stats, utils, mgcv, grDevices, graphics, openxlsx

c(person("Yi", "Ya", role=c("aut","cre"),email="Yi.YA_yaya@hotmail.com"),
person("Jacob", "Zhang", role="aut"))
Three main functions about analyzing massive data (missing observations are allowed) considered from multiple layers of categories.

Marie Du-Roy-De-Chaumary, Matthieu Marbac and Fabien Navarro
Estimation of the latent states and partition by maximum likelihood. Model can be used for analyzing accelerometer data. In such a case, the latent states corresponds to activity levels and the partition permits to consider heterogeneity within the population. Emission laws are zero-inflated gamma distributions. Their parameters depends on the latent states but not on the partition, to compare the time spent by activity levels between classes. Model description is available in Du Roy de Chaumaray, M. and Marbac, M. and Navarro, F. (2019) <arXiv:1906.01547>.
Rcpp (>= 0.11.1), methods, parallel, ggplot2, reshape2,
gridExtra
'param.R' 'checkinputs.R' 'rmodel.R' 'data.R' 'MHMM.R'
'results.R' 'plot.R' 'smartinit.R' 'viterbi.R' 'MHMMpackage.R'
'RcppExports.R'

Tests Controlling the FDR / FWER under Certain Copula Models
c(
person("Jonathan", "von Schroeder", email = "jvs@uni-bremen.de", role = c("aut","cre")),
person("Taras", "Bodnar", email = "bodnar@math.hu-berlin.de", role = "aut"),
person("Jens","Stange", email= "Jens.Stange@wias-berlin.de", role = "aut"))
Implements tests controlling the false discovery rate (FDR) / family-wise error rate (FWER) for some copula models.
stats, copula, matrixStats, mvtnorm, stabledist, MCMCpack
Jonathan von Schroeder [aut, cre],
Taras Bodnar [aut],
Jens Stange [aut]

Multiple Hypotheses Testing for Discrete Data
A Comprehensive tool for almost all existing multiple testing methods for discrete data. The package also provides some novel multiple testing procedures controlling
FWER/FDR for discrete data. In order to make decisions conveniently,  given discrete p-values and their domains, the [method].p.adjust function returns adjusted p-values, which can be used to compare with the nominal significant level alpha.