├── inst ├── .DS_Store ├── COPYRIGHTS └── COPYING ├── R ├── plot.IsingFit.R ├── print.IsingFit.R ├── summary.IsingFit.R └── IsingFit.R ├── NAMESPACE ├── NEWS ├── IsingFit.Rproj ├── man ├── methods.Rd ├── isingfit-package.rd └── isingfit.rd ├── DESCRIPTION └── README.md /inst/.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/cvborkulo/IsingFit/HEAD/inst/.DS_Store -------------------------------------------------------------------------------- /R/plot.IsingFit.R: -------------------------------------------------------------------------------- 1 | plot.IsingFit <- 2 | function(x,...) qgraph(x$q,DoNotPlot = FALSE, ...) 3 | -------------------------------------------------------------------------------- /R/print.IsingFit.R: -------------------------------------------------------------------------------- 1 | print.IsingFit <- 2 | function(x, ...) 3 | { 4 | cat("Estimated network:\n") 5 | 6 | print(round(x$weiadj,2)) 7 | 8 | cat("\n\nEstimated Thresholds:\n") 9 | 10 | print(x$thresholds) 11 | } 12 | -------------------------------------------------------------------------------- /NAMESPACE: -------------------------------------------------------------------------------- 1 | export(IsingFit) 2 | S3method(plot,IsingFit) 3 | S3method(print,IsingFit) 4 | S3method(summary,IsingFit) 5 | 6 | import(qgraph) 7 | import(Matrix) 8 | import(glmnet) 9 | importFrom("stats", "sd") 10 | importFrom("utils", "setTxtProgressBar", "txtProgressBar") -------------------------------------------------------------------------------- /inst/COPYRIGHTS: -------------------------------------------------------------------------------- 1 | COPYRIGHT STATUS 2 | ---------------- 3 | 4 | This code is 5 | 6 | Copyright (C) 2013, 2014 Claudia van Borkulo 7 | 8 | All code is subject to the GNU General Public License, Version 2. See 9 | the file COPYING for the exact conditions under which you may 10 | redistribute it. -------------------------------------------------------------------------------- /NEWS: -------------------------------------------------------------------------------- 1 | Changes in version 0.4.1: 2 | - Added Jesse Boot as contributer 3 | 4 | Changes in version 0.4: 5 | - Maintainer (temporarily) changed to Sacha Epskamp 6 | - Added the min_sum argument for handling selection bias (Boot, De Ron, Haslbeck & Epskamp, in preperation) 7 | - IsingFit now gives an error when the data is not binary or contains missing values -------------------------------------------------------------------------------- /IsingFit.Rproj: -------------------------------------------------------------------------------- 1 | Version: 1.0 2 | 3 | RestoreWorkspace: Default 4 | SaveWorkspace: Default 5 | AlwaysSaveHistory: Default 6 | 7 | EnableCodeIndexing: Yes 8 | UseSpacesForTab: Yes 9 | NumSpacesForTab: 2 10 | Encoding: UTF-8 11 | 12 | RnwWeave: Sweave 13 | LaTeX: pdfLaTeX 14 | 15 | BuildType: Package 16 | PackageInstallArgs: --no-multiarch --with-keep.source 17 | -------------------------------------------------------------------------------- /R/summary.IsingFit.R: -------------------------------------------------------------------------------- 1 | summary.IsingFit <- 2 | function(object, ...) 3 | { 4 | cat("\tNetwork Density:\t\t", round(mean(object$weiadj[upper.tri(object$weiadj)]!=0),2),"\n", 5 | "Gamma:\t\t\t",round(object$gamma,2),"\n", 6 | "Rule used:\t\t",ifelse(object$AND,"And-rule","Or-rule"),"\n", 7 | "Analysis took:\t\t",format(object$time,format="%s"),"\n" 8 | ) 9 | } 10 | -------------------------------------------------------------------------------- /man/methods.Rd: -------------------------------------------------------------------------------- 1 | \name{Ising-methods} 2 | \alias{print.IsingFit} 3 | \alias{plot.IsingFit} 4 | \alias{summary.IsingFit} 5 | %- Also NEED an '\alias' for EACH other topic documented here. 6 | \title{ 7 | Methods for IsingFit objects 8 | } 9 | \description{ 10 | Print method prints the IsingFit output , plot method plots the estimated network (with the \code{qgraph} package), and summary method returns density of the network, the value of gamma used, the rule used, and the time the analysis took. 11 | } 12 | \usage{ 13 | \method{print}{IsingFit}(x, \dots) 14 | \method{summary}{IsingFit}(object, \dots) 15 | \method{plot}{IsingFit}(x, \dots) 16 | } 17 | %- maybe also 'usage' for other objects documented here. 18 | \arguments{ 19 | \item{x}{ 20 | output of \code{\link{IsingFit}} 21 | } 22 | \item{object}{ 23 | output of \code{\link{IsingFit}} 24 | } 25 | \item{\dots}{ 26 | Arguments sent to qgraph. Only used in plot method. 