Title: MixTwice--a Package for Large-Scale Hypothesis Testing
Author: Zihao Zheng and  Michael A.Newton
Description: Implements large-scale hypothesis testing by variance mixing. It takes two statistics per testing unit -- an estimated effect and its associated squared standard error -- and fits a nonparametric, shape-constrained mixture separately on two latent parameters. It reports local false discovery rates (lfdr) and local false sign rates (lfsr). Manuscript describing algorithm of MixTwice: Zheng et al(2020) <arXiv:2011.07420>.

Title: Large-Scale Hypothesis Testing by Variance Mixing
Imports: alabama, ashr, fdrtool, Iso, stats
Description: Implements large-scale hypothesis testing by variance mixing. It takes two statistics per testing unit -- an estimated effect and its associated squared standard error -- and fits a nonparametric, shape-constrained mixture separately on two latent parameters. It reports local false discovery rates (lfdr) and local false sign rates (lfsr). Manuscript describing algorithm of MixTwice: Zheng et al(2021) <doi: 10.1093/bioinformatics/btab162>.

Title: Missing Data Imputation for Continuous and Categorical Data
using Nonparametric Bayesian Joint Models
Description: Missing data imputation for continuous and categorical data, using nonparametric Bayesian joint models (specifically the hierarchically coupled mixture model with local dependence described in Murray and Reiter (2015); see 'citation("MixedDataImpute")' or http://arxiv.org/abs/1410.0438). See '?hcmm_impute' for example usage.
X-CRAN-Original-Maintainer: Jared S. Murray <jsmurray@stat.cmu.edu>
X-CRAN-Comment: Orphaned on 2019-08-21 as maintainer's email address

Title: Tests of Randomness and Tests of Independence
Authors@R: c( person(c("Bouchra","R."), "Nasri", role = c("aut", "cre","cph"), email = "bouchra.nasri@umontreal.ca"),
person(c("Bruno","N"), "Remillard", role = c("aut"),email = "bruno.remillard@hec.ca"),
person(c("Johanna","G"), "Neslehova", role = c("aut"),email = "johanna.neslehova@mcgill.ca"),
person(c("Christian"), "Genest", role = c("aut"),email = "christian.genest@mcgill.ca"))
Description: Functions for testing randomness for a univariate time series with arbitrary distribution  (discrete, continuous, mixture of both types) and for testing  independence between random variables with arbitrary distributions. The test statistics are based on the multilinear empirical copula and multipliers are used to compute P-values. The test of independence between random variables appeared in <doi:10.1093/biomet/asy059>.
Depends: R (>= 3.5.0), doParallel, parallel, foreach, stats
Packaged: 2021-07-29 14:07:10 UTC; Utilisateur