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5 projects in result set.
Finite Mixure of AFT and FMR models  FMRS package provides estimation and variable selection in Finite Mixture of Accelerated Failure Time Regression (FMAFTR) and Finite Mixture of Regression (FMR) models with a large number of covariates and/or right censoring and heterogeneous structure.  
Tags: Bioinformatics, Biostatistics, C++, Cancer, Medical Science, Mixture, Next generation Sequencing, R, Regression, Statistics, lasso, model estimation, model selection, penalized regression, regularization, survival, tuning parameters, variable selection  

Activity Percentile: 0 Activity Ranking: 0 Registered: 20160307 02:31 
FusedANOVA  This package adjusts a penalized ANOVA model with FusedLASSO (or Total Variation) penality, i.e. a sum of weighted l1norm on the difference of each coefficient. The fitting procedure is accompanied with a highly efficient crossvalidation method.  
Tags: sparse method, penalized regression, crossvalidation, classification  
This project has not yet categorized itself in the Trove Software Map  Activity Percentile: 0 Activity Ranking: 0 Registered: 20130926 07:58 
MPAgenomics  This package contains an implementation of the lars algorithm for the lasso and fusion penalization. It works even if the number of covariate is greater than the number of individuals.  
Tags: lasso, lars, sparse linear model, C++, genomic, Bioinformatics, Multivariate Analysis, Multivariate Regression, high dimentional data, low sample size high dimensional data, penalized regression  

Activity Percentile: 0 Activity Ranking: 0 Registered: 20130418 16:20 
Sparsity by Quadratic Penalties  This package fits classical sparse regression models with efficient active set algorithms by solving quadratic problems. Also provides a few methods for model selection purpose (crossvalidation, stability selection).  
Tags: multivariate regression, sparse method, penalized regression, feature selection, crossvalidation, stability selection, elasticnet  

Activity Percentile: 0 Activity Ranking: 0 Registered: 20130321 09:55 
therese  The purpose of this package is to propose a method for inferring networks from expression data obtained in various experimental condition. The "cLasso" (consensus Lasso) method is implemented.  
Tags: microarray, multivariate regression, network, penalized regression  

Activity Percentile: 0 Activity Ranking: 0 Registered: 20130705 08:44 