27 | } 28 | } 29 | \author{ 30 | Claudia van Borkulo 31 | } -------------------------------------------------------------------------------- /DESCRIPTION: -------------------------------------------------------------------------------- 1 | Package: IsingFit 2 | Type: Package 3 | Title: Fitting Ising Models Using the ELasso Method 4 | Version: 0.4.1 5 | Authors@R: c( 6 | person("van Borkulo", "Claudia", role = c("aut")), 7 | person("Sacha", "Epskamp", email = "mail@sachaepskamp.com",role = c("aut", "cre")), 8 | person("Alexander", "Robitzsch", role = c("ctb")), 9 | person("Mihai Alexandru", "Constantin", role = c("ctb")), 10 | person("Jesse", "Boot", role = c("ctb")) 11 | ) 12 | Maintainer: Sacha Epskamp 13 | Depends: R (>= 3.0.0) 14 | Imports: qgraph, Matrix, glmnet 15 | Suggests: IsingSampler 16 | Description: This network estimation procedure eLasso, which is based on the Ising model, combines l1-regularized logistic regression with model selection based on the Extended Bayesian Information Criterion (EBIC). EBIC is a fit measure that identifies relevant relationships between variables. The resulting network consists of variables as nodes and relevant relationships as edges. Can deal with binary data. 17 | License: GPL-2 18 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | IsingFit 2 | ========== 3 | 4 | This network estimation procedure eLasso, which is based on the Ising model, combines l1-regularized logistic regression with model selection based on the Extended Bayesian Information Criterion (EBIC). EBIC is a fit measure that identifies relevant relationships between variables. The resulting network consists of variables as nodes and relevant relationships as edges. Can deal with binary data. 5 | 6 | ## Background Information 7 | For more information on IsingFit, take a look at: 8 | 9 | Van Borkulo, C. D., Borsboom, D., Epskamp, S., Blanken, T. F., Boschloo, L., Schoevers, R. A., & Waldorp, L. J. (2014). A new method for constructing networks from binary data. Scientific reports, 4(1), 5918. 10 | 11 | ## Bug Reports, Feature Request, or Contributing 12 | If you encounter any bugs or have ideas for new features, you can submit them by creating an issue on Github. Additionally, if you want to contribute to the development of IsingFit, you can initiate a branch with a pull request; we can review and discuss the proposed changes. 13 | 14 | ## Credits 15 | The package was developed by [Claudia van Borkulo](https://cvborkulo.com/) during her PhD at the University of Amsterdam. It is now maintained by [Sacha Epskamp](https://scholar.google.nl/citations?hl=en&user=fQpiw-sAAAAJ), an Associate Professor at the National University of Singapore: Department of Psychology. 16 | -------------------------------------------------------------------------------- /man/isingfit-package.rd: -------------------------------------------------------------------------------- 1 | \name{IsingFit-package} 2 | \alias{IsingFit-package} 3 | \docType{package} 4 | \title{ 5 | Network estimation using the eLasso method 6 | } 7 | \description{ 8 | This network estimation procedure eLasso, which is based on the Ising model, combines l1-regularized logistic regression with model selection based on the Extended Bayesian Information Criterion (EBIC). EBIC is a fit measure that identifies relevant relationships between variables. The resulting network consists of variables as nodes and relevant relationships as edges. Can deal with binary data. 9 | } 10 | \details{ 11 | \tabular{ll}{ 12 | Package: \tab IsingFit\cr 13 | Type: \tab Package\cr 14 | Version: \tab 0.3.2\cr 15 | Date: \tab 2018-6-1\cr 16 | 17 | License: \tab What license is it under?\cr 18 | } 19 | } 20 | \author{ 21 | Claudia D. van Borkulo, Sacha Epskamp; with contributions from Alexander Robitzsch and Mihai Alexandru Constantin 22 | 23 | Maintainer: Claudia D. van Borkulo 24 | } 25 | \references{ 26 | Chen, J., & Chen, Z. (2008). Extended bayesian information criteria for model selection with large model spaces. Biometrika, 95(3), 759-771. 