
 Description:
JProGraM is an opensource Java library which can be used for learning a number of statistical models from data, such as Bayesian networks, Markov random fields, hybrid random fields, probabilistic decision trees, dependency networks, Gaussian mixture models, Parzen windows, and NadarayaWatson conditional density estimators. Along with learning algorithms, some simple inference methods are also implemented. JProGraM is released under the GNU General Public License. It is not yet as polished as I would like it to be, and there is no proper documentation, but it should be especially useful for research purposes. One strong point of the library is the extended support for graphical models with continuous random variables, covering not only standard Gaussian models, but also more recent cuttingedge techniques such as nonparanormal estimation of undirected graphs, scalable dualtree recursion methods for kernel bandwidth selection (as described e.g. here), or kernelbased random fields. On the other hand, you can use JProGraM to estimate various random network models, such as the ErdősRényi, WattsStrogatz, or BarabásiAlbert model, and Markov/higherorder exponential random graphs, as well as to sample subgraphs from largescale networks (through random walk, snowball sampling, ...) or compute graph spectra. As additional tools for data analysis, principal components analysis, independent component analysis (through the FastICA library), and (to some extent) data clustering are also supported. Starting with release 12.8, a basic implementation of multilayer feedforward neural networks has been added, using the backpropagation learning algorithm.
 Changes to previous version:
JProGraM 12.8  CHANGE LOG
Release date: August , 2012
This is a major update of JProGraM. Most notable additions include:  A basic implementation of multilayer feedforward neural networks (ninofreno.ann package);  ErdősRényi, WattsStrogatz, and BarabásiAlbert random network models (ninofreno.graph.gen package);  Markov and higherorder exponential random graph models for networks, using kstar and ktriangle statistics (ninofreno.graph.ergm package);  Subgraph sampling algorithms for undirected networks (random walk, random jump, snowball, inducent and incident subgraph sampler), along with methods for computing graph spectra (ninofreno.graph package);  Area under the ROC curve calculator (ninofreno.data package);  Lots of bugfixes and enhancements.
JProGraM 10.5  CHANGE LOG
Release date: May 30, 2010
 Support for continuous graphical models has been added: Gaussian, nonparanormal, and kernelbased Markov random fields, hybrid random fields and Bayes nets are now implemented;  Routines for kernelbased conditional density estimation (NadarayaWatson estimators) have been implemented, with support for scalable dualtree recursion techniques (used in the bandwidth selection routines);  Methods for generating arbitrarily shaped multivariate density functions and for sampling datasets from them have been added;  Independent Component Analysis is now also supported (by wrapping the FastICA library);  Gaussian mixture models have been greatly improved by adding support for expectationmaximization, fixing some numerical stability issues and differentiating a simpler version with diagonal covariance matrices from a more complex version with full covariance matrices;  Other minor features have been added, and a number of corrections have been introduced.
Note that the Gaussian and nonparanormal Markov random fields relying on the graphical lasso technique require a working R distribution (http://www.rproject.org/) to be installed on your system, including in particular the external glasso package (http://wwwstat.stanford.edu/~tibs/glasso/). Provided that R and the glasso package are correctly installed, the relevant JProGraM routines are able to exploit the R installation without any manual intervention. This means that users of the JProGraM library can sinmply call the routines executing the graphical lasso within their Java code without the need to manipulate any R code. (This has been tested successfully on several Linux distributions, but not on Windows or Mac OS X).
JProGraM 9.1  CHANGE LOG
Release date: January 29, 2009
 Principal Components Analysis is now supported;  A number of bugs within the ninofreno.gmm and ninofreno.clustering packages have been fixed;  Other minor features have been added (especially within the MyMath class).
JProGraM 8.10  CHANGE LOG
Release date: October 7, 2008
The following algorithms are now supported by JProGraM:  KMeans (for clustering);  KaufmanRousseuw algorithm for initializing cluster centroids;  Gaussian Mixture Model for probability density function estimation.
JProGraM 8.6  CHANGE LOG
Release date: June 8, 2008
The following statistical models are now supported by JProGraM:  Parzen Windows for probability density function estimation;  Probabilistic decision trees for discrete pattern classification;  Dependency networks for discrete pseudolikelihood estimation.
JProGraM 8.1
Release date: February 16, 2008
The following statistical models are supported by JProGraM:  Bayesian networks;  Markov random fields;  Hybrid random fields.
 BibTeX Entry: Download
 Corresponding Paper BibTeX Entry: Download
 URL: Project Homepage
 Supported Operating Systems: Agnostic
 Data Formats: None
 Tags: Density Estimation, Machine Learning, Decision Tree Learning, Naive Bayes, Bayesian Networks, Graphical Models, Markov Random Fields, Dependency Networks, Hybrid Random Fields, Ne
 Archive: download here
Other available revisons

Version Changelog Date 13.2 JProGraM 13.2  CHANGE LOG
Release date: February 13, 2012
New features:  Support for Fiedler random graphs/random field models for largescale networks (ninofreno.graph.fiedler package);  Various bugfixes and enhancements (especially in the ninofreno.graph and ninofreno.math package).
