genomicsclass.github.io/book/_ 441 | 442 | \[69\] 443 | 444 | Introduction to Econometrics with R: _https://link.zhihu.com/?target=https%3A//www.econometrics-with-r.org/_ 445 | 446 | \[70\] 447 | 448 | Forecasting: Principles and Practice (3rd ed): _https://link.zhihu.com/?target=https%3A//otexts.com/fpp3/index.html_ 449 | 450 | \[71\] 451 | 452 | An Introduction to Statistical Learning(1 ed.): _https://link.zhihu.com/?target=https%3A//www.statlearning.com/s/ISLRSeventhPrinting.pdf_ 453 | 454 | \[72\] 455 | 456 | 官网: _https://link.zhihu.com/?target=https%3A//www.statlearning.com/_ 457 | 458 | \[73\] 459 | 460 | The Elements of Statistical Learning: _https://link.zhihu.com/?target=https%3A//web.stanford.edu/~hastie/ElemStatLearn/_ 461 | 462 | \[74\] 463 | 464 | Awesome R: _https://link.zhihu.com/?target=https%3A//github.com/qinwf/awesome-R/blob/master/README.md_ 465 | 466 | \[75\] 467 | 468 | tidyverse: _https://link.zhihu.com/?target=https%3A//www.tidyverse.org/packages/_ 469 | 470 | \[76\] 471 | 472 | tidymodels: _https://link.zhihu.com/?target=https%3A//www.tidymodels.org/packages/_ 473 | 474 | \[77\] 475 | 476 | ggplot2: _https://link.zhihu.com/?target=https%3A//ggplot2.tidyverse.org/_ 477 | 478 | \[78\] 479 | 480 | its 82 extensions: _https://link.zhihu.com/?target=https%3A//exts.ggplot2.tidyverse.org/gallery/_ 481 | 482 | \[79\] 483 | 484 | shiny: _https://link.zhihu.com/?target=https%3A//shiny.rstudio.com/_ 485 | 486 | \[80\] 487 | 488 | 它的画廊: _https://link.zhihu.com/?target=https%3A//shiny.rstudio.com/gallery/_ 489 | 490 | \[81\] 491 | 492 | plotly: _https://link.zhihu.com/?target=https%3A//plotly.com/r/_ 493 | 494 | \[82\] 495 | 496 | htmlwidgets for R: _https://link.zhihu.com/?target=https%3A//gallery.htmlwidgets.org/_ 497 | 498 | \[83\] 499 | 500 | R任务视图: _https://link.zhihu.com/?target=https%3A//cran.r-project.org/web/views/_ 501 | 502 | \[84\] 503 | 504 | xaringan: _https://link.zhihu.com/?target=https%3A//github.com/yihui/xaringan_ 505 | 506 | \[85\] 507 | 508 | 英文模板: _https://link.zhihu.com/?target=https%3A//slides.yihui.org/xaringan/_ 509 | 510 | \[86\] 511 | 512 | 中文模板: _https://link.zhihu.com/?target=https%3A//slides.yihui.org/xaringan/zh-CN.html_ 513 | 514 | \[87\] 515 | 516 | datesets: _https://link.zhihu.com/?target=https%3A//stat.ethz.ch/R-manual/R-devel/library/datasets/html/00Index.html_ 517 | 518 | \[88\] 519 | 520 | Rdatasets: _https://link.zhihu.com/?target=https%3A//vincentarelbundock.github.io/Rdatasets/articles/data.html_ 521 | 522 | \[89\] 523 | 524 | R官方文档: _https://link.zhihu.com/?target=https%3A//www.r-project.org/other-docs.html_ 525 | 526 | \[90\] 527 | 528 | R贡献文档: _https://link.zhihu.com/?target=https%3A//cran.r-project.org/other-docs.html_ 529 | 530 | \[91\] 531 | 532 | timeline-of-statistics.pdf: _https://link.zhihu.com/?target=http%3A//www.statslife.org.uk/images/pdf/timeline-of-statistics.pdf_ 533 | 534 | \[92\] 535 | 536 | RStudio的cheatsheet: _https://link.zhihu.com/?target=https%3A//www.rstudio.com/resources/cheatsheets/_ 537 | 538 | \[93\] 539 | 540 | DataCamp的cheatsheet: _https://link.zhihu.com/?target=https%3A//www.datacamp.com/community/data-science-cheatsheets_ 541 | 542 | \[94\] 543 | 544 | UCB统计系推荐阅读清单: _https://link.zhihu.com/?target=http%3A//sgsa.berkeley.edu/current\_students/books/_ 545 | 546 | \[95\] 547 | 548 | ASA的统计学本科课程大纲: _https://link.zhihu.com/?target=http%3A//www.amstat.org/education/pdfs/guidelines2014-11-15.pdf_ 549 | 550 | \[96\] 551 | 552 | Statistical Science Conversations: _https://link.zhihu.com/?target=https%3A//imstat.org/journals-and-publications/statistical-science/conversations/_ 553 | 554 | \[97\] 555 | 556 | How R Helps Airbnb Make the Most of its Data: _https://link.zhihu.com/?target=https%3A//www.tandfonline.com/doi/full/10.1080/00031305.2017.1392362_ 557 | 558 | \[98\] 559 | 560 | Why Is It Called That Way?! – Origin and Meaning of R Package Names: _https://link.zhihu.com/?target=https%3A//www.statworx.com/en/blog/why-is-it-called-that-way-origin-and-meaning-of-r-package-names/_ 561 | 562 | \[99\] 563 | 564 | Tidy Data: _https://link.zhihu.com/?target=https%3A//vita.had.co.nz/papers/tidy-data.pdf_ 565 | 566 | **推荐：** 可以保存以下照片，在b站扫该二维码，或者b站搜索【`庄闪闪`】观看Rmarkdown系列的视频教程。Rmarkdown视频新增两节视频（**写轮眼幻灯片制作**）需要视频内的文档，可在公众号回复【`rmarkdown`】 567 | 568 | ![图片](https://mmbiz.qpic.cn/mmbiz_jpg/MIcgkkEyTHiaeFwBoiay0lvVXcbQeRaN8gYUHG5cbmFfv2QaichZdDbZhxKtlB0FSJJJczAQLAO04Pny5mlhoId1w/640?wx_fmt=jpeg&wxfrom=5&wx_lazy=1&wx_co=1) 569 | 570 | 571 | 572 | [ 573 | 574 | ![图片](https://mmbiz.qpic.cn/mmbiz_jpg/MIcgkkEyTHjqYtTRnKYwYP4k7iaZoMibUQYZg8ziaLUAFiaIK4cNS6u5iazLD9ffuN9C8M9pk0Nq5y7GhM5npfTibnRw/640?wx_fmt=jpeg&wxfrom=5&wx_lazy=1&wx_co=1) 575 | 576 | R沟通｜Rmarkdown教程（4） 577 | 578 | 579 | 580 | 581 | 582 | 583 | 584 | ](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247492028&idx=1&sn=9964f55b6cdc984f22620330e752226e&chksm=ea271e58dd50974e35b274816cd9dde9cda3c0864487b187b4f2ac87c9773d782b02e5b28816&scene=21#wechat_redirect) 585 | 586 | 587 | 588 | [ 589 | 590 | ![图片](https://mmbiz.qpic.cn/mmbiz_jpg/MIcgkkEyTHjQaBFpiaqVdicmbG9Qc6jzc6libMFWfa3BibLBAuEvheHibyV9XmEElb1t3DbCLQksuL8TK9PWYaZsmrw/640?wx_fmt=jpeg&wxfrom=5&wx_lazy=1&wx_co=1) 591 | 592 | R沟通｜Rmarkdown教程（3） 593 | 594 | 595 | 596 | 597 | 598 | 599 | 600 | ](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247491844&idx=1&sn=36decacb06ca6ce1fc689141174bb98f&chksm=ea271ee0dd5097f615b87c27151200635a9e5ff3c3072864bcf8fe6e8fbc41c0cffc6c95148f&scene=21#wechat_redirect) 601 | 602 | 603 | 604 | [ 605 | 606 | ![图片](https://mmbiz.qpic.cn/mmbiz_jpg/MIcgkkEyTHiayicgGYwRzibR9sxwM8TDrHOXun1v2WF5SpvjOYrTvQh1A9pmu8NLzId0pcZ3j1MYmib5ibeqk6icUeAA/640?wx_fmt=jpeg&wxfrom=5&wx_lazy=1&wx_co=1) 607 | 608 | R沟通｜Rmarkdown教程（2） 609 | 610 | 611 | 612 | 613 | 614 | 615 | 616 | ](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247491546&idx=1&sn=00f8dea8903dbf4ec6e683ab5061a7a5&chksm=ea24e03edd536928ff6c5a3600c8fbbd87cafbf9286ad47bfe4c084032cada9bf6ee7dfddcd9&scene=21#wechat_redirect) 617 | 618 | 619 | 620 | [ 621 | 622 | ![图片](https://mmbiz.qpic.cn/mmbiz_jpg/MIcgkkEyTHgQIt6ob17tBZRRISiczGtKzRNPmiceYZib7KnNOWw2gM971ugt1KuY99tTwNicIxBB6If2AjCIicxR9TQ/640?wx_fmt=jpeg&wxfrom=5&wx_lazy=1&wx_co=1) 623 | 624 | R沟通｜Rmarkdown教程（1） 625 | 626 | 627 | 628 | 629 | 630 | 631 | 632 | ](http://mp.weixin.qq.com/s?__biz=MzI1NjUwMjQxMQ==&mid=2247491355&idx=1&sn=79739adfde0822e2eedc60e7c6761820&chksm=ea24e0ffdd5369e9806d20c84c669febced9c36595f15357b3ff108f82e62cc105f0a0c456fe&scene=21#wechat_redirect) 633 | 634 | 635 | [Source](https://mp.weixin.qq.com/s/hLU-pFZ7xf0Fl0OrK-sYPw) -------------------------------------------------------------------------------- /docs/DataScience/_category_.json: -------------------------------------------------------------------------------- 1 | { 2 | "label": "Data Science", 3 | "position": 3 4 | } 5 | -------------------------------------------------------------------------------- /docs/DataScience/datascience.md: -------------------------------------------------------------------------------- 1 | --- 2 | sidebar_position: 1 3 | --- 4 | 5 | [academic/awesome-datascience: An awesome Data Science repository to learn and apply for real world problems.](https://github.com/academic/awesome-datascience) 6 | 7 | [R分享｜100 个统计学和 R 语言学习资源网站](https://mp.weixin.qq.com/s/hLU-pFZ7xf0Fl0OrK-sYPw) 8 | 9 | [【精心整理】2022年各专业领域全网最新最顶级的R语言新书 - 知乎](https://zhuanlan.zhihu.com/p/530525295) 10 | # R 11 | [Home | Bookdown](https://bookdown.org/) 12 | 13 | [hadley/r4ds: R for data science: a book](https://github.com/hadley/r4ds) 14 | 15 | R_for_Data_Science-master2021605101531:数据科学中的 R 语言 16 | [数据科学中的 R 语言](https://bookdown.org/wangminjie/R4DS/) 17 | 18 | 19 | [李东风R语言教程](https://www.math.pku.edu.cn/teachers/lidf/docs/Rbook/html/_Rbook/index.html) 20 | 21 | R in Action by Robert I. Kabacoff 2021530132511.pdf 22 | ## Rmarkdown and Bookdown 23 | 24 | [R Markdown: The Definitive Guide](https://bookdown.org/yihui/rmarkdown/) 25 | 26 | [R Markdown Cookbook](https://bookdown.org/yihui/rmarkdown-cookbook/) 27 | 28 | [bookdown: Authoring Books and Technical Documents with R Markdown](https://bookdown.org/yihui/bookdown/) 29 | 30 | [https://mp.weixin.qq.com/mp/homepage?__biz=MzI1NjUwMjQxMQ==&hid=9&sn=bd6a66baabf4a5de929d0934306e3f0e&scene=18#wechat_redirect](https://mp.weixin.qq.com/mp/homepage?__biz=MzI1NjUwMjQxMQ==&hid=9&sn=bd6a66baabf4a5de929d0934306e3f0e&scene=18#wechat_redirect) 31 | 32 | 33 | 34 | # Python 35 | 36 | [wesm/pydata-book: Materials and IPython notebooks for "Python for Data Analysis" by Wes McKinney, published by O'Reilly Media](https://github.com/wesm/pydata-book) 37 | 38 | 39 | 40 | -------------------------------------------------------------------------------- /docs/DataScience/【精心整理】2022年各专业领域全网最新最顶级的R语言新书.md: -------------------------------------------------------------------------------- 1 | # 【精心整理】2022年各专业领域全网最新最顶级的R语言新书 2 | 3 | > 由张敬信和 tidy R语言群部分群友整理分享，绝对最新、最顶级，欢迎补充： 专业领域TOP-R书籍或资源作者书籍地址R编程R语言编程：基于tidyverse张敬信https://zhuanlan.zhihu.com/p/467134727R编程R语言教程李东风… 4 | 5 | 专业领域TOP-R书籍或资源作者书籍地址R编程R语言编程：基于tidyverse张敬信[https://zhuanlan.zhihu.com/p/467134727](https://zhuanlan.zhihu.com/p/467134727)R编程R语言教程李东风[https://www.math.pku.edu.cn/teachers/lidf/docs/Rbook/html/\_Rbook/index.html](https://link.zhihu.com/?target=https%3A//www.math.pku.edu.cn/teachers/lidf/docs/Rbook/html/_Rbook/index.html)R编程Advanced RHadley[https://adv-r.hadley.nz/](https://link.zhihu.com/?target=https%3A//adv-r.hadley.nz/)R编程Efficient R programmingColin Gillespie, Robin Lovelace[https://csgillespie.github.io/efficientR/](https://link.zhihu.com/?target=https%3A//csgillespie.github.io/efficientR/)R可视化ggplot2: Elegant Graphics for Data AnalysisHadley[https://ggplot2-book.org/](https://link.zhihu.com/?target=https%3A//ggplot2-book.org/)R可视化Interactive web-based data visualization with R, plotly, and shinyCarson