27 | 28 | Foygel, R., & Drton, M. (2011). Bayesian model choice and information criteria in sparse generalized linear models. arXiv preprint arXiv:1112.5635. 29 | 30 | Ravikumar, P., Wainwright, M. J., & Lafferty, J. D. (2010). High-dimensional Ising model selection using l1-regularized logistic regression. The Annals of Statistics, 38, 1287 - 1319. 31 | 32 | van Borkulo, C. D., Borsboom, D., Epskamp, S., Blanken, T. F., Boschloo, L., Schoevers, R. A., & Waldorp, L. J. (2014). A new method for constructing networks from binary data. Scientific Reports 4, 5918; DOI:10.1038/srep05918. 33 | } 34 | % ~~ Optionally other standard keywords, one per line, from file ~~ 35 | % ~~ KEYWORDS in the R documentation directory ~~ 36 | 37 | -------------------------------------------------------------------------------- /man/isingfit.rd: -------------------------------------------------------------------------------- 1 | \name{IsingFit} 2 | \alias{IsingFit} 3 | \title{ 4 | Network estimation using the eLasso method 5 | } 6 | \description{ 7 | This network estimation procedure eLasso, which is based on the Ising model, combines l1-regularized logistic regression with model selection based on the Extended Bayesian Information Criterion (EBIC). EBIC is a fit measure that identifies relevant relationships between variables. The resulting network consists of variables as nodes and relevant relationships as edges. Can deal with binary data. 8 | } 9 | \usage{ 10 | IsingFit(x, family = "binomial", AND = TRUE, gamma = 0.25, plot 11 | = TRUE, progressbar = TRUE, min_sum = -Inf, 12 | lowerbound.lambda = NA, ...) 13 | 14 | } 15 | 16 | \arguments{ 17 | \item{x}{ 18 | Input matrix. The dimension of the matrix is nobs x nvars; each row is a vector of observations of the variables. Must be cross-sectional data. 19 | } 20 | \item{family}{ 21 | The default is 'binomial', treating the data as binary. Currently, this procedure is only supported for binary data. 22 | } 23 | \item{AND}{ 24 | Logical. Can be TRUE of FALSE to indicate whether the AND-rule or the OR-rule should be used to define the edges in the network. Defaults to TRUE. 25 | } 26 | \item{gamma}{ 27 | A value of hyperparameter gamma in the extended BIC. Can be anything between 0 and 1. Defaults to .25. 28 | } 29 | \item{plot}{ 30 | Logical. Should the resulting network be plotted? 31 | } 32 | \item{progressbar}{ 33 | Logical. Should the pbar be plotted in order to see the progress of the estimation procedure? 34 | } 35 | \item{min_sum}{ The minimum sum score that is artifically possible in the dataset. Defaults to -Inf. Set this only if you know a lower sum score is not possible in the data, for example due to selection bias.} 36 | \item{lowerbound.lambda}{ 37 | The minimum value of tuning parameter lambda (regularization parameter). Can be used to compare networks that are based on different sample sizes. The lowerbound.lambda is based on the number of observations in the smallest group n: sqrt(log(p)/n). p is the number of variables, that should be the same in both groups. When both networks are estimated with the same lowerbound for lambda (based on the smallest group), the two networks can be directly compared. 38 | } 39 | \item{\dots}{ 40 | Arguments sent to \code{qgraph}. 41 | } 42 | } 43 | 44 | \value{ 45 | IsingFit returns (invisibly) a 'IsingFit' object that contains the following items: 46 | \item{weiadj }{The weighted adjacency matrix.} 47 | \item{thresholds }{Thresholds of the variables.} 48 | \item{q }{The object that is returned by qgraph (class 'qgraph').} 49 | \item{gamma }{The value of hyperparameter gamma.} 50 | \item{AND }{A logical indicating whether the AND-rule is used or not. If not, the OR-rule is used.} 51 | \item{time }{The time it took to estimate the network.} 52 | \item{asymm.weights }{The (asymmetrical) weighted adjacency matrix before applying the AND/OR rule.} 53 | \item{lambda.values }{The values of the tuning parameter per node that ensured the best fitting set of neighbors.