Fit a Grey-Zone Model with Survival Data (greyzoneSurv)
Allows one to classify patients into low, intermediate, and high risk groups for disease progression based on a continuous marker that is associated with progression-free survival. It uses a latent class model to link the marker and survival outcome and produces two cutoffs for the marker to divide patients into three groups. See the References section for more details.

Interactive JS Charts from R http://rcharts.io (rCharts)
rCharts is an R package to create, customize and publish interactive javascript visualizations from R using a familiar lattice style plotting interface.

Time-Varying DBN Inference with the ARTIVA (Auto Regressive TIme VArying) Model (ARTIVA)
Reversible Jump MCMC (RJ-MCMC)sampling for approximating the posterior distribution of a time varying regulatory network, under the Auto Regressive TIme VArying (ARTIVA) model (for a detailed description of the algorithm, see Lebre et al. BMC Systems Biology, 2010). Starting from time-course gene expression measurements for a gene of interest (referred to as ‘target gene’) and a set of genes (referred to as ‘parent genes’) which may explain the expression of the target gene, the ARTIVA procedure identifies temporal segments for which a set of interactions occur between the ‘parent genes’ and the ‘target gene’. The time points that delimit the different temporal segments are referred to as changepoints (CP).

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