Sievert[https://plotly-r.com/](https://link.zhihu.com/?target=https%3A//plotly-r.com/)R可视化现代统计图形赵鹏，谢益辉，黄湘云[https://bookdown.org/xiangyun/msg/](https://link.zhihu.com/?target=https%3A//bookdown.org/xiangyun/msg/)R可视化rstudio::conf 2020 data visualization workshopKieran Healy[https://github.com/rstudio-conf\-2020/dataviz](https://link.zhihu.com/?target=https%3A//github.com/rstudio-conf-2020/dataviz)R可视化R Graphics Cookbook, 2nd editionWinston Chang[https://r-graphics.org/](https://link.zhihu.com/?target=https%3A//r-graphics.org/)R可视化R语言数据可视化之美张杰[https://gitee.com/easyshu/Beautiful-Visualization-with-R](https://link.zhihu.com/?target=https%3A//gitee.com/easyshu/Beautiful-Visualization-with-R)文档沟通R Markdown Cookbook谢益辉[https://bookdown.org/yihui/rmarkdown-cookbook/](https://link.zhihu.com/?target=https%3A//bookdown.org/yihui/rmarkdown-cookbook/)文档沟通R Markdown: The Definitive Guide谢益辉[https://bookdown.org/yihui/rmarkdown/](https://link.zhihu.com/?target=https%3A//bookdown.org/yihui/rmarkdown/)文档沟通bookdown: Authoring Books and Technical Documents with R Markdown谢益辉[https://bookdown.org/yihui/bookdown/](https://link.zhihu.com/?target=https%3A//bookdown.org/yihui/bookdown/)开发R包R PackagesHadley[https://r-pkgs.org/](https://link.zhihu.com/?target=https%3A//r-pkgs.org/)ShinyMastering ShinyHadley[https://mastering-shiny.org/](https://link.zhihu.com/?target=https%3A//mastering-shiny.org/)ShinyEngineering Production-Grade Shiny AppsColin Fay, Sébastien Rochette, Vincent Guyader and Cervan Girard[https://engineering-shiny.org/](https://link.zhihu.com/?target=https%3A//engineering-shiny.org/)ShinyJavaScript for RJohn Coene[https://javascript-for-r.com/](https://link.zhihu.com/?target=https%3A//javascript-for-r.com/)应用统计Statistical Inference via Data ScienceChester Ismay and Albert Y. Kim[https://moderndive.com/](https://link.zhihu.com/?target=https%3A//moderndive.com/)应用统计Introduction to Modern StatisticsMine Çetinkaya-Rundel[https://openintro-ims.netlify.app/](https://link.zhihu.com/?target=https%3A//openintro-ims.netlify.app/)应用统计现代应用统计与 R 语言黄湘云[https://bookdown.org/xiangyun/masr/](https://link.zhihu.com/?target=https%3A//bookdown.org/xiangyun/masr/)应用统计统计学与R语言(课件)张敬信tidy-R语言2群（222427909）群文件实验设计The Grammar of Experimental DesignsEmi Tanaka[https://emitanaka.org/edibble-book/](https://link.zhihu.com/?target=https%3A//emitanaka.org/edibble-book/)贝叶斯Doing Bayesian Data Analysis in brms and the tidyverseA Solomon Kurz[https://bookdown.org/content/3686/](https://link.zhihu.com/?target=https%3A//bookdown.org/content/3686/)贝叶斯Introduction to Bayesian Econometrics: A GUIded tour using RAndrés Ramírez-Hassan[https://bookdown.org/aramir21/IntroductionBayesianEconometricsGuidedTour/](https://link.zhihu.com/?target=https%3A//bookdown.org/aramir21/IntroductionBayesianEconometricsGuidedTour/)贝叶斯An Introduction to Bayesian Reasoning and MethodsKevin Ross[https://bookdown.org/kevin\_davisross/bayesian-reasoning-and-methods/](https://link.zhihu.com/?target=https%3A//bookdown.org/kevin_davisross/bayesian-reasoning-and-methods/)贝叶斯Statistical rethinking with brms, ggplot2, and the tidyverse: Second editionA Solomon Kurz[https://bookdown.org/content/70a06054-8138-4d90-aaa0-895f57aab1b4/](https://link.zhihu.com/?target=https%3A//bookdown.org/content/70a06054-8138-4d90-aaa0-895f57aab1b4/)数据科学R for Data ScienceHadley[https://r4ds.had.co.nz/](https://link.zhihu.com/?target=https%3A//r4ds.had.co.nz/)数据科学Tidyverse Skills for Data Science in RCarrie Wright, Shannon Ellis, Stephanie Hicks, and Roger D. Peng[https://leanpub.com/tidyverseskillsdatascience](https://link.zhihu.com/?target=https%3A//leanpub.com/tidyverseskillsdatascience)数据科学数据科学中的R语言王敏杰[https://bookdown.org/wangminjie/R4DS/](https://link.zhihu.com/?target=https%3A//bookdown.org/wangminjie/R4DS/)数据科学Data Science Live BookPablo Casas[https://livebook.datascienceheroes.com/](https://link.zhihu.com/?target=https%3A//livebook.datascienceheroes.com/)数据科学R语言实战（第三版）Rob Kabacoff[https://livebook.manning.com/book/r-in-action-third-edition/](https://link.zhihu.com/?target=https%3A//livebook.manning.com/book/r-in-action-third-edition/)数据科学Modern Data Science with R (2nd)Benjamin S. Baumer, Daniel T. Kaplan, and Nicholas J. Horton[https://mdsr-book.github.io/mdsr2e/](https://link.zhihu.com/?target=https%3A//mdsr-book.github.io/mdsr2e/)机器学习mlr3book官方出品[https://mlr3book.mlr-org.com/](https://link.zhihu.com/?target=https%3A//mlr3book.mlr-org.com/)机器学习Tidy Modeling with RMAX KUHN AND JULIASILGE[https://www.tmwr.org/index.html](https://link.zhihu.com/?target=https%3A//www.tmwr.org/index.html)机器学习Hands-On Machine Learning with RBradley Boehmke & Brandon Greenwell[https://bradleyboehmke.github.io/HOML/](https://link.zhihu.com/?target=https%3A//bradleyboehmke.github.io/HOML/)机器学习Feature Engineering and Selection: A Practical Approach for Predictive ModelsMax Kuhn and Kjell Johnson[http://www.feat.engineering/](https://link.zhihu.com/?target=http%3A//www.feat.engineering/)机器学习An Introduction to Statistical Learning with RGareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani[https://trevorhastie.github.io/ISLR/](https://link.zhihu.com/?target=https%3A//trevorhastie.github.io/ISLR/) 6 | [https://emilhvitfeldt.github.io/ISLR-tidymodels-labs/index.html](https://link.zhihu.com/?target=https%3A//emilhvitfeldt.github.io/ISLR-tidymodels-labs/index.html)机器学习R机器学习：基于mlr3verse(课件)张敬信tidy-R语言2群（222427909）群文件深度学习Deep Learning with R, Second EditionFrançois Chollet with Tomasz Kalinowski and J. J. Allaire[https://www.manning.com/books/deep-learning-with-r-second-edition](https://link.zhihu.com/?target=https%3A//www.manning.com/books/deep-learning-with-r-second-edition)文本挖掘Supervised Machine Learning for Text Analysis in REMIL HVITFELDT AND JULIA SILGE[https://smltar.com/](https://link.zhihu.com/?target=https%3A//smltar.com/)文本挖掘QUANTEDA TUTORIALSKohei Watanabe and Stefan Müller[https://tutorials.quanteda.io/](https://link.zhihu.com/?target=https%3A//tutorials.quanteda.io/)文本挖掘Text Analysis TutorialsLADAL[https://slcladal.github.io/index.html](https://link.zhihu.com/?target=https%3A//slcladal.github.io/index.html)计量Introduction to Econometrics with RChristoph Hanck, Martin Arnold, Alexander Gerber, and Martin Schmelzer[https://www.econometrics-with-r.org/](https://link.zhihu.com/?target=https%3A//www.econometrics-with-r.org/)计量Beyond Multiple Linear Regression: Applied Generalized Linear Models and Multilevel Models in RPaul Roback and Julie Legler[https://bookdown.org/roback/bookdown-BeyondMLR/](https://link.zhihu.com/?target=https%3A//bookdown.org/roback/bookdown-BeyondMLR/)计量Mixed Models with R Getting started with random effectsMichael Clark[https://m-clark.github.io/mixed\-models-with-R/](https://link.zhihu.com/?target=https%3A//m-clark.github.io/mixed-models-with-R/)时间序列Forecasting: Principles and Practice (3rd ed)RobJHyndman[https://otexts.com/fpp3/](https://link.zhihu.com/?target=https%3A//otexts.com/fpp3/)时间序列金融时间序列分析讲义李东风[https://www.math.pku.edu.cn/teachers/lidf/course/fts/ftsnotes/html/\_ftsnotes/index.html](https://link.zhihu.com/?target=https%3A//www.math.pku.edu.cn/teachers/lidf/course/fts/ftsnotes/html/_ftsnotes/index.html)金融Tidy Finance with RChristoph Scheuch, Stefan Voigt, and Patrick Weiss[https://tidy-finance.org/](https://link.zhihu.com/?target=https%3A//tidy-finance.org/)空间数据分析Geocomputation with RRobin Lovelace, Jakub Nowosad, Jannes Muenchow[https://geocompr.robinlovelace.net](https://link.zhihu.com/?target=https%3A//geocompr.robinlovelace.net)空间数据分析Spatial Data Science with applications in REdzer Pebesma, Roger Bivand[https://keen-swartz-3146c4.netlify.app/](https://link.zhihu.com/?target=https%3A//keen-swartz-3146c4.netlify.app/)空间数据分析NHH ECS530 2021 course: Spatial data analysis (with R)Roger Bivand[https://rsbivand.github.io/ECS530\_h21/](https://link.zhihu.com/?target=https%3A//rsbivand.github.io/ECS530_h21/)因果推断The Effect: An Introduction to Research Design and CausalityNick Huntington-Klein[https://theeffectbook.net/](https://link.zhihu.com/?target=https%3A//theeffectbook.net/)因果推断Causal Inference: The MixtapeScott Cunningham[https://mixtape.scunning.com/index.html](https://link.zhihu.com/?target=https%3A//mixtape.scunning.com/index.html)社会科学Data Analytics for the Social Sciences Applications in RG. David Garson社会科学A Business Analyst’s Introduction to Business AnalyticsAdam Fleischhacker[https://www.causact.com/](https://link.zhihu.com/?target=https%3A//www.causact.com/)社会科学Computing for the Social SciencesBenjamin Soltoff[https://cfss.uchicago.edu/notes/intro-to-course/](https://link.zhihu.com/?target=https%3A//cfss.uchicago.edu/notes/intro-to-course/)网络分析Methods for Network AnalysisMark Hoffman[https://bookdown.org/markhoff/social\_network\_analysis/](https://link.zhihu.com/?target=https%3A//bookdown.org/markhoff/social_network_analysis/)网络分析Handbook of Graphs and Networks in People Analytics: With Examples in R and PythonKeith McNulty[https://ona-book.org/](https://link.zhihu.com/?target=https%3A//ona-book.org/)网络建模workshop2020\_Network Modeling for Epidemics[http://statnet.org](https://link.zhihu.com/?target=http%3A//statnet.org)[https://statnet.org/nme/d1.html](https://link.zhihu.com/?target=https%3A//statnet.org/nme/d1.html)模型计算Model Estimation by Example Demonstrations with RMichael Clark[https://m-clark.github.io/models-by-example/](https://link.zhihu.com/?target=https%3A//m-clark.github.io/models-by-example/)模型计算Computer-age Calculus with RDaniel Kaplan[https://dtkaplan.github.io/RforCalculus/](https://link.zhihu.com/?target=https%3A//dtkaplan.github.io/RforCalculus/)大数据Mastering Spark with RJavier Luraschi, Kevin Kuo, Edgar Ruiz[https://therinspark.com/](https://link.zhihu.com/?target=https%3A//therinspark.com/)元分析Doing Meta-Analysis in R: A Hands-on GuideHarrer, M., Cuijpers, P., Furukawa, T.A., & Ebert, D.D[https://bookdown.org/MathiasHarrer/Doing\_Meta\_Analysis\_in\_R/](https://link.zhihu.com/?target=https%3A//bookdown.org/MathiasHarrer/Doing_Meta_Analysis_in_R/)生存分析Applied Survival Analysis Using RDirk F. Moore 7 | 8 | 9 | [Source](https://zhuanlan.zhihu.com/p/530525295) -------------------------------------------------------------------------------- /docs/DataSources/DataIOtools.md: -------------------------------------------------------------------------------- 1 | --- 2 | sidebar_position: 1 3 | --- 4 | 5 | 6 | # PDF and Python 7 | [camelot-dev/camelot: A Python library to extract tabular data from PDFs](https://github.com/camelot-dev/camelot) 8 | 9 | # Excel and Python 10 | [Python Resources for working with Excel - Working with Excel Files in Python](http://www.python-excel.org/) 11 | 12 | [openpyxl - A Python library to read/write Excel 2010 xlsx/xlsm files — openpyxl 3.0.9 documentation](https://openpyxl.readthedocs.io/en/stable/) -------------------------------------------------------------------------------- /docs/DataSources/EpiData.md: -------------------------------------------------------------------------------- 1 | --- 2 | sidebar_position: 2 3 | --- 4 | 5 | # Websites 6 | 7 | WHO [Coronavirus disease (COVID-19)](https://www.who.int/emergencies/diseases/novel-coronavirus-2019) 8 | 9 | CDC [COVID-19 Science and Research | CDC](https://www.cdc.gov/coronavirus/2019-ncov/science/science-and-research.html) 10 | 11 | ECDC[Homepage | European Centre for Disease Prevention and Control](https://www.ecdc.europa.eu/en) 12 | 13 | [Coronavirus disease (COVID-19) outbreak updates, symptoms, prevention, travel, preparation - Canada.ca](https://www.canada.ca/en/public-health/services/diseases/coronavirus-disease-covid-19.html) 14 | 15 | # Packages 16 | [covid19datahub/COVID19: A worldwide epidemiological database for COVID-19 at fine-grained spatial resolution](https://github.com/covid19datahub/COVID19) 17 | 18 | [Subnational Data for COVID-19 Epidemiology • covidregionaldata](https://epiforecasts.io/covidregionaldata/) 19 | 20 | [kjhealy/covdata: COVID-related data from a variety of sources, packaged for use in R](https://github.com/kjhealy/covdata) 21 | 22 | [mponce0/covid19.analytics: R package to obtain and analyze live data from the nCOVID19 coronavirus](https://github.com/mponce0/covid19.analytics) 23 | 24 | # Good Datasets 25 | 26 | 27 | Epidemic Data Sources: 28 | 29 | Good[COVID-19 Pandemic - Humanitarian Data Exchange](https://data.humdata.org/event/covid-19) 30 | 31 | Good[Open data in the COVID-19 pandemic](https://www.nature.com/collections/ebaiehhfhg) 32 | 33 | Good[COVID-19 Data & Statistical Sources | Map and Data Library](https://mdl.library.utoronto.ca/covid-19-data-statistical-sources) 34 | 35 | [IZA - Institute of Labor Economics](https://www.iza.org/research/idsc/covid-19-resources) 36 | 37 | [COVID-19 Data & Statistical Sources | Map and Data Library](https://mdl.library.utoronto.ca/covid-19-data-statistical-sources) 38 | 39 | [soroushchehresa/awesome-coronavirus: 🦠 Huge collection of useful projects and resources for COVID-19 (2019 novel Coronavirus)](https://github.com/soroushchehresa/awesome-coronavirus) 40 | 41 | [COVID-19 Dashboards | A site that displays up to date COVID-19 stats, powered by fastpages.](https://covid19dashboards.com/) 42 | 43 | 44 | Best[owid/covid-19-data: Data on COVID-19 (coronavirus) cases, deaths, hospitalizations, tests • All countries • Updated daily by Our World in Data](https://github.com/owid/covid-19-data) 45 | [Our World in Data](https://github.com/owid) 46 | 47 | 48 | [Download COVID-19 datasets](https://www.ecdc.europa.eu/en/covid-19/data) 49 | 50 | 51 | [Data - COVID-19's Impact on Business - Research Sources & Guides at Stanford University Business School](https://libguides.stanford.edu/library/covid19#s-lg-box-23867902) 52 | 53 | # Cases data associataion datasets 54 | 55 | ## Case death recovery Data 56 | Best R[covid19datahub/COVID19: A worldwide epidemiological database for COVID-19 at fine-grained spatial resolution](https://github.com/covid19datahub/COVID19) 57 | 58 | Best[CSSEGISandData/COVID-19: Novel Coronavirus (COVID-19) Cases, provided by JHU CSSE](https://github.com/CSSEGISandData/COVID-19) 59 | 60 | Best[owid/covid-19-data: Data on COVID-19 (coronavirus) cases, deaths, hospitalizations, tests • All countries • Updated daily by Our World in Data](https://github.com/owid/covid-19-data) 61 | 62 | 63 | ## Tests 64 | Best[owid/covid-19-data: Data on COVID-19 (coronavirus) cases, deaths, hospitalizations, tests • All countries • Updated daily by Our World in Data](https://github.com/owid/covid-19-data) 65 | 66 | ## Vaccination 67 | Best[owid/covid-19-data: Data on COVID-19 (coronavirus) cases, deaths, hospitalizations, tests • All countries • Updated daily by Our World in Data](https://github.com/owid/covid-19-data) 68 | 69 | [A global database of COVID-19 vaccinations | Nature Human Behaviour](https://www.nature.com/articles/s41562-021-01122-8) 70 | 71 | ## Hospitalization 72 | Best[owid/covid-19-data: Data on COVID-19 (coronavirus) cases, deaths, hospitalizations, tests • All countries • Updated daily by Our World in Data](https://github.com/owid/covid-19-data) 73 | 74 | # population 75 | [owid/covid-19-data: Data on COVID-19 (coronavirus) cases, deaths, hospitalizations, tests • All countries • Updated daily by Our World in Data](https://github.com/owid/covid-19-data) 76 | # Public Response 77 | 78 | Best[OxCGRT/covid-policy-tracker: Systematic dataset of Covid-19 policy, from Oxford University](https://github.com/OxCGRT/covid-policy-tracker) 79 | 80 | [CoronaNet Research Project](https://www.coronanet-project.org/) 81 | 82 | [lshtm-gis/WHO-PHSM: Repository for releases of the WHO PHSM dataset](https://github.com/lshtm-gis/WHO-PHSM) 83 | 84 | # Contact Matrix 85 | 86 | [mobs-lab/mixing-patterns: Inferring high-resolution human mixing patterns for disease modeling](https://github.com/mobs-lab/mixing-patterns) 87 | 88 | # Human Mobility, Flights 89 | 90 | [scikit-mobility/scikit-mobility: scikit-mobility: mobility analysis in Python](https://github.com/scikit-mobility/scikit-mobility) 91 | 92 | [descarteslabs/DL-COVID-19: Mobility changes in response to COVID-19, provided by Descartes Labs](https://github.com/descarteslabs/DL-COVID-19) 93 | 94 | [Data - COVID-19's Impact on Business - Research Sources & Guides at Stanford University Business School](https://libguides.stanford.edu/library/covid19#s-lg-box-23867902) 95 | 96 | [OpenSky COVID-19 Flight Dataset](https://opensky-network.org/community/blog/item/6-opensky-covid-19-flight-dataset) 97 | # Economic 98 | 99 | [Data - COVID-19's Impact on Business - Research Sources & Guides at Stanford University Business School](https://libguides.stanford.edu/library/covid19#s-lg-box-23867902) 100 | # Health Resources 101 | 102 | # Variants 103 | Best[CoVariants](https://covariants.org/) 104 | 105 | Best[GISAID - Initiative](https://www.gisaid.org/) 106 | 107 | 108 | # Behaviour Changes, Social Media 109 | 110 | Best[YouGov-Data/covid-19-tracker: This is the data repository for the Imperial College London YouGov Covid 19 Behaviour Tracker Data Hub.](https://github.com/YouGov-Data/covid-19-tracker) 111 | 112 | [echen102/COVID-19-TweetIDs: The repository contains an ongoing collection of tweets IDs associated with the novel coronavirus COVID-19 (SARS-CoV-2), which commenced on January 28, 2020.](https://github.com/echen102/COVID-19-TweetIDs) 113 | 114 | # Paper Datasets 115 | Best[COVID-19 Open Research Dataset Challenge (CORD-19) | Kaggle](https://www.kaggle.com/allen-institute-for-ai/CORD-19-research-challenge) 116 | 117 | [Global research on coronavirus disease (COVID-19)](https://www.who.int/emergencies/diseases/novel-coronavirus-2019/global-research-on-novel-coronavirus-2019-ncov) 118 | 119 | [CORD-19 | Semantic Scholar](https://www.semanticscholar.org/cord19) 120 | 121 | 122 | # Wastewater 123 | 124 | [COVID-19 Wastewater Epidemiology SARS-CoV-2 | Covid19wbec.org](https://www.covid19wbec.org/) 125 | 126 | [ColoSSoS - Collaboration on Sewage Surveillance of SARS-CoV-2 | WaterRA](https://www.waterra.com.au/project-details/264) 127 | 128 | Good[Search - Wastewater SPHERE](https://sphere.waterpathogens.org/search) 129 | 130 | [NORMAN Database System - SARS-CoV-2](https://www.norman-network.com/nds/sars_cov_2/) 131 | 132 | Canada[Big-Life-Lab/PHES-ODM: Metadata and code to support covid-19 wastewater surveillance and open science.](https://github.com/Big-Life-Lab/PHES-ODM) 133 | 134 | 135 | 136 | # Map File 137 | 138 | [GADM](https://gadm.org/index.html) 139 | 140 | [tmcw/awesome-geojson: GeoJSON utilities that will make your life easier.](https://github.com/tmcw/awesome-geojson) 141 | 142 | 中国地图(配合echarts4r) 143 | 144 | [lyhmyd1211/GeoMapData_CN: China province/city/country geoJSON data](https://github.com/lyhmyd1211/GeoMapData_CN) 145 | 146 | [yezongyang/china-geojson: 最新中国地图json文件，可用d3开发中国地图](https://github.com/yezongyang/china-geojson) 147 | 148 | [阿里云 DataV - 数据可视化平台](http://datav.aliyun.com/portal/school/atlas/area_selector) 149 | 150 | 151 | 152 | # CDCFluView 153 | 154 | [CRAN - Package cdcfluview](https://cran.r-project.org/web/packages/cdcfluview/index.html) 155 | -------------------------------------------------------------------------------- /docs/DataSources/JuliaDatasets.md: -------------------------------------------------------------------------------- 1 | --- 2 | sidebar_position: 4 3 | --- 4 | 5 | [MLDatasets · JuliaHub](https://juliahub.com/ui/Packages/MLDatasets/9CUQK/0.5.13) 6 | 7 | [JuliaStats/RDatasets.jl: Julia package for loading many of the data sets available in R](https://github.com/JuliaStats/RDatasets.jl) 8 | 9 | 10 | [simonschoelly/GraphDatasets.jl: A package for downloading and working with graph datasets](https://github.com/simonschoelly/GraphDatasets.jl) 11 | 12 | [queryverse/VegaDatasets.jl: Julia package for loading the standard Vega data sets](https://github.com/queryverse/VegaDatasets.jl) -------------------------------------------------------------------------------- /docs/DataSources/MLData.md: -------------------------------------------------------------------------------- 1 | --- 2 | sidebar_position: 3 3 | --- 4 | 5 | [这些超全的SCI论文数据网站来了！！