} 54 | } 55 | 56 | \references{ 57 | Chen, J., & Chen, Z. (2008). Extended bayesian information criteria for model selection with large model spaces. Biometrika, 95(3), 759-771. 58 | 59 | Foygel, R., & Drton, M. (2011). Bayesian model choice and information criteria in sparse generalized linear models. arXiv preprint arXiv:1112.5635. 60 | 61 | Ravikumar, P., Wainwright, M. J., & Lafferty, J. D. (2010). High-dimensional Ising model selection using l1-regularized logistic regression. The Annals of Statistics, 38, 1287 - 1319. 62 | 63 | van Borkulo, C. D., Borsboom, D., Epskamp, S., Blanken, T. F., Boschloo, L., Schoevers, R. A., & Waldorp, L. J. (2014). A new method for constructing networks from binary data. Scientific Reports 4, 5918; DOI:10.1038/srep05918. 64 | } 65 | \author{ 66 | Claudia D. van Borkulo, Sacha Epskamp; with contributions from Alexander Robitzsch and Mihai Alexandru Constantin 67 | 68 | Maintainer: Claudia D. van Borkulo 69 | } 70 | \note{ 71 | See also my website: http://cvborkulo.com 72 | } 73 | 74 | \examples{ 75 | library("IsingSampler") 76 | 77 | ### Simulate dataset ### 78 | # Input: 79 | N <- 6 # Number of nodes 80 | nSample <- 1000 # Number of samples 81 | 82 | # Ising parameters: 83 | Graph <- matrix(sample(0:1,N^2,TRUE,prob = c(0.8, 0.2)),N,N) * runif(N^2,0.5,2) 84 | Graph <- pmax(Graph,t(Graph)) 85 | diag(Graph) <- 0 86 | Thresh <- -rowSums(Graph) / 2 87 | 88 | # Simulate: 89 | Data <- IsingSampler(nSample, Graph, Thresh) 90 | 91 | ### Fit using IsingFit ### 92 | Res <- IsingFit(Data, family='binomial', plot=FALSE) 93 | 94 | # Plot results: 95 | library("qgraph") 96 | layout(t(1:2)) 97 | qgraph(Res$weiadj,fade = FALSE) 98 | title("Estimated network") 99 | qgraph(Graph,fade = FALSE) 100 | title("Original network") 101 | } 102 | % Add one or more standard keywords, see file 'KEYWORDS' in the 103 | % R documentation directory. 104 | % \keyword{ ~kwd1 } 105 | % \keyword{ ~kwd2 }% __ONLY ONE__ keyword per line 106 | -------------------------------------------------------------------------------- /R/IsingFit.R: -------------------------------------------------------------------------------- 1 | IsingFit <- 2 | function(x, family = "binomial", AND = TRUE, gamma = 0.25, plot = TRUE, progressbar = TRUE, min_sum = -Inf, lowerbound.lambda = NA, ...) { 3 | t0 <- Sys.time() 4 | xx <- x 5 | if (family != "binomial") { 6 | stop("This procedure is currently only supported for binary (family='binomial') data") 7 | } 8 | 9 | ## Check to prevent error of lognet() in package glmnet 10 | # checklognet <- function(y){ 11 | # res <- c() # 0: too little variance, 1: good to go 12 | # y=as.factor(y) 13 | # ntab=table(y) 14 | # minclass=min(ntab) 15 | # if(minclass<=1) res=0 else res=1 16 | # return(res) 17 | # } 18 | 19 | allowedNodes <- function(nodeValues) { 20 | nodeValues <- as.factor(nodeValues) 21 | valuesFrequency <- table(nodeValues) 22 | minFrequency <- min(valuesFrequency) 23 | maxFrequency <- max(valuesFrequency) 24 | if (minFrequency <= 1 || maxFrequency >= length(nodeValues) - 1) { 25 | return(0) 26 | } else { 27 | return(1) 28 | } 29 | } 30 | 31 | # NodesToAnalyze <- apply(x,2,checklognet) !=0 32 | NodesToAnalyze <- apply(x, 2, allowedNodes) != 0 33 | names(NodesToAnalyze) <- colnames(x) 34 | if (!any(NodesToAnalyze)) stop("No variance in dataset") 35 | if (any(!NodesToAnalyze)) { 36 | warning(paste("Nodes with too little variance (not allowed):", paste(colnames(x)[!NodesToAnalyze], collapse = ", "))) 37 | } 38 | ## 39 | 40 | x <- as.matrix(x) 41 | 42 | # Check data: 43 | # Any missing? 44 | if (any(is.na(x))){ 45 | stop("IsingFit does not support missing data") 46 | } 47 | 48 | # Binary? 49 | if (!all( x == 1 | x== 0)) { 50 | stop("IsingFit only supports data that is encoded as (0,1)") 51 | } 52 | 53 | # Minimum sum score? 