Our software

Our group develops software, either as part of research projects where new methods are developed, or as newer and efficient implementations of existing methods. Mostly we implement methods as R packages, which as R are open source and free. Packages can be downloaded and installed in any computer where R is installed.

Here we give a summary of available software developed by our group, with links to where software packages themselves can be found. Links to additional packages, including those developed by former group members, are available from the personal web site of Wessel van Wieringen (link).

Software packages


A software package for (integrative and meta-analytic) network modeling and analysis.


ShrinkBayes: Bayesian differential expression analysis for RNAseq, microarray or RNAi-screens data.


A software package for the joint analysis of high-throughput data from multiple molecular levels.


GRridge: Better prediction by use of co-data: adaptive group-regularized regression.


Several packages for analysing DNA copy number data, in collaboration with the Tumor Genome Analysis Core.


globalSeq: global test for counts; testing for association between RNA-Seq and high-dimensional data.


semisup: semi-supervised mixture model; detecting SNPs with interactive effect on a quantitative trait.


The package reconstructs and exploits networks from time course data.


R-package to estimate group-regularized logistic elastic net regression models for high-dimensional data, with the inclusion of external information.


palasso: paired lasso; sparse regression with paired covariates.


Use the ecpc method to flexibly learn from multiple and various co-data to improve prediction and covariate selection.


Use the squeezy R-package for efficient estimation of penalties in group-regularised elastic net models for linear and logistic regression in high-dimensional data settings.


Multi-penalty linear, logistic and cox ridge regression, including estimation of the penalty parameters by efficient (repeated) cross-validation and marginal likelihood maximization.  CRAN package