c(person("Yi", "Guo", role = c("aut","cre"),
email = "y.guo@westernsydney.edu.au"),
person("Mark", "Berman", role = "aut",
email = "mark.berman@data61.csiro.au"))
Group subset selection for linear regression models is provided in this package. Given response variable, and explanatory variables, which are organised in groups, group subset selection selects a small number of groups to explain response variable linearly using least squares.

c(
person("Jack", "Freestone", email = "jfre0619@uni.sydney.edu.au", role = c("aut", "cre", "cph")),
person("Uri", "Keich", email = "uri.keich@sydney.edu.au", role = c("aut", "cph")))
A procedure that allows the user to reject multiple hypotheses in the presence of group structure, while controlling the false discovery rate (FDR).
Jack Freestone [aut, cre, cph],
Uri Keich [aut, cph]

A procedure that uses target-decoy competition (or knockoffs) to reject multiple hypotheses in the presence of group structure. The procedure controls the false discovery rate (FDR) at a user-specified threshold.

Wavelet Functional ANOVA Through Markov Groves
Functional denoising and functional ANOVA through wavelet-domain
Markov groves. Fore more details see: Ma L. and Soriano J. (2016)
Efficient functional ANOVA through wavelet-domain Markov groves.
<arXiv:1602.03990v2 [stat.ME]>.

Bayesian semiparametric growth curve models that additionally
include multiple membership random effects.
Employs a Dirichlet Process (DP) prior on the set of
by-subject random effect parameters under repeated waves of
measurements to allow the number of random effect parameters
specified per subject, \code{q}, to be equal to the number of
measurement waves, \code{T}.  Random effects are grouped by
subject and all \code{q} parameters receive the DP prior.  An
additional set of random effects are included that utilize a
different grouping factor; e.g. treatment(s) exposure or
dosage.  These additional random effects are mapped back to
subjects through a multiple membership weight matrix.
Rcpp, RcppArmadillo, reshape2, scales, ggplot2, Formula,
testthat
'summary_quantiles.R' 'mcmcPlots.R' 'relabel.R' 'getmf.R'
'XZcov.R' 'growthCurve.R' 'dpgrowmm.R' 'dpgrow.R' 'help.R'
'growplot.R' 'trtplot.R' 'datsim.R' 'datsimcov.R'
'datsimmult.R' 'dpgrowmult.R' 'effectsplot.R' 'datbrghtterms.R'