54 | if (any(rowSums(x) < min_sum)){ 55 | stop("Rows detected with sumscore < min_sum") 56 | } 57 | 58 | 59 | allthemeans <- colMeans(x) 60 | x <- x[, NodesToAnalyze, drop = FALSE] 61 | nvar <- ncol(x) 62 | p <- nvar - 1 63 | intercepts <- betas <- lambdas <- list(vector, nvar) 64 | nlambdas <- rep(0, nvar) 65 | N <- vector() 66 | for (i in 1:nvar) { 67 | subData <- x[rowSums(replace(x[, -i, drop = FALSE], x[, -i, drop = FALSE] < 0, 0)) != (min_sum - 1), ] 68 | a <- glmnet(subData[, -i], subData[, i], family = "binomial") 69 | intercepts[[i]] <- a$a0 70 | betas[[i]] <- a$beta 71 | lambdas[[i]] <- a$lambda 72 | nlambdas[i] <- length(lambdas[[i]]) 73 | N[i] <- nrow(subData) 74 | } 75 | 76 | if (progressbar == TRUE) pb <- txtProgressBar(max = nvar, style = 3) 77 | # penalty now different for each variable so for EBIC and penalty also empty matrix 78 | P <- logl <- sumlogl <- J <- EBIC <- penalty <- matrix(0, max(nlambdas), nvar) 79 | for (i in 1:nvar) 80 | { 81 | J[1:ncol(betas[[i]]), i] <- colSums(betas[[i]] != 0) 82 | } 83 | logl_M <- P_M <- list() # I could not get an array with differ nrows so now a list 84 | for (i in 1:length(N)) { 85 | logl_M[[i]] <- P_M[[i]] <- array(0, dim = c(N[i], max(nlambdas))) # sample size 86 | } 87 | 88 | for (i in 1:nvar) { # i <- 1 89 | 90 | subData <- x[rowSums(replace(x[, -i, drop = FALSE], x[, -i, drop = FALSE] < 0, 0)) != (min_sum - 1), ] 91 | sample_size <- N[i] 92 | betas.ii <- as.matrix(betas[[i]]) 93 | int.ii <- intercepts[[i]] 94 | y <- matrix(0, nrow = sample_size, ncol = ncol(betas.ii)) 95 | xi <- subData[, -i] 96 | NB <- nrow(betas.ii) # number of rows in beta 97 | for (bb in 1:NB) { # bb <- 1 98 | y <- y + betas.ii[rep(bb, sample_size), ] * xi[, bb] 99 | } 100 | y <- matrix(int.ii, nrow = sample_size, ncol = ncol(y), byrow = TRUE) + y 101 | # number of NAs 102 | n_NA <- max(nlambdas) - ncol(y) 103 | if (n_NA > 0) { 104 | for (vv in 1:n_NA) { 105 | y <- cbind(y, NA) 106 | } 107 | } 108 | # calculate P matrix 109 | P_M[[i]] <- exp(y * subData[, i]) / (1 + exp(y)) 110 | logl_M[[i]] <- log(P_M[[i]]) 111 | if (progressbar==TRUE) setTxtProgressBar(pb, i) 112 | } 113 | 114 | for (i in 1:nvar) { 115 | sumlogl[, i] <- colSums(logl_M[[i]]) 116 | } 117 | if (progressbar==TRUE) close(pb) 118 | sumlogl[sumlogl == 0] <- NA 119 | 120 | for (i in 1:nvar) { 121 | penalty[, i] <- J[, i] * log(N[i]) + 2 * gamma * J[, i] * log(p) 122 | EBIC[, i] <- -2 * sumlogl[, i] + penalty[, i] 123 | } 124 | 125 | 126 | 127 | lambda.mat <- matrix(NA, nrow(EBIC), ncol(EBIC)) 128 | for (i in 1:nvar) { 129 | lambda.mat[, i] <- c(lambdas[[i]], rep(NA, nrow(EBIC) - length(lambdas[[i]]))) 130 | } 131 | 132 | if (!is.na(lowerbound.lambda)) { 133 | EBIC <- EBIC / (lambda.mat >= lowerbound.lambda) * 1 134 | } 135 | 136 | lambda.opt <- apply(EBIC, 2, which.min) 137 | lambda.val <- rep(NA, nvar) 138 | thresholds <- 0 139 | for (i in 1:length(lambda.opt)) { 140 | lambda.val[i] <- lambda.mat[lambda.opt[i], i] 141 | thresholds[i] <- intercepts[[i]][lambda.opt[i]] 142 | } 143 | weights.opt <- matrix(, nvar, nvar) 144 | for (i in 1:nvar) { 145 | weights.opt[i, -i] <- betas[[i]][, lambda.opt[i]] 146 | } 147 | asymm.weights <- weights.opt 148 | diag(asymm.weights) <- 0 149 | if (AND == TRUE) { 150 | adj <- weights.opt 151 | adj <- (adj != 0) * 1 152 | EN.weights <- adj * t(adj) 153 | EN.weights <- EN.weights * weights.opt 154 | meanweights.opt <- (EN.weights + t(EN.weights)) / 2 155 | diag(meanweights.opt) <- 0 156 | } else { 157 | meanweights.opt <- (weights.opt + t(weights.opt)) / 2 158 | diag(meanweights.opt) <- 0 159 | } 160 | graphNew <- matrix(0, length(NodesToAnalyze), length(NodesToAnalyze)) 161 | graphNew[NodesToAnalyze, NodesToAnalyze] <- meanweights.opt 162 | colnames(graphNew) <- rownames(graphNew) <- colnames(xx) 163 | threshNew <- ifelse(allthemeans > 0.5, -Inf, Inf) 164 | threshNew[NodesToAnalyze] <- thresholds 165 | if (plot == TRUE) notplot <- FALSE else notplot <- TRUE 166 | q <- qgraph(graphNew, layout = "spring", labels = names(NodesToAnalyze), DoNotPlot = notplot, ...) 167 | Res <- list( 168 | weiadj = graphNew, thresholds = threshNew, q = q, gamma = gamma, 169 | AND = AND, time = Sys.time() - t0, asymm.weights = asymm.weights, 170 | lambda.values = lambda.val 171 | ) 172 | class(Res) <- "IsingFit" 173 | return(Res) 174 | } 175 | 176 | plot.IsingFit <- function(object,...) qgraph(object$q,DoNotPlot = FALSE, ...) 177 | 178 | print.IsingFit <- function(x) 179 | { 180 | cat("Estimated network:\n") 181 | 182 | print(round(x$weiadj,2)) 183 | 184 | cat("\n\nEstimated Thresholds:\n") 185 | 186 | print(x$thresholds) 187 | } 188 | 189 | summary.IsingFit <- function(object) 190 | { 191 | cat("\tNetwork Density:\t\t", round(mean(object$weiadj[upper.tri(object$weiadj)]!