2. ORCME- ORCME project develops a package for order restricted clustering of microarray experiments. It reflects on-going research of the bioinformatics group within the Center for Statistics at UHasselt.
5. BayesSurv- This package estimates mortality with covariates from capture-recapture/recovery data in a Bayesian framework when many individuals are of unknown age. It includes tools for data checking, model diagnostics and outputs such as life-tables and plots.
6. Gauss-Seidel Estimation of GLMM- This project implements a block-wise Gauss-Seidel (GS) algorithm to fit Generalized Linear Mixed Models (GLMMs). The GS algorithm allows for a significant performance increase and memory saving.
8. SPecies' LImits by Threshold Statistics- splits contains tools for delimiting species and automated taxonomy at many levels of biological organization (eg. DNA barcoding, morphometrics), top-down (merging phylogenetic and phylogeographic methods) and bottom-up (single samples into >1 groups).
10. Quantitative Fitness Analysis- QFA is an experimental and computational methodology for estimation of the fitnesses of arrayed microbial cultures growing on solid agar. Methods for estimating fitnesses, fitness variability and genetic interaction strengths are included.
11. sampSurf - Sampling surface simulation- This package will allow the estimation of sampling surfaces over a spatial extent for use in assessing different areal sampling methods used in forestry and ecology.
14. Analysis of Independent Contrasts- The CAIC package provides methods for the comparative analysis of taxa using independent contrasts methods. It reimplements the methods of the CAIC and MacroCAIC programs will implement alternative methods such as those provided in LDAP.
17. semPLS- This package offers an implementation to fit structural equatation models (SEM) by the partial least squares (PLS) algorithm. The PLS approach is referred to as 'soft-modeling' technique and requires no distributional assumptions on the observed data.
18. pfda: Functional Principal Components- The PFDA package for R performs functional principal component analysis with B-splines for univariate and bivariate responses. Also handled are binary responses and an additive model for extra variables.