推荐收藏~~](https://mp.weixin.qq.com/s/_hwCEm1zdpGDltVd0DWTzQ) 6 | 7 | 8 | [awesomedata/awesome-public-datasets: A topic-centric list of HQ open datasets.](https://github.com/awesomedata/awesome-public-datasets) 9 | 10 | [awesomedata/awesome-public-datasets: A topic-centric list of HQ open datasets.](https://github.com/awesomedata/awesome-public-datasets) 11 | 12 | [huggingface/datasets: 🤗 The largest hub of ready-to-use datasets for ML models with fast, easy-to-use and efficient data manipulation tools](https://github.com/huggingface/datasets) 13 | 14 | [Hugging Face – The AI community building the future.](https://huggingface.co/datasets) 15 | 16 | 17 | [tensorflow/datasets: TFDS is a collection of datasets ready to use with TensorFlow, Jax, ...](https://github.com/tensorflow/datasets) 18 | 19 | [TensorFlow Datasets](https://www.tensorflow.org/datasets/catalog/overview#all_datasets) 20 | 21 | 22 | 23 | Stanford Graph Datasets 24 | [Stanford Large Network Dataset Collection](https://snap.stanford.edu/data/) 25 | -------------------------------------------------------------------------------- /docs/DataSources/Rdatasets.md: -------------------------------------------------------------------------------- 1 | --- 2 | sidebar_position: 5 3 | --- 4 | 5 | [List of Free Datasets | R Statistical Programming Language | r-directory](https://r-dir.com/reference/datasets.html) 6 | 7 | 内置数据集[vincentarelbundock/Rdatasets: A collection of datasets originally distributed in R packages](https://github.com/vincentarelbundock/Rdatasets) 8 | 9 | # Epidemic 10 | [mponce0/covid19.analytics: R package to obtain and analyze live data from the nCOVID19 coronavirus](https://github.com/mponce0/covid19.analytics) 11 | 12 | [covid19datahub/COVID19: A worldwide epidemiological database for COVID-19 at fine-grained spatial resolution](https://github.com/covid19datahub/COVID19) -------------------------------------------------------------------------------- /docs/DataSources/_category_.json: -------------------------------------------------------------------------------- 1 | { 2 | "label": "Data Sources Collections", 3 | "position": 2 4 | } 5 | -------------------------------------------------------------------------------- /docs/DataVisualization/_category_.json: -------------------------------------------------------------------------------- 1 | { 2 | "label": "Data Visualization", 3 | "position": 4 4 | } 5 | -------------------------------------------------------------------------------- /docs/DataVisualization/dataviz.md: -------------------------------------------------------------------------------- 1 | --- 2 | sidebar_position: 1 3 | --- 4 | 5 | # 1. Basics of data visulization, Grammar of graph 6 | 7 | Book: The Grammar of Graphics 8 | 9 | Study[Welcome | ggplot2](https://ggplot2-book.org/) 10 | 11 | Study[数据可视化基本套路总结](https://mp.weixin.qq.com/s/LsYD6HpikatIkfjols1U5w) 12 | 13 | Study[Fundamentals of Data Visualization](https://clauswilke.com/dataviz/) 14 | 15 | Reviews[Visualization Universe - The Most Searched for Visualization Types, Tools, and Books](http://visualizationuniverse.com/) 16 | 17 | [20条超全建议！让你轻松搞定高质量数据可视化~~](https://mp.weixin.qq.com/s/3JZKmaJbKq-UzNtrnZlkQA) 18 | 19 | [可视化图表使用避坑指南 (上篇)](https://mp.weixin.qq.com/s/rgeQpMsUlcMcoY2_C3eBQA) 20 | 21 | ## Layouts 22 | [(文末送书)绘图技巧 | 一行代码实现多图组合和风格主题设置](https://mp.weixin.qq.com/s/eaM_mXM8uJAMNNvyBAbIOg) 23 | 24 | 25 | ## Tools 26 | Tool[有哪些值得推荐的数据可视化工具？ - 知乎](https://www.zhihu.com/question/19929609) 27 | 28 | Tool[ImageGP | 易汉博生物信息在线作图](http://www.ehbio.com/ImageGP/) 29 | # 2. How to choose charts types? (Tool) 30 | [From data to Viz | Find the graphic you need](https://www.data-to-viz.com/) 31 | 32 | [The Data Visualisation Catalogue](https://datavizcatalogue.com/) 33 | 34 | [Elegant scientific graphs: Learning from examples | rmf](https://www.royfrancis.com/elegant-scientific-graphs-learning-from-examples/) 35 | ## 2.1. Baiscs 36 | [The Data Visualisation Catalogue](https://datavizcatalogue.com/index.html) 37 | 38 | [Chart choosing - Chart.Guide](https://chart.guide/charts/chart-choosing/) 39 | 40 | [Data Viz Project | Collection of data visualizations to get inspired and finding the right type.](https://datavizproject.com/#) 41 | 42 | [From data to Viz | Find the graphic you need](https://www.data-to-viz.com/) 43 | 44 | [图之典](http://tuzhidian.com/) 45 | 46 | ## 2.2. Advanced 47 | 48 | [Gallery | Charticulator](https://charticulator.com/gallery/index.html) 49 | 50 | [Create Infographics, Presentations & Reports Online | Piktochart](https://piktochart.com/) 51 | 52 | [Gallery | RAWGraphs](https://rawgraphs.io/gallery) 53 | 54 | [PlotDB: Share Visualizations with Everyone](https://plotdb.com/chart/explore/) 55 | 56 | [Information is Beautiful Awards](https://www.informationisbeautifulawards.com/) 57 | 58 | ## 2.3. Github Repos 59 | [holtzy (Holtz Yan)](https://github.com/holtzy) 60 | 61 | ## 2.4. Special type of graphs. 62 | 63 | [常用60类图表使用场景、制作工具推荐！](https://mp.weixin.qq.com/s/212G-ObML0wEhIlp4zGiMA) 64 | 65 | 66 | [https://mp.weixin.qq.com/s/h5IcSW1a7P6H0iu2VFdlAA](https://mp.weixin.qq.com/s/h5IcSW1a7P6H0iu2VFdlAA) 67 | 68 | 69 | ### Map 70 | (done)[11个S级工具，满足「地理空间数据可视化」的一切幻想](https://mp.weixin.qq.com/s/09yV8B3PvVUz-vBqTEswdg) 71 | 72 | [爱了！！这种小清新地图一眼就上头，赶快学习一下吧~](https://mp.weixin.qq.com/s/EWqOsNJRONzx5bCButs6cw) 73 | 74 | [绘图技巧 | 绘制带饼图的地图可视化技巧分享](https://mp.weixin.qq.com/s/5Rqt6IF02bo-i4V0uRM62w) 75 | 76 | [Python 空间绘图 - 房价气泡图绘制](https://mp.weixin.qq.com/s/yLoDsQaMoLtxTY-HlUZ_Fw) 77 | 78 | [绘图技巧 | 第七次全国人口普查数据还能这么玩！？技巧都在这了](https://mp.weixin.qq.com/s/Fjs-sncZfX4JtcPwoZ6fhA) 79 | 80 | [绘图技巧 | 双变量映射地图可视化绘制方法](https://mp.weixin.qq.com/s/E_cToXEc4b_UAjUWnYi4ZQ) 81 | 82 | R: 83 | cartography 84 | ggmap 85 | 86 | 87 | Python 88 | 89 | [Jupyter Notebook - kepler.gl](https://docs.kepler.gl/docs/keplergl-jupyter) 90 | 91 | (good)[visgl/deck.gl: WebGL2 powered visualization framework](https://github.com/visgl/deck.gl) 92 | 93 | (good)[giswqs/leafmap: A Python package for interactive mapping and geospatial analysis with minimal coding in a Jupyter environment](https://github.com/giswqs/leafmap) 94 | 95 | (pyecharts) 96 | ### Chord Graph 97 | [绘图技巧 | 和弦图(Chord diagram)绘制方法汇总](https://mp.weixin.qq.com/s/-gVz8zuDWcLrZ5EqVlQp5w) 98 | 99 | R: 100 | 101 | circlize 102 | 103 | Python: 104 | 105 | Chord 106 | ### Forest Plot 107 | [真的！森林图(Forest Plot)全部绘制技巧都在这了](https://mp.weixin.qq.com/s?__biz=Mzg3MDY4ODI3MQ==&mid=2247498141&idx=1&sn=168bbecb73b1fd2ab190569dc3523b0b&source=41#wechat_redirect) 108 | 109 | R: forestplot,ggforestplot 110 | 111 | Python: zepid 112 | 113 | ### Venn 114 | 115 | [Venn Diagram cookbook in R](https://venn.bio-spring.top/) 116 | 117 | 118 | (done)[Evenn](http://www.ehbio.com/test/venn/#/) 119 | 120 | (done)[绘图技巧 | 我总结了韦恩图(Venn Diagram)绘制方法(R+Python)](https://mp.weixin.qq.com/s/FHvFbrxmSkvdJmcaW3ov7g) 121 | 122 | ggVennDiagram 123 | 124 | VennDiagram 125 | 126 | 127 | 128 | matplotlib-venn 129 | 130 | ### 环形进化树 131 | ggtree 132 | 133 | ggtreeextra 134 | ### 甘特图 135 | [原来甘特图(Gantt Chart)还可以这么美！赶快掌握下绘制方法吧~~](https://mp.weixin.qq.com/s/FoTk0LlnaZHQx4LMIqloZw) 136 | 137 | ganttrify 138 | 139 | ### 漏斗图 140 | [漏斗图(Funnel Plots)这下介绍的够全面了吧~](https://mp.weixin.qq.com/s/er0J6adIsAo3808cdDb_kQ) 141 | 142 | ### Table 143 | [全了！！表格可视化制作技巧大汇总~~](https://mp.weixin.qq.com/s/2mYkyyYEWljq7Vzp8aljAQ) 144 | 145 | 146 | R-DT 147 | R-gt 148 | R-sjPlot 149 | R-ggpubar 150 | R-ggpmisc 151 | 152 | R-gtsummary 153 | R-kableExtra 154 | R-formattable 155 | R-reactable 156 | R-flextable 157 | ### 泰勒图 158 | [超干货 | 泰勒图(Taylor diagram)绘制方法大汇总](https://mp.weixin.qq.com/s/DD0i2C1otfpo_NoJgsEAuw) 159 | 160 | ### 置信椭圆(confidence ellipses) 161 | 162 | [绘图技巧 | 散点置信椭圆的可视化绘制技巧(文末送书)](https://mp.weixin.qq.com/s/uAVwNZs1diPMBDSuJQukLQ) 163 | 164 | ### 添加表情符号(emoji)可视化绘制 165 | [绘图技巧 | 这种“符号”可视化图表绘制技巧分享](https://mp.weixin.qq.com/s/OCiK4PY3nSxjHLLxRwBOmw) 166 | 167 | ### 相关性矩阵图 168 | [绘图技巧 | 相关性矩阵图绘制方法汇总](https://mp.weixin.qq.com/s/pLWCTq-mWuSkHIobykZrmQ) 169 | 170 | 171 | [这下全了吧！！相关性矩阵图绘制方法大汇总~~](https://mp.weixin.qq.com/s/ahQQujnrWayCD49DhYxtjg) 172 | 173 | ### 子弹图 174 | [子弹图(Bullet chart)绘制很难吗？绘制技巧整理送你~~](https://mp.weixin.qq.com/s/TvDXsVvC3_Auvwo7Zu_0ZQ) 175 | 176 | ### 矩阵图 Matrix Graph 177 | scatterPlotMatrix 178 | 179 | [scatterPlotMatrix包：一行命令轻松搞定交互式散点图矩阵](https://mp.weixin.qq.com/s/sPq8n60WqynV-qrmbGVe7Q) 180 | 181 | ### 竞赛图 182 | [JackMcKew/pandas_alive: Create stunning, animated visualisations with Pandas & Matplotlib as easy as calling `df.plot_animated()`](https://github.com/JackMcKew/pandas_alive) 183 | 184 | [ianhi/mpl-interactions: Sliders to control matplotlib and other interactive goodies. Works in any interactive backend and even uses ipywidgets when in a Jupyter notebook](https://github.com/ianhi/mpl-interactions) 185 | 186 | ### Raincloud plots 187 | 188 | [这种显示多种统计结果的云雨图(Raincloud plots)怎么绘制？？](https://mp.weixin.qq.com/s/M0OuMC8MR_wkLiXS6RkzLw) 189 | 190 | 191 | ### 树图 192 | treemap 193 | 194 | ### 树形图 195 | ggraph 196 | 197 | ### 楔形图(Wedge Plots) 198 | [connectedblue/pdext_collection](https://github.com/connectedblue/pdext_collection) 199 | ### Missing Values 200 | # 3. R 201 | [erikgahner/awesome-ggplot2: A curated list of awesome ggplot2 tutorials, packages etc.](https://github.com/erikgahner/awesome-ggplot2) 202 | 203 | ## 3.1. Books 204 | ### Basics 205 | Study[R Graphics Cookbook, 2nd edition](https://r-graphics.org/) 206 | 207 | ### Templates 208 | Study[R-Gallery-Book](https://www.kyle-w-brown.com/R-Gallery/) 209 | 210 | [The R Graph Gallery – Help and inspiration for R charts](https://www.r-graph-gallery.com/) 211 | 212 | [R CHARTS | A collection of charts and graphs made with the R programming language](https://r-charts.com/) 213 | 214 | ## 3.2. Packages 215 | 216 | [erikgahner/awesome-ggplot2: A curated list of awesome ggplot2 tutorials, packages etc.](https://github.com/erikgahner/awesome-ggplot2) 217 | 218 | 219 | [50个ggplot2可视化案例](https://mp.weixin.qq.com/s/YMy04iB8ZySHQ3ptJCzyOA) 220 | 221 | [https://exts.ggplot2.tidyverse.org/gallery/](https://exts.ggplot2.tidyverse.org/gallery/) 222 | 223 | ggvis:interactive 224 | 225 | circlize: 226 | 227 | Graphpad Prism风格学术图表[https://mp.weixin.qq.com/s/nBU4Cmek5O5O7Ut7zNkwuw](https://mp.weixin.qq.com/s/nBU4Cmek5O5O7Ut7zNkwuw) 228 | 229 | ggpattern包和一般的ggplot2拓展包不同，其为了更好的实现灵活的底纹填充功能，独立编写了强大的 ggplot2的geoms功能，当然，其语法还是我么熟悉的那个味道。