=0),2),"\n", 192 | "Gamma:\t\t\t",round(object$gamma,2),"\n", 193 | "Rule used:\t\t",ifelse(object$AND,"And-rule","Or-rule"),"\n", 194 | "Analysis took:\t\t",format(object$time,format="%s"),"\n" 195 | ) 196 | } 197 | 198 | -------------------------------------------------------------------------------- /inst/COPYING: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 2, June 1991 3 | 4 | Copyright (C) 1989, 1991 Free Software Foundation, Inc. 5 | 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA 6 | Everyone is permitted to copy and distribute verbatim copies 7 | of this license document, but changing it is not allowed. 8 | 9 | Preamble 10 | 11 | The licenses for most software are designed to take away your 12 | freedom to share and change it. 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You may copy and distribute the Program (or a work based on it, 135 | under Section 2) in object code or executable form under the terms of 136 | Sections 1 and 2 above provided that you also do one of the following: 137 | 138 | a) Accompany it with the complete corresponding machine-readable 139 | source code, which must be distributed under the terms of Sections 140 | 1 and 2 above on a medium customarily used for software interchange; or, 141 | 142 | b) Accompany it with a written offer, valid for at least three 143 | years, to give any third party, for a charge no more than your 144 | cost of physically performing source distribution, a complete 145 | machine-readable copy of the corresponding source code, to be 146 | distributed under the terms of Sections 1 and 2 above on a medium 147 | customarily used for software interchange; or, 148 | 149 | c) Accompany it with the information you received as to the offer 150 | to distribute corresponding source code. (This alternative is 151 | allowed only for noncommercial distribution and only if you 152 | received the program in object code or executable form with such 153 | an offer, in accord with Subsection b above.) 154 | 155 | The source code for a work means the preferred form of the work for 156 | making modifications to it. For an executable work, complete source 157 | code means all the source code for all modules it contains, plus any 158 | associated interface definition files, plus the scripts used to 159 | control compilation and installation of the executable. However, as a 160 | special exception, the source code distributed need not include 161 | anything that is normally distributed (in either source or binary 162 | form) with the major components (compiler, kernel, and so on) of the 163 | operating system on which the executable runs, unless that component 164 | itself accompanies the executable. 165 | 166 | If distribution of executable or object code is made by offering 167 | access to copy from a designated place, then offering equivalent 168 | access to copy the source code from the same place counts as 169 | distribution of the source code, even though third parties are not 170 | compelled to copy the source along with the object code. 171 | 172 | 4. You may not copy, modify, sublicense, or distribute the Program 173 | except as expressly provided under this License. Any attempt 174 | otherwise to copy, modify, sublicense or distribute the Program is 175 | void, and will automatically terminate your rights under this License. 176 | However, parties who have received copies, or rights, from you under 177 | this License will not have their licenses terminated so long as such 178 | parties remain in full compliance. 179 | 180 | 5. You are not required to accept this License, since you have not 181 | signed it. However, nothing else grants you permission to modify or 182 | distribute the Program or its derivative works. These actions are 183 | prohibited by law if you do not accept this License. Therefore, by 184 | modifying or distributing the Program (or any work based on the 185 | Program), you indicate your acceptance of this License to do so, and 186 | all its terms and conditions for copying, distributing or modifying 187 | the Program or works based on it. 188 | 189 | 6. Each time you redistribute the Program (or any work based on the 190 | Program), the recipient automatically receives a license from the 191 | original licensor to copy, distribute or modify the Program subject to 192 | these terms and conditions. You may not impose any further 193 | restrictions on the recipients' exercise of the rights granted herein. 