Aligned Rank Transform for Nonparametric Factorial Analysis
An implementation of the Aligned Rank Transform technique for
factorial analysis (see references below for details) including models with
missing terms (unsaturated factorial models). The function first
computes a separate aligned ranked response variable for each effect of the
user-specified model, and then runs a classic ANOVA on each of the aligned
ranked responses. For further details, see Higgins, J. J. and Tashtoush, S.
(1994). An aligned rank transform test for interaction. Nonlinear World 1
(2), pp. 201-211. Wobbrock, J.O., Findlater, L., Gergle, D. and
Higgins,J.J. (2011). The Aligned Rank Transform for nonparametric factorial
analyses using only ANOVA procedures. Proceedings of the ACM Conference on
Human Factors in Computing Systems (CHI '11). New York: ACM Press, pp.
143-146. <doi:10.1145/1978942.1978963>.

Infer a time-varying DBN network from time series data.
This package generates 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).

This package generates 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).