[coolbutuseless/ggpattern: ggplot geoms with pattern fills](https://github.com/coolbutuseless/ggpattern) 230 | 231 | R-mapsf，R-cartography[爱了！！这种小清新地图一眼就上头，赶快学习一下吧~](https://mp.weixin.qq.com/s/EWqOsNJRONzx5bCButs6cw) 232 | 233 | scatterPlotMatrix 234 | 235 | 236 | -ggshadow加阴影 237 | 238 | # 4. Python 239 | 重点学习 240 | 241 | matplotlib+seaborn+ProPlot+mpl_interactions+pandas-alive 242 | 243 | Plotly+Plotly-express+Dash 244 | 245 | Pyecharts 246 | 247 | leafmap+deck.gl+kepler.gl 248 | 249 | [All Tools — PyViz 0.0.1 documentation](https://pyviz.org/tools.html) 250 | ## 4.1. Books 251 | 252 | ## 4.2. Templates 253 | [Python Graph Gallery](https://www.python-graph-gallery.com/) 254 | 255 | ## 4.3. Packages 256 | ==matplotlib==基础图形 257 | 258 | (good)[The basics — ProPlot documentation](https://proplot.readthedocs.io/en/stable/basics.html) 259 | 260 | (good)matplotlib+seaborn 261 | 262 | [nschloe/matplotx: More styles and useful extensions for Matplotlib](https://github.com/nschloe/matplotx) 263 | 264 | Basemap+cartopy: [Introduction — cartopy 0.19.0rc2.dev8+gd251b2f documentation](https://scitools.org.uk/cartopy/docs/latest/) 265 | 266 | ==plotly==交互性中高级图形(good) 267 | 268 | plotly [Plotly Python Graphing Library | Python | Plotly](https://plotly.com/python/) 可画地图 类似D3.js 269 | 270 | Plotly_express [Plotly Express | Python | Plotly](https://plotly.com/python/plotly-express/) 271 | 272 | Dash: web 可视化[plotly/dash: Analytical Web Apps for Python, R, Julia, and Jupyter. No JavaScript Required.](https://github.com/plotly/dash) 273 | 274 | ==ggplot2== 275 | plotnine: 类似ggplot2 [A Grammar of Graphics for Python — plotnine 0.8.0 documentation](https://plotnine.readthedocs.io/en/stable/) 276 | 277 | ==pyecharts==高级图形(good) 278 | pyecharts[简介 - pyecharts - A Python Echarts Plotting Library built with love.](https://pyecharts.org/#/zh-cn/intro)可画地图 279 | 280 | [Document](https://gallery.pyecharts.org/#/) 281 | 282 | ==Vega== 283 | [altair-viz/altair: Declarative statistical visualization library for Python](https://github.com/altair-viz/altair) 284 | 285 | 286 | 287 | [visgl/deck.gl: WebGL2 powered visualization framework](https://github.com/visgl/deck.gl) 288 | 289 | [giswqs/leafmap: A Python package for interactive mapping and geospatial analysis with minimal coding in a Jupyter environment](https://github.com/giswqs/leafmap) 290 | 291 | 292 | 293 | (good)Missingno 显示缺失数据[ResidentMario/missingno: Missing data visualization module for Python.](https://github.com/ResidentMario/missingno) 294 | 295 | (done)Gleam: 的灵感来自 R 的Shiny包。它允许你仅使用 Python 代码将图形转换为出色的 Web 应用程序。这对不了解 HTML 和 CSS 的人很有帮助。它不是真正的可视化库，而是与任何可视化库一起使用。 296 | 297 | Bokeh: 交互数据库 298 | 299 | 300 | 地图库合集：[Python 如何画出漂亮的地图？ - 知乎](https://www.zhihu.com/question/33783546) 301 | 302 | [11个S级工具，满足「地理空间数据可视化」的一切幻想](https://mp.weixin.qq.com/s/09yV8B3PvVUz-vBqTEswdg) 303 | 304 | geopandas： 地图[GeoPandas 0.9.0 — GeoPandas 0.9.0 documentation](https://geopandas.org/) 305 | 306 | folium [Folium — Folium 0.12.1 documentation](https://python-visualization.github.io/folium/) 307 | 308 | [Jupyter Notebook - kepler.gl](https://docs.kepler.gl/docs/keplergl-jupyter) 309 | 310 | GCmap 航线图 311 | 312 | bqplot交互式图形 313 | [bqplot/bqplot: Plotting library for IPython/Jupyter notebooks](https://github.com/bqplot/bqplot) 314 | 315 | # 5. Julia 316 | 317 | [lazarusA/BeautifulMakie: https://lazarusa.github.io/BeautifulMakie/](https://github.com/lazarusA/BeautifulMakie) 318 | 319 | 320 | 321 | [eirikbrandsaas/DuBoisPlots.jl: A package that replicates some of the unique visualizations by W. E. B. DuBois](https://github.com/eirikbrandsaas/DuBoisPlots.jl) 322 | 323 | 地图 324 | 325 | [JuliaEarth](https://github.com/JuliaEarth) 326 | 327 | [JuliaGeometry](https://github.com/JuliaGeometry) 328 | 329 | # 6. Handling Colors, Color palettes 330 | Best[配色案例,网页配色,设计配色,配色图表,配色卡,SDC优设网配色工具](https://color.uisdc.com/pick.html) 331 | 332 | 333 | [R语言ggplot2作图配色相关R包备选](https://mp.weixin.qq.com/s/nZ3z4aERLbNTGIno955VJw) 334 | 335 | [ColorDrop - New colors](https://colordrop.io/) 336 | 337 | [Palettes | Flat UI Colors 🎨 280 handpicked colors ready for COPY & PASTE](https://flatuicolors.com/) 338 | 339 | [艳红 - 中国色 - 中国传统颜色](http://zhongguose.com/#yanhong) 340 | 341 | 342 | [R Color Palettes [497 continuous and discrete palettes] | R CHARTS](https://r-charts.com/color-palettes/) 343 | 344 | [HTML Color Codes](https://htmlcolorcodes.com/) 345 | 346 | [颜色系(color palette)是什么？一文带你掌握全部用法！](https://mp.weixin.qq.com/s/QtdxVR7Gpal8KjNXSgSu6g) 347 | 348 | [默认配色辣眼睛？！那是你没发现这些宝藏学术颜色包(Colormaps)~~](https://mp.weixin.qq.com/s/sWGlKscW__dOjugjHOtxAw) 349 | # 6.1. R 350 | paletteer 351 | 352 | ggsci 353 | 354 | wesanderson 355 | 356 | viridis 357 | 358 | RColorBrewer 359 | 360 | [BlakeRMills/MetBrewer: Color palette package in R inspired by works at the Metropolitan Museum of Art in New York](https://github.com/BlakeRMills/MetBrewer/tree/main) 361 | 362 | [R语言ggplot2作图配色相关R包备选](https://mp.weixin.qq.com/s/nZ3z4aERLbNTGIno955VJw) 363 | 364 | # 6.2. Python 365 | 366 | colormap[Choosing Colormaps in Matplotlib — Matplotlib 3.4.2 documentation](https://matplotlib.org/stable/tutorials/colors/colormaps.html) 367 | 368 | Palettable[Palettable](https://jiffyclub.github.io/palettable/#matplotlib-discrete-colormap) 369 | ## 6.3. SCI 370 | 371 | R:ggsci 372 | 373 | Python: SciencePlots 374 | 375 | 376 | ## 6.4. Grab color from plots 377 | Python: **Haishoku**从图片提取配色方案 378 | 379 | R: [zumbov2/colorfindr: Extracts colors from various image types, plots treemaps and 3D scatterplots of color compositions, create color palettes.](https://github.com/zumbov2/colorfindr) 380 | 381 | 382 | # 7. Web 383 | 384 | [streamlit/streamlit: Streamlit — The fastest way to build data apps in Python](https://github.com/streamlit/streamlit) 385 | 386 | [jrieke/best-of-streamlit: 🏆 A ranked gallery of awesome streamlit apps built by the community](https://github.com/jrieke/best-of-streamlit) 387 | -------------------------------------------------------------------------------- /docs/EpiRepos/EpidemicRepos.md: -------------------------------------------------------------------------------- 1 | --- 2 | sidebar_position: 1 3 | --- 4 | 5 | [reichlab/covid19-forecast-hub: Projections of COVID-19, in standardized format](https://github.com/reichlab/covid19-forecast-hub) 6 | 7 | [midas-network/COVID-19: 2019 novel coronavirus repository](https://github.com/midas-network/COVID-19) 8 | 9 | [Priesemann-Group/covid19_inference_forecast](https://github.com/Priesemann-Group/covid19_inference_forecast) 10 | 11 | Julia: [epirecipes/sir-julia: Various implementations of the classical SIR model in Julia](https://github.com/epirecipes/sir-julia) 12 | 13 | Nice[Tools](https://canmod.net/tools) 14 | 15 | [Epiforecasts](https://github.com/epiforecasts) 16 | 17 | 18 | R: 19 | 20 | Review [cran-task-views/Epidemiology: CRAN Task View: Epidemiology](https://github.com/cran-task-views/Epidemiology) 21 | 22 | [EpiModel/EpiModel: Mathematical Modeling of Infectious Disease Dynamics](https://github.com/EpiModel/EpiModel) 23 | 24 | # Epi Group 25 | 26 | [Epiforecasts](https://github.com/epiforecasts) 27 | 28 | [MRC Centre for Global Infectious Disease Analysis](https://github.com/mrc-ide) 29 | 30 | [COVID-19 Wastewater Epidemiology SARS-CoV-2 | Covid19wbec.org](https://www.covid19wbec.org/) 31 | 32 | [Institute for Disease Modeling](https://github.com/InstituteforDiseaseModeling) 33 | 34 | [publications - MOBS Lab](https://www.mobs-lab.org/publications.html) 35 | 36 | [R Epidemics Consortium](https://github.com/reconhub) 37 | 38 | [Oxford Big Data Institute: Pathogen Dynamics Group](https://github.com/BDI-pathogens) 39 | 40 | 41 | [Mac-Theobio](https://github.com/mac-theobio) 42 | # Compartment Models 43 | 44 | Python: 45 | 46 | [uiuc-covid19-modeling/pydemic: a python driver for epidemic modeling and inference](https://github.com/uiuc-covid19-modeling/pydemic) 47 | 48 | [I-Bouros/multi-epi-model-cross-analysis: A collection of multiple epidemiological models used in the modelling of cases observed during the COVID-19 pandemic.](https://github.com/I-Bouros/multi-epi-model-cross-analysis) 49 | 50 | Julia 51 | 52 | Include data cleaning[berndblasius/Covid19: Wang et al SEIR-like model for 2019/20 Covid-19 outbreak](https://github.com/berndblasius/Covid19) 53 | 54 | [jtmatamalas/MMCAcovid19.jl: Microscopic Markov Chain Approach to model the spreading of COVID-19](https://github.com/jtmatamalas/MMCAcovid19.jl) 55 | 56 | [affans/covid-19-lancetid: A clone of Peter Jentsch's covid19 model](https://github.com/affans/covid-19-lancetid) 57 | 58 | [uncomfyhalomacro/covid19_mindanao_seir_model: COVID19 SEIR model in Mindanao, Philippines using Julia](https://github.com/uncomfyhalomacro/covid19_mindanao_seir_model) 59 | 60 | [schrimpf/CovidSEIR.jl: Bayesian estimation of a SEIR model or Corona Virus](https://github.com/schrimpf/CovidSEIR.jl) 61 | 62 | R: 63 | 64 | [ImperialCollegeLondon/epidemia: epidemia package](https://github.com/ImperialCollegeLondon/epidemia) 65 | 66 | # Agent based modelling 67 | 68 | Julia: 69 | [RenuSolanki/EasyABM.jl: An easy to use and performant Julia framework for agent based modeling.](https://github.com/RenuSolanki/EasyABM.jl) 70 | 71 | [BDI-pathogens/OpenABM-Covid19: OpenABM-Covid19: an agent-based model for modelling the spread of SARS-CoV-2 (coronavirus) and control interventions for the Covid-19 epidemic](https://github.com/BDI-pathogens/OpenABM-Covid19) 72 | 73 | [jangevaare/Pathogen.jl: Simulation, visualization, and inference tools for modelling the spread of infectious diseases with Julia](https://github.com/jangevaare/Pathogen.jl) 74 | 75 | [affans/covid19abm.jl: Agent Based Model for COVID 19 transmission dynamics](https://github.com/affans/covid19abm.jl) 76 | 77 | [thomasvilches/vac_covid_ontario](https://github.com/thomasvilches/vac_covid_ontario) 78 | 79 | good[ETACE/ace_covid19: An integrated agent-based economic and epidemiological model of the COVID-19 pandemic.](https://github.com/ETACE/ace_covid19) 80 | 81 | [thomasvilches/multiple_strains](https://github.com/thomasvilches/multiple_strains) 82 | 83 | [IgorDouven/COVID.jl: Julia code for agent-based model of COVID-19 outbreaks and possible intervention strategies](https://github.com/IgorDouven/COVID.jl) 84 | 85 | [werzum/COVID19-ABM: An Agent-Based model of the COVID-19 spread in Germany](https://github.com/werzum/COVID19-ABM) 86 | 87 | [kkmann/cov19sim: agent-based simulation of covid-19 for small populations](https://github.com/kkmann/cov19sim) 88 | 89 | Python: 90 | 91 | Good[InstituteforDiseaseModeling/covasim: COVID-19 Agent-based Simulator (Covasim): a model for exploring coronavirus dynamics and interventions](https://github.com/InstituteforDiseaseModeling/covasim) 92 | 93 | [petroniocandido/COVID19_AgentBasedSimulation: COVID-ABS: An Agent-Based Model of COVID-19 Epidemic to Simulate Health and Economic Effects of Social Distancing Interventions](https://github.com/petroniocandido/COVID19_AgentBasedSimulation) 94 | 95 | [BDI-pathogens/OpenABM-Covid19: OpenABM-Covid19: an agent-based model for modelling the spread of SARS-CoV-2 (coronavirus) and control interventions for the Covid-19 epidemic](https://github.com/BDI-pathogens/OpenABM-Covid19) 96 | 97 | [worldbank/covid19-agent-based-model: This repository contains the Python implementation of the agent-based model used to model the spread of COVID-19.](https://github.com/worldbank/covid19-agent-based-model) 98 | 99 | [maplerainresearch/covid19-sim-mesa: COVID-19 infection multi-agent simulation using Mesa framework](https://github.com/maplerainresearch/covid19-sim-mesa) 100 | 101 | Matlab: 102 | 103 | # Network Model 104 | 105 | Julia: 106 | 107 | [bridgewalker/Fastnet.jl](https://github.com/bridgewalker/Fastnet.jl) 108 | 109 | [csimal/NetworkEpidemics.jl: A Small package for simulating epidemic spreading on networks, including a generic Gillespie algorithm and Mean Field Approximations](https://github.com/csimal/NetworkEpidemics.jl) 110 | 111 | [EdMHill/covid19_uni_network_model](https://github.com/EdMHill/covid19_uni_network_model) 112 | 113 | [EdMHill/covid19_worker_network_model](https://github.com/EdMHill/covid19_worker_network_model) 114 | 115 | [social_agent_based_modelling/scripts at 606f5ec1ed51781926b7891f67f87ac4404c7d57 · lquante/social_agent_based_modelling](https://github.com/lquante/social_agent_based_modelling/tree/606f5ec1ed51781926b7891f67f87ac4404c7d57/scripts) 116 | 117 | [csimal/temporal-networks](https://github.com/csimal/temporal-networks) 118 | 119 | Python: 120 | 121 | [ryansmcgee/seirsplus: Models of SEIRS epidemic dynamics with extensions, including network-structured populations, testing, contact tracing, and social distancing.](https://github.com/ryansmcgee/seirsplus) 122 | 123 | [alsnhll/COVID19NetworkSimulations: Public repository for stochastic network simulations of COVID19 dynamics](https://github.com/alsnhll/COVID19NetworkSimulations) 124 | 125 | [COVID-IWG/epimargin: networked, stochastic SIRD epidemiological model with Bayesian parameter estimation and policy scenario comparison tools](https://github.com/COVID-IWG/epimargin) 126 | 127 | [springer-math/Mathematics-of-Epidemics-on-Networks: Source code accompanying 'Mathematics of Epidemics on Networks' by Kiss, Miller, and Simon http://www.springer.com/us/book/9783319508047 . Documentation for the software package is at https://epidemicsonnetworks.readthedocs.io/en/latest/](https://github.com/springer-math/Mathematics-of-Epidemics-on-Networks) 128 | 129 | [simoninireland/epydemic: Epidemic processes on networks in Python](https://github.com/simoninireland/epydemic) 130 | 131 | R: 132 | 133 | [EpiModel/EpiModel-Gallery: Gallery of Network-Based Epidemic Model Templates for EpiModel](https://github.com/EpiModel/EpiModel-Gallery) 134 | 135 | # Partial Differential Model 136 | 137 | Julia: 138 | 139 | [R-DjidjouDemasse/Age-structured-model-COVID19-Julia-code: Age-structured non-pharmaceutical interventions for optimal control of COVID-19 epidemic by Q. Richard, S. Alizon, M. Choisy, M.T. Sofonea, R. Djidjou-Demasse](https://github.com/R-DjidjouDemasse/Age-structured-model-COVID19-Julia-code) 140 | 141 | # Epiforecasting 142 | 143 | [The Reich Lab at UMass-Amherst](https://github.com/reichlab) 144 | 145 | [reichlab/covid19-forecast-hub: Projections of COVID-19, in standardized format](https://github.com/reichlab/covid19-forecast-hub) 146 | 147 | # Social Contact Matrix 148 | 149 | [sbfnk/epimixr: Epidemiological analysis using social mixing matrices](https://github.com/sbfnk/epimixr) 150 | 151 | [R Epidemics Consortium](https://github.com/reconhub) 152 | 153 | # Rt 154 | 155 | [ResearchLuxembourg/restimator: Real time estimation of epidemic Effective Reproduction Number for Luxembourg](https://github.com/ResearchLuxembourg/restimator) 156 | 157 | [epiforecasts/covid-rt-estimates: National and subnational estimates of the time-varying reproduction number for Covid-19](https://github.com/epiforecasts/covid-rt-estimates) 158 | 159 | [Estimate Real-Time Case Counts and Time-Varying Epidemiological Parameters • EpiNow2](https://epiforecasts.io/EpiNow2/) 160 | 161 | [mrc-ide/EpiEstim: A tool to estimate time varying instantaneous reproduction number during epidemics](https://github.com/mrc-ide/EpiEstim) 162 | 163 | [lo-hfk/epyestim: Python package to estimate the time-varying effective reproduction number of an epidemic from reported case numbers](https://github.com/lo-hfk/epyestim) 164 | 165 | 166 | [davidearn/epigrowthfit: Estimating Epidemic Growth Rates](https://github.com/davidearn/epigrowthfit) 167 | -------------------------------------------------------------------------------- /docs/EpiRepos/_category_.json: -------------------------------------------------------------------------------- 1 | { 2 | "label": "Mathematical Epidemiology Repos", 3 | "position": 5 4 | } 5 | -------------------------------------------------------------------------------- /docs/HowToResearch/Exhibition.md: -------------------------------------------------------------------------------- 1 | --- 2 | sidebar_position: 3 3 | --- 4 | 5 | # Static Web Tools 6 | 7 | [Quarto](https://quarto.org/) 8 | 9 | [JuliaDocs/Documenter.jl: A documentation generator for Julia.](https://github.com/JuliaDocs/Documenter.jl) 10 | 11 | [chriskiehl/Gooey: Turn (almost) any Python command line program into a full GUI application with one line](https://github.com/chriskiehl/Gooey) 12 | 13 | Latex: 14 | 15 | [Detexify LaTeX handwritten symbol recognition](http://detexify.kirelabs.org/classify.html) 16 | 17 | Display Julia Unicode in Latex 18 | 19 | [mossr/julia-mono-listings: LaTeX listings style for Julia and Unicode support for the JuliaMono font](https://github.com/mossr/julia-mono-listings) 20 | 21 | [wg030/jlcode: A latex package for displaying Julia code using the listings package. The package supports pdftex, luatex and xetex for compilation.](https://github.com/wg030/jlcode) 22 | 23 | [davibarreira/NotebookToLaTeX.jl: A Julia package for converting your Pluto and Jupyter Notebooks into beautiful Latex.](https://github.com/davibarreira/NotebookToLaTeX.jl) 24 | 25 | Web: 26 | 27 | [facebook/docusaurus: Easy to maintain open source documentation websites.](https://github.com/facebook/docusaurus) 28 | 29 | Hexo 30 | 31 | [Jekyll • Simple, blog-aware, static sites | Transform your plain text into static websites and blogs](https://jekyllrb.com/) 32 | 33 | [tlienart/Franklin.jl: (yet another) static site generator. Simple, customisable, fast, maths with KaTeX, code evaluation, optional pre-rendering, in Julia.](https://github.com/tlienart/Franklin.jl) 34 | 35 | [一个傻瓜式构建可视化 web的 Python 神器 -- streamlit](https://mp.weixin.qq.com/s/AxZPxQgLfJ6g8bhonTvKxA) 36 | 37 | [streamlit/streamlit: Streamlit — The fastest way to build data apps in Python](https://github.com/streamlit/streamlit) 38 | 39 | [gradio-app/gradio: Create UIs for your machine learning model in Python in 3 minutes](https://github.com/gradio-app/gradio) 40 | 41 | GitHub Profile Settings: 42 | 43 | [abhisheknaiidu/awesome-github-profile-readme: 😎 A curated list of awesome GitHub Profile READMEs 📝](https://github.com/abhisheknaiidu/awesome-github-profile-readme) 44 | 45 | [Shields.io: Quality metadata badges for open source projects](https://shields.io/) 46 | 47 | [ButterAndButterfly/GithubTools: 目标是创建会刷新的ReadMe首页！ 