February 13, 2013, 20:29:38 12.8 JProGraM 12.8  CHANGE LOG
Release date: August , 2012
This is a major update of JProGraM. Most notable additions include:  A basic implementation of multilayer feedforward neural networks (ninofreno.ann package);  ErdősRényi, WattsStrogatz, and BarabásiAlbert random network models (ninofreno.graph.gen package);  Markov and higherorder exponential random graph models for networks, using kstar and ktriangle statistics (ninofreno.graph.ergm package);  Subgraph sampling algorithms for undirected networks (random walk, random jump, snowball, inducent and incident subgraph sampler), along with methods for computing graph spectra (ninofreno.graph package);  Area under the ROC curve calculator (ninofreno.data package);  Lots of bugfixes and enhancements.
JProGraM 10.5  CHANGE LOG
Release date: May 30, 2010
 Support for continuous graphical models has been added: Gaussian, nonparanormal, and kernelbased Markov random fields, hybrid random fields and Bayes nets are now implemented;  Routines for kernelbased conditional density estimation (NadarayaWatson estimators) have been implemented, with support for scalable dualtree recursion techniques (used in the bandwidth selection routines);  Methods for generating arbitrarily shaped multivariate density functions and for sampling datasets from them have been added;  Independent Component Analysis is now also supported (by wrapping the FastICA library);  Gaussian mixture models have been greatly improved by adding support for expectationmaximization, fixing some numerical stability issues and differentiating a simpler version with diagonal covariance matrices from a more complex version with full covariance matrices;  Other minor features have been added, and a number of corrections have been introduced.
Note that the Gaussian and nonparanormal Markov random fields relying on the graphical lasso technique require a working R distribution (http://www.rproject.org/) to be installed on your system, including in particular the external glasso package (http://wwwstat.stanford.edu/~tibs/glasso/). Provided that R and the glasso package are correctly installed, the relevant JProGraM routines are able to exploit the R installation without any manual intervention. This means that users of the JProGraM library can sinmply call the routines executing the graphical lasso within their Java code without the need to manipulate any R code. (This has been tested successfully on several Linux distributions, but not on Windows or Mac OS X).
JProGraM 9.1  CHANGE LOG
Release date: January 29, 2009
 Principal Components Analysis is now supported;  A number of bugs within the ninofreno.gmm and ninofreno.clustering packages have been fixed;  Other minor features have been added (especially within the MyMath class).
JProGraM 8.10  CHANGE LOG
Release date: October 7, 2008
The following algorithms are now supported by JProGraM:  KMeans (for clustering);  KaufmanRousseuw algorithm for initializing cluster centroids;  Gaussian Mixture Model for probability density function estimation.
JProGraM 8.6  CHANGE LOG
Release date: June 8, 2008
The following statistical models are now supported by JProGraM:  Parzen Windows for probability density function estimation;  Probabilistic decision trees for discrete pattern classification;  Dependency networks for discrete pseudolikelihood estimation.
JProGraM 8.1
Release date: February 16, 2008
The following statistical models are supported by JProGraM:  Bayesian networks;  Markov random fields;  Hybrid random fields.
August 7, 2012, 15:08:07 10.5 JProGraM 10.5  CHANGE LOG
Release date: May 30, 2010
 Support for continuous graphical models has been added: Gaussian, nonparanormal, and kernelbased Markov random fields, hybrid random fields and Bayes nets are now implemented;  Routines for kernelbased conditional density estimation (NadarayaWatson estimators) have been implemented, with support for scalable dualtree recursion techniques (used in the bandwidth selection routines);  Methods for generating arbitrarily shaped multivariate density functions and for sampling datasets from them have been added;  Independent Component Analysis is now also supported (by wrapping the FastICA library);  Gaussian mixture models have been greatly improved by adding support for expectationmaximization, fixing some numerical stability issues and differentiating a simpler version with diagonal covariance matrices from a more complex version with full covariance matrices;  Other minor features have been added, and a number of corrections have been introduced.
Note that the Gaussian and nonparanormal Markov random fields relying on the graphical lasso technique require a working R distribution (http://www.rproject.org/) to be installed on your system, including in particular the external glasso package (http://wwwstat.stanford.edu/~tibs/glasso/). Provided that R and the glasso package are correctly installed, the relevant JProGraM routines are able to exploit the R installation without any manual intervention. This means that users of the JProGraM library can sinmply call the routines executing the graphical lasso within their Java code without the need to manipulate any R code. (This has been tested successfully on several Linux distributions, but not on Windows or Mac OS X).
JProGraM 9.1  CHANGE LOG
Release date: January 29, 2009
 Principal Components Analysis is now supported;  A number of bugs within the ninofreno.gmm and ninofreno.clustering packages have been fixed;  Other minor features have been added (especially within the MyMath class).
JProGraM 8.10  CHANGE LOG
Release date: October 7, 2008
The following algorithms are now supported by JProGraM:  KMeans (for clustering);  KaufmanRousseuw algorithm for initializing cluster centroids;  Gaussian Mixture Model for probability density function estimation.
JProGraM 8.6  CHANGE LOG
Release date: June 8, 2008
The following statistical models are now supported by JProGraM:  Parzen Windows for probability density function estimation;  Probabilistic decision trees for discrete pattern classification;  Dependency networks for discrete pseudolikelihood estimation.
JProGraM 8.1
Release date: February 16, 2008
The following statistical models are supported by JProGraM:  Bayesian networks;  Markov random fields;  Hybrid random fields.
May 31, 2010, 12:14:02 9.1 Initial Announcement on mloss.org.
June 13, 2008, 16:03:47
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