194 | You are not responsible for enforcing compliance by third parties to 195 | this License. 196 | 197 | 7. If, as a consequence of a court judgment or allegation of patent 198 | infringement or for any other reason (not limited to patent issues), 199 | conditions are imposed on you (whether by court order, agreement or 200 | otherwise) that contradict the conditions of this License, they do not 201 | excuse you from the conditions of this License. If you cannot 202 | distribute so as to satisfy simultaneously your obligations under this 203 | License and any other pertinent obligations, then as a consequence you 204 | may not distribute the Program at all. For example, if a patent 205 | license would not permit royalty-free redistribution of the Program by 206 | all those who receive copies directly or indirectly through you, then 207 | the only way you could satisfy both it and this License would be to 208 | refrain entirely from distribution of the Program. 209 | 210 | If any portion of this section is held invalid or unenforceable under 211 | any particular circumstance, the balance of the section is intended to 212 | apply and the section as a whole is intended to apply in other 213 | circumstances. 214 | 215 | It is not the purpose of this section to induce you to infringe any 216 | patents or other property right claims or to contest validity of any 217 | such claims; this section has the sole purpose of protecting the 218 | integrity of the free software distribution system, which is 219 | implemented by public license practices. Many people have made 220 | generous contributions to the wide range of software distributed 221 | through that system in reliance on consistent application of that 222 | system; it is up to the author/donor to decide if he or she is willing 223 | to distribute software through any other system and a licensee cannot 224 | impose that choice. 225 | 226 | This section is intended to make thoroughly clear what is believed to 227 | be a consequence of the rest of this License. 228 | 229 | 8. If the distribution and/or use of the Program is restricted in 230 | certain countries either by patents or by copyrighted interfaces, the 231 | original copyright holder who places the Program under this License 232 | may add an explicit geographical distribution limitation excluding 233 | those countries, so that distribution is permitted only in or among 234 | countries not thus excluded. In such case, this License incorporates 235 | the limitation as if written in the body of this License. 236 | 237 | 9. The Free Software Foundation may publish revised and/or new versions 238 | of the General Public License from time to time. Such new versions will 239 | be similar in spirit to the present version, but may differ in detail to 240 | address new problems or concerns. 241 | 242 | Each version is given a distinguishing version number. If the Program 243 | specifies a version number of this License which applies to it and "any 244 | later version", you have the option of following the terms and conditions 245 | either of that version or of any later version published by the Free 246 | Software Foundation. If the Program does not specify a version number of 247 | this License, you may choose any version ever published by the Free Software 248 | Foundation. 249 | 250 | 10. If you wish to incorporate parts of the Program into other free 251 | programs whose distribution conditions are different, write to the author 252 | to ask for permission. For software which is copyrighted by the Free 253 | Software Foundation, write to the Free Software Foundation; we sometimes 254 | make exceptions for this. Our decision will be guided by the two goals 255 | of preserving the free status of all derivatives of our free software and 256 | of promoting the sharing and reuse of software generally. 