在这里，你可以得到Github star/fork总数图标, 项目star历史曲线，star数最多的前N个Repo信息...](https://github.com/ButterAndButterfly/GithubTools) 48 | 49 | 常用[anuraghazra/github-readme-stats: Dynamically generated stats for your github readmes](https://github.com/anuraghazra/github-readme-stats) 50 | 51 | 字体： 52 | [be5invis/Sarasa-Gothic: Sarasa Gothic / 更纱黑体 / 更紗黑體 / 更紗ゴシック / 사라사 고딕](https://github.com/be5invis/Sarasa-Gothic) 53 | 54 | [Nerd Fonts - Iconic font aggregator, glyphs/icons collection, & fonts patcher](https://www.nerdfonts.com/font-downloads) 55 | 56 | ## Terminal 57 | 58 | zsh 59 | 60 | ohmyzsh 61 | 62 | ohmyposh [Customize | Oh My Posh](https://ohmyposh.dev/docs/installation/customize) 63 | ## Personal Blogs 64 | 65 | Hexo 66 | 67 | ## Organization Documents 68 | 69 | Docusaurus 70 | [facebook/docusaurus: Easy to maintain open source documentation websites.](https://github.com/facebook/docusaurus) 71 | 72 | ## Data Visualization Web 73 | Streamlit 74 | 75 | Shinny App 76 | 77 | -------------------------------------------------------------------------------- /docs/HowToResearch/PaperWriting.md: -------------------------------------------------------------------------------- 1 | --- 2 | sidebar_position: 2 3 | --- 4 | All solutions to Latex [LaTeX 工作室](https://www.latexstudio.net/) 5 | 6 | Online Latex: [Your Projects - Overleaf, Online LaTeX Editor](https://www.overleaf.com/project) 7 | 8 | Next generation tool[Quarto](https://quarto.org/) 9 | 10 | [csekri/tkzgeom: GUI tool for TikZ figure production](https://github.com/csekri/tkzgeom) 11 | 12 | R Bookdown and Markdown 13 | 14 | [Linggle - Language Reference Search Engines - NLPLab](https://linggle.com/) 15 | 16 | [Academic Phrasebank | The University of Manchester](https://www.phrasebank.manchester.ac.uk/) 17 | 18 | [Paraphrasing Tool | QuillBot AI](https://quillbot.com/?utm_source=Google&utm_medium=Search&utm_campaign=Paraphrase_Premium&gclid=CjwKCAjwzt6LBhBeEiwAbPGOgVLg_Gi-6pvX4WNTspTbJToGX6ybPiaf1nfdwcmCAJWjck2y4psrTxoCWSYQAvD_BwE) 19 | -------------------------------------------------------------------------------- /docs/HowToResearch/References.md: -------------------------------------------------------------------------------- 1 | --- 2 | sidebar_position: 1 3 | --- 4 | 5 | # References Searching 6 | ## References 7 | 8 | [企鹅论文 - 比Sci-Hub更好用的免费论文下载网站【永久免费】](https://doi.qqsci.com/) 9 | ### Google Scholar 10 | ### SciHub 11 | Latest link [Love Science,Love Sci-Hub! – The latest Sci-Hub working domain](https://lovescihub.wordpress.com/) 12 | 13 | [请问SCI-HUB该如何使用？ - 知乎](https://www.zhihu.com/question/266097642/answer/1497246783) 14 | 15 | sci-hub.se 16 | sci-hub.ru 17 | sci-hub.st 18 | sci-hub.cat 19 | sci-hub.ren 20 | https://sci-hub.ee/ 21 | 22 | At the bottom of the website[科研者之家-scihub-SCI写作助手-国家自然科学基金-期刊查询](https://www.home-for-researchers.com/static/index.html#/) 23 | 24 | Zotero Support[ethanwillis/zotero-scihub: A plugin that will automatically download PDFs of zotero items from sci-hub](https://github.com/ethanwillis/zotero-scihub) 25 | 26 | Web plugin[KnifeOnlyI/SciHub-Injector: A simple firefox/chrome extension adds Sci-Hub direct link access on publishing websites](https://github.com/KnifeOnlyI/SciHub-Injector) 27 | 28 | Web plugin: PubMedplus, EasyPubMed 29 | ## Books 30 | [Z-Libarary](https://ca1lib.org) 31 | 32 | [Library Genesis](https://libgen.gs/index.php) 33 | 34 | [PDF Drive - Search and download PDF files for free.](https://www.pdfdrive.com/) 35 | 36 | # References Reviews 37 | [Connected Papers | Find and explore academic papers](https://www.connectedpapers.com/) 38 | 39 | # References Management 40 | 41 | ## References Management 42 | 43 | Zotero是个人认为最好的 44 | 45 | [青柠学术 - 知乎](https://www.zhihu.com/people/qnscholar) 46 | 47 | Citavi 48 | 49 | EndNote 50 | 51 | Mendeley 52 | 53 | ## Social 54 | Slack 55 | 56 | Overleaf 57 | 58 | ReadPaper 59 | 60 | Notion 61 | 62 | Github 63 | 64 | # References Tracking 65 | 66 | ## Tracking newest paper in special directions 67 | 68 | RSS [可能是目前最全的RSS订阅源了 - 奔跑中的奶酪](https://www.runningcheese.com/rss-subscriptions) 69 | 70 | [Inoreader - NEJM](https://www.innoreader.com/) 71 | 72 | 微信公众号: 集智俱乐部、专知、arXIv每日学术速递 73 | 74 | ## 最新的科研软件以及电脑技巧 75 | 76 | 科研软件：Blibli 和 公众号艾米的科研宝库 77 | 78 | 电脑技术：[零度解说 – 零度博客](https://www.freedidi.com/) 79 | -------------------------------------------------------------------------------- /docs/HowToResearch/_category_.json: -------------------------------------------------------------------------------- 1 | { 2 | "label": "How To Research", 3 | "position": 6 4 | } 5 | -------------------------------------------------------------------------------- /docs/UsefulTools/VideoTools.md: -------------------------------------------------------------------------------- 1 | # ScreenShot 电脑录屏 2 | 3 | 适合教师[Thank You For Downloading ScreenRec — Free Screen Recorder](https://screenrec.com/screenrec-quickstart-guide-for-windows-xp-vista-7-8-10/) 4 | 5 | 6 | ShareX 7 | 8 | 9 | 简单好用[Captura](https://mathewsachin.github.io/Captura/) 10 | 11 | 最强大但学习难[Open Broadcaster Software | OBS](https://obsproject.com/) 12 | 13 | # 视频剪辑 14 | [Video Editing Software. Free Download. Easy Movie Editor.](https://www.nchsoftware.com/videopad/) 15 | 16 | [VSDC Free Video Software: audio and video editing tools](http://www.videosoftdev.com/) 17 | 18 | [OpenShot 视频编辑器 | 自由、开放、获奖的视频编辑器，支持 Linux、Mac 以及 Windows!](https://www.openshot.org/zh-hans/) 19 | 20 | 21 | Apple [iMovie - Apple](https://www.apple.com/imovie/) 22 | 23 | Linux[Kdenlive - Video Editing Freedom](https://kdenlive.org/zh/) 24 | 25 | 简单易用[Shotcut - Home](https://shotcut.org/) 26 | 27 | 最强大但学习难[DaVinci Resolve 17 – Edit | Blackmagic Design](https://www.blackmagicdesign.com/products/davinciresolve/edit) 28 | 29 | 30 | 自动剪辑工具[WyattBlue/auto-editor: Auto-Editor: Effort free video editing!](https://github.com/WyattBlue/auto-editor) 31 | 32 | 33 | # 国产 34 | [剪映专业版-全能易用的桌面端剪辑软件-轻而易剪 上演大幕](https://lv.ulikecam.com/) 35 | -------------------------------------------------------------------------------- /docs/UsefulTools/_category_.json: -------------------------------------------------------------------------------- 1 | { 2 | "label": "Data IO Tools", 3 | "position": 2 4 | } 5 | -------------------------------------------------------------------------------- /docs/UsefulTools/browertools.md: -------------------------------------------------------------------------------- 1 | # Tapermonkey 2 | 3 | [Tampermonkey • 首页](https://www.tampermonkey.net/) 4 | 5 | [Greasy Fork - 安全、实用的用户脚本大全](https://greasyfork.org/zh-CN) 6 | 7 | # Userscript+ 8 | 9 | [Userscript+ : 显示当前网站所有可用的UserJS脚本 Jaeger](https://greasyfork.org/zh-CN/scripts/24508-userscript-show-site-all-userjs) 10 | 11 | # 价格变动器 12 | [Online Shopping Price Drop Alert Tracking Website - Wispri](https://wispri.com.au/) 13 | -------------------------------------------------------------------------------- /docs/intro.md: -------------------------------------------------------------------------------- 1 | --- 2 | sidebar_position: 1 3 | --- 4 | # Resources concerned with Data Mining Workflows 5 | 6 | -------------------------------------------------------------------------------- /docusaurus.config.js: -------------------------------------------------------------------------------- 1 | // @ts-check 2 | // Note: type annotations allow type checking and IDEs autocompletion 3 | 4 | const lightCodeTheme = require('prism-react-renderer/themes/github'); 5 | const darkCodeTheme = require('prism-react-renderer/themes/dracula'); 6 | 7 | /** @type {import('@docusaurus/types').Config} */ 8 | const config = { 9 | title: 'MathEpiDeepLearning', //网站名字 10 | tagline: 'Mathematical Epidemiology and Deep Learning', 11 | url: 'https://JuliaEpi.github.io/', // 基准网站 12 | baseUrl: '/MathEpiDeepLearning/', //网站子名字 13 | onBrokenLinks: 'throw', 14 | onBrokenMarkdownLinks: 'warn', 15 | favicon: 'img/mathepia.ico', // 浏览器tab网站logo 16 | organizationName: 'JuliaEpi', // Usually your GitHub org/user name. 17 | projectName: 'MathEpiDeepLearning', // Usually your repo name. 18 | 19 | presets: [ 20 | [ 21 | 'classic', 22 | /** @type {import('@docusaurus/preset-classic').Options} */ 23 | ({ 24 | docs: { 25 | sidebarPath: require.resolve('./sidebars.js'), 26 | // Please change this to your repo. 27 | editUrl: 'https://github.com/JuliaEpi/MathEpiDeepLearning', 28 | }, 29 | blog: { 30 | showReadingTime: true, 31 | // Please change this to your repo. 32 | editUrl: 33 | 'https://github.com/JuliaEpi/MathEpiDeepLearning', 34 | }, 35 | theme: { 36 | customCss: require.resolve('./src/css/custom.css'), 37 | }, 38 | }), 39 | ], 40 | ], 41 | 42 | themeConfig: 43 | /** @type {import('@docusaurus/preset-classic').ThemeConfig} */ 44 | ({ 45 | announcementBar: { 46 | id: 'announcementBar-2', // Increment on change 47 | content: `⭐️ If you like MathEpiDeepLearning, don't hesitate to star us`, 48 | }, 49 | navbar: { 50 | title: 'MathEpiDeepLearning', 51 | logo: { 52 | alt: 'Mathepia Logo', 53 | src: 'img/avatar.jpg', 54 | }, 55 | items: [ 56 | { 57 | type: 'doc', 58 | docId: 'intro', 59 | position: 'left', 60 | label: 'Resources', 61 | }, 62 | { 63 | to: 'packagecollections', 64 | label: 'Packages', 65 | }, 66 | {to: '/blog', label: 'Blog', position: 'left'}, 67 | { 68 | href: 'https://github.com/JuliaEpi', 69 | label: 'GitHub', 70 | position: 'right', 71 | }, 72 | { 73 | href: 'https://song921012.github.io/', 74 | label: 'PengfeiCV', 75 | }, 76 | { 77 | label: 'Mathepia', 78 | href: 'https://github.com/Mathepia', 79 | }, 80 | { 81 | label: 'Mathepia.jl', 82 | href: 'https://github.com/JuliaEpi/Mathepia.jl', 83 | }, 84 | ], 85 | }, 86 | footer: { 87 | style: 'dark', 88 | links: [ 89 | { 90 | title: 'Resources', 91 | items: [ 92 | { 93 | label: 'Julia-Python-Packages', 94 | to: 'packagecollections', 95 | }, 96 | { 97 | label: 'Data-Mining-Resources', 98 | to: '/docs/intro', 99 | }, 100 | { 101 | href: 'https://github.com/Song921012/MathEpiDeepLearningTutorial', 102 | label: 'Tutorials', 103 | },{ 104 | label: 'MathepiaPrograms', 105 | href: 'https://github.com/Mathepia/MathepiaPrograms', 106 | }, 107 | ], 108 | }, 109 | { 110 | title: 'Community', 111 | items: [ 112 | { 113 | label: 'JuliaEpi Organizations', 114 | href: 'https://github.com/Mathepia', 115 | }, 116 | { 117 | label: 'Mathepia Packages Systems', 118 | href: 'https://github.com/JuliaEpi/Mathepia.jl', 119 | }, 120 | ], 121 | }, 122 | { 123 | title: 'More', 124 | items: [ 125 | { 126 | label: 'Pengfei Website', 127 | href: 'https://song921012.github.io/', 128 | }, 129 | { 130 | label: 'Mathepia Organization', 131 | href: 'https://github.com/Mathepia', 132 | }, 133 | ], 134 | }, 135 | ], 136 | copyright: `Copyright © ${new Date().getFullYear()} Mathepia, Inc. Built with Docusaurus.`, 137 | }, 138 | prism: { 139 | theme: lightCodeTheme, 140 | darkTheme: darkCodeTheme, 141 | }, 142 | }), 143 | }; 144 | 145 | module.exports = config; 146 | -------------------------------------------------------------------------------- /package.json: -------------------------------------------------------------------------------- 1 | { 2 | "name": "website", 3 | "version": "0.0.0", 4 | "private": true, 5 | "scripts": { 6 | "docusaurus": "docusaurus", 7 | "start": "docusaurus start", 8 | "build": "docusaurus build", 9 | "swizzle": "docusaurus swizzle", 10 | "deploy": "docusaurus deploy", 11 | "clear": "docusaurus clear", 12 | "serve": "docusaurus serve", 13 | "write-translations": "docusaurus write-translations", 14 | "write-heading-ids": "docusaurus write-heading-ids" 15 | }, 16 | "dependencies": { 17 | "@docusaurus/core": "2.0.0-beta.18", 18 | "@docusaurus/preset-classic": "2.0.0-beta.18", 19 | "@mdx-js/react": "^1.6.22", 20 | "clsx": "^1.1.1", 21 | "prism-react-renderer": "^1.3.1", 22 | "react": "^17.0.2", 23 | "react-dom": "^17.0.2" 24 | }, 25 | "browserslist": { 26 | "production": [ 27 | ">0.5%", 28 | "not dead", 29 | "not op_mini all" 30 | ], 31 | "development": [ 32 | "last 1 chrome version", 33 | "last 1 firefox version", 34 | "last 1 safari version" 35 | ] 36 | } 37 | } 38 | -------------------------------------------------------------------------------- /sidebars.js: -------------------------------------------------------------------------------- 1 | /** 2 | * Creating a sidebar enables you to: 3 | - create an ordered group of docs 4 | - render a sidebar for each doc of that group 5 | - provide next/previous navigation 6 | 7 | The sidebars can be generated from the filesystem, or explicitly defined here. 8 | 9 | Create as many sidebars as you want. 10 | */ 11 | 12 | // @ts-check 13 | 14 | /** @type {import('@docusaurus/plugin-content-docs').SidebarsConfig} */ 15 | const sidebars = { 16 | // By default, Docusaurus generates a sidebar from the docs folder structure 17 | tutorialSidebar: [{type: 'autogenerated', dirName: '.'}], 18 | 19 | // But you can create a sidebar manually 20 | /* 21 | tutorialSidebar: [ 22 | { 23 | type: 'category', 24 | label: 'Tutorial', 25 | items: ['hello'], 26 | }, 27 | ], 28 | */ 29 | }; 30 | 31 | module.exports = sidebars; 32 | -------------------------------------------------------------------------------- /src/components/HomepageFeatures/index.js: -------------------------------------------------------------------------------- 1 | import React from 'react'; 2 | import clsx from 'clsx'; 3 | import styles from './styles.module.css'; 4 | 5 | const FeatureList = [ 6 | { 7 | title: 'Julia and Python Packages', 8 | Svg: require('@site/static/img/undraw_docusaurus_mountain.svg').default, 9 | description: ( 10 | <> 11 | Awesome-spatial-temporal-data-mining-packages. Julia and Python resources on spatial and temporal data mining. Mathematical epidemiology as an application. 12 | 13 | ), 14 | }, 15 | { 16 | title: 'Data Mining Resources Collections', 17 | Svg: require('@site/static/img/undraw_docusaurus_tree.svg').default, 18 | description: ( 19 | <> 20 | Datasets, Data Science, Data visulization, How to research, Other Useful Tools 21 | 22 | ), 23 | }, 24 | { 25 | title: 'My Projects', 26 | Svg: require('@site/static/img/undraw_docusaurus_react.svg').default, 27 | description: ( 28 | <> 29 | Mathepia Projects 30 | 31 | ), 32 | }, 33 | ]; 34 | 35 | function Feature({Svg, title, description}) { 36 | return ( 37 |
38 |
39 | 40 |
41 |
42 |

{title}

43 |

{description}

44 |
45 |
46 | ); 47 | } 48 | 49 | export default function HomepageFeatures() { 50 | return ( 51 |
52 |
53 |
54 | {FeatureList.map((props, idx) => ( 55 | 56 | ))} 57 |
58 |
59 |
60 | ); 61 | } 62 | -------------------------------------------------------------------------------- /src/components/HomepageFeatures/styles.module.css: -------------------------------------------------------------------------------- 1 | .features { 2 | display: flex; 3 | align-items: center; 4 | padding: 2rem 0; 5 | width: 100%; 6 | } 7 | 8 | .featureSvg { 9 | height: 200px; 10 | width: 200px; 11 | } 12 | -------------------------------------------------------------------------------- /src/css/custom.css: -------------------------------------------------------------------------------- 1 | /** 2 | * Any CSS included here will be global. The classic template 3 | * bundles Infima by default. Infima is a CSS framework designed to 4 | * work well for content-centric websites. 5 | */ 6 | 7 | /* You can override the default Infima variables here. */ 8 | :root { 9 | --ifm-color-primary: #2e8555; 10 | --ifm-color-primary-dark: #29784c; 11 | --ifm-color-primary-darker: #277148; 12 | --ifm-color-primary-darkest: #205d3b; 13 | --ifm-color-primary-light: #339961; 14 | --ifm-color-primary-lighter: #359962; 15 | --ifm-color-primary-lightest: #3cad6e; 16 | --ifm-code-font-size: 95%; 17 | } 18 | 19 | /* For readability concerns, you should choose a lighter palette in dark mode. */ 20 | [data-theme='dark'] { 21 | --ifm-color-primary: #25c2a0; 22 | --ifm-color-primary-dark: #21af90; 23 | --ifm-color-primary-darker: #1fa588; 24 | --ifm-color-primary-darkest: #1a8870; 25 | --ifm-color-primary-light: #29d5b0; 26 | --ifm-color-primary-lighter: #32d8b4; 27 | --ifm-color-primary-lightest: #4fddbf; 28 | } 29 | 30 | .docusaurus-highlight-code-line { 31 | background-color: rgba(0, 0, 0, 0.1); 32 | display: block; 33 | margin: 0 calc(-1 * var(--ifm-pre-padding)); 34 | padding: 0 var(--ifm-pre-padding); 35 | } 36 | 37 | [data-theme='dark'] .docusaurus-highlight-code-line { 38 | background-color: rgba(0, 0, 0, 0.3); 39 | } 40 | -------------------------------------------------------------------------------- /src/pages/index.js: -------------------------------------------------------------------------------- 1 | import React from 'react'; 2 | import clsx from 'clsx'; 3 | import Layout from '@theme/Layout'; 4 | import Link from '@docusaurus/Link'; 5 | import useDocusaurusContext from '@docusaurus/useDocusaurusContext'; 6 | import styles from './index.module.css'; 7 | import HomepageFeatures from '@site/src/components/HomepageFeatures'; 8 | 9 | function HomepageHeader() { 10 | const {siteConfig} = useDocusaurusContext(); 11 | return ( 12 |
13 |
14 |

{siteConfig.title}

15 |

{siteConfig.tagline}

16 |
17 | 20 | Github 21 | 22 |
23 |
24 |
25 | ); 26 | } 27 | 28 | export default function Home() { 29 | const {siteConfig} = useDocusaurusContext(); 30 | return ( 31 | 34 | 35 |
36 | 37 |
38 | 39 | ); 40 | } 41 | -------------------------------------------------------------------------------- /src/pages/index.module.css: -------------------------------------------------------------------------------- 1 | /** 2 | * CSS files with the .module.css suffix will be treated as CSS modules 3 | * and scoped locally. 4 | */ 5 | 6 | .heroBanner { 7 | padding: 4rem 0; 8 | text-align: center; 9 | position: relative; 10 | overflow: hidden; 11 | } 12 | 13 | @media screen and (max-width: 996px) { 14 | .heroBanner { 15 | padding: 2rem; 16 | } 17 | } 18 | 19 | .buttons { 20 | display: flex; 21 | align-items: center; 22 | justify-content: center; 23 | } 24 | -------------------------------------------------------------------------------- /static/.nojekyll: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JuliaEpi/MathEpiDeepLearning/e667795cd42ca9ae009880fedfc3db1008d3e6c4/static/.nojekyll -------------------------------------------------------------------------------- /static/img/avatar.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JuliaEpi/MathEpiDeepLearning/e667795cd42ca9ae009880fedfc3db1008d3e6c4/static/img/avatar.jpg -------------------------------------------------------------------------------- /static/img/docusaurus.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JuliaEpi/MathEpiDeepLearning/e667795cd42ca9ae009880fedfc3db1008d3e6c4/static/img/docusaurus.png -------------------------------------------------------------------------------- /static/img/logo.svg: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /static/img/mathepia.ico: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JuliaEpi/MathEpiDeepLearning/e667795cd42ca9ae009880fedfc3db1008d3e6c4/static/img/mathepia.ico -------------------------------------------------------------------------------- /static/img/tutorial/docsVersionDropdown.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JuliaEpi/MathEpiDeepLearning/e667795cd42ca9ae009880fedfc3db1008d3e6c4/static/img/tutorial/docsVersionDropdown.png -------------------------------------------------------------------------------- /static/img/tutorial/localeDropdown.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JuliaEpi/MathEpiDeepLearning/e667795cd42ca9ae009880fedfc3db1008d3e6c4/static/img/tutorial/localeDropdown.png -------------------------------------------------------------------------------- /static/img/undraw_docusaurus_mountain.svg: -------------------------------------------------------------------------------- 1 | 2 | Easy to Use 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 46 | 47 | 48 | 49 | 50 | 51 | 52 | 53 | 54 | 55 | 56 | 57 | 58 | 59 | 60 | 61 | 62 | 63 | 64 | 65 | 66 | 67 | 68 | 69 | 70 | 71 | 72 | 73 | 74 | 75 | 76 | 77 | 78 | 79 | 80 | 81 | 82 | 83 | 84 | 85 | 86 | 87 | 88 | 89 | 90 | 91 | 92 | 93 | 94 | 95 | 96 | 97 | 98 | 99 | 100 | 101 | 102 | 103 | 104 | 105 | 106 | 107 | 108 | 109 | 110 | 111 | 112 | 113 | 114 | 115 | 116 | 117 | 118 | 119 | 120 | 121 | 122 | 123 | 124 | 125 | 126 | 127 | 128 | 129 | 130 | 131 | 132 | 133 | 134 | 135 | 136 | 137 | 138 | 139 | 140 | 141 | 142 | 143 | 144 | 145 | 146 | 147 | 148 | 149 | 150 | 151 | 152 | 153 | 154 | 155 | 156 | 157 | 158 | 159 | 160 | 161 | 162 | 163 | 164 | 165 | 166 | 167 | 168 | 169 | 170 | 171 | 172 | -------------------------------------------------------------------------------- /static/img/undraw_docusaurus_tree.svg: -------------------------------------------------------------------------------- 1 | 2 | Focus on What Matters 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | --------------------------------------------------------------------------------