257 | 258 | NO WARRANTY 259 | 260 | 11. BECAUSE THE PROGRAM IS LICENSED FREE OF CHARGE, THERE IS NO WARRANTY 261 | FOR THE PROGRAM, TO THE EXTENT PERMITTED BY APPLICABLE LAW. EXCEPT WHEN 262 | OTHERWISE STATED IN WRITING THE COPYRIGHT HOLDERS AND/OR OTHER PARTIES 263 | PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY OF ANY KIND, EITHER EXPRESSED 264 | OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF 265 | MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE ENTIRE RISK AS 266 | TO THE QUALITY AND PERFORMANCE OF THE PROGRAM IS WITH YOU. SHOULD THE 267 | PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF ALL NECESSARY SERVICING, 268 | REPAIR OR CORRECTION. 269 | 270 | 12. IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING 271 | WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MAY MODIFY AND/OR 272 | REDISTRIBUTE THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, 273 | INCLUDING ANY GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING 274 | OUT OF THE USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED 275 | TO LOSS OF DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY 276 | YOU OR THIRD PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER 277 | PROGRAMS), EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE 278 | POSSIBILITY OF SUCH DAMAGES. 279 | 280 | END OF TERMS AND CONDITIONS 281 | 282 | How to Apply These Terms to Your New Programs 283 | 284 | If you develop a new program, and you want it to be of the greatest 285 | possible use to the public, the best way to achieve this is to make it 286 | free software which everyone can redistribute and change under these terms. 287 | 288 | To do so, attach the following notices to the program. It is safest 289 | to attach them to the start of each source file to most effectively 290 | convey the exclusion of warranty; and each file should have at least 291 | the "copyright" line and a pointer to where the full notice is found. 292 | 293 | 294 | Copyright (C) 295 | 296 | This program is free software; you can redistribute it and/or modify 297 | it under the terms of the GNU General Public License as published by 298 | the Free Software Foundation; either version 2 of the License, or 299 | (at your option) any later version. 300 | 301 | This program is distributed in the hope that it will be useful, 302 | but WITHOUT ANY WARRANTY; without even the implied warranty of 303 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 304 | GNU General Public License for more details. 305 | 306 | You should have received a copy of the GNU General Public License 307 | along with this program; if not, write to the Free Software 308 | Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA 309 | 310 | 311 | Also add information on how to contact you by electronic and paper mail. 312 | 313 | If the program is interactive, make it output a short notice like this 314 | when it starts in an interactive mode: 315 | 316 | Gnomovision version 69, Copyright (C) year name of author 317 | Gnomovision comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 318 | This is free software, and you are welcome to redistribute it 319 | under certain conditions; type `show c' for details. 320 | 321 | The hypothetical commands `show w' and `show c' should show the appropriate 322 | parts of the General Public License. Of course, the commands you use may 323 | be called something other than `show w' and `show c'; they could even be 324 | mouse-clicks or menu items--whatever suits your program. 325 | 326 | You should also get your employer (if you work as a programmer) or your 327 | school, if any, to sign a "copyright disclaimer" for the program, if 328 | necessary. Here is a sample; alter the names: 329 | 330 | Yoyodyne, Inc., hereby disclaims all copyright interest in the program 331 | `Gnomovision' (which makes passes at compilers) written by James Hacker. 332 | 333 | , 1 April 1989 334 | Ty Coon, President of Vice 335 | 336 | This General Public License does not permit incorporating your program into 337 | proprietary programs. If your program is a subroutine library, you may 338 | consider it more useful to permit linking proprietary applications with the 339 | library. If this is what you want to do, use the GNU Library General 340 | Public License instead of this License. --------------------------------------------------------------------------------