Bayesian Variable Selection Methods for Multivariate Data
Kyu Ha Lee, Mahlet G. Tadesse, Brent A. Coull, Jacqueline R. Starr
Bayesian variable selection methods for data with multivariate responses and multiple covariates. The package contains implementations of multivariate Bayesian variable selection methods for continuous data and zero-inflated count data.

Bayesian variable selection methods for data with multivariate responses and multiple covariates. The package contains implementations of multivariate Bayesian variable selection methods for continuous data (Lee et al., Biometrics, 2017 <doi: 10.1111/biom.12557>) and zero-inflated count data (Lee et al., Biostatistics, 2020 <doi:10.1093/biostatistics/kxy067>).

Inferring Causal Network from Mixed Observational Data Using a
Directed Acyclic Graph
Learning a mixed directed acyclic graph based on both continuous and categorical data.
Rcpp (>= 0.12.14), pcalg, mgm, bnlearn, methods, nnet

Wujuan Zhong, Li Dong, Quefeng Li, Xiaojing Zheng

The method 'm:Explorer' associates a given list of target genes (e.g. those involved in a biological process) to gene regulators such as transcription factors. Transcription factors that bind DNA near significantly many target genes or correlate with target genes in transcriptional (microarray or RNAseq data) are selected. Selection of candidate master regulators is carried out using multinomial regression models, likelihood ratio tests and multiple testing correction. Reference: m:Explorer: multinomial regression models reveal positive and negative regulators of longevity in yeast quiescence. Juri Reimand, Anu Aun, Jaak Vilo, Juan M Vaquerizas, Juhan Sedman and Nicholas M Luscombe. Genome Biology (2012) 13:R55 <doi:10.1186/gb-2012-13-6-r55>.
Identifying Master Gene Regulators from Gene Expression and
DNA-Binding Data

Compute and Illustrate the Multiple Facets of Functional
Diversity
c(
person(given   = "Camille",
family  = "Magneville",
email   = "camille.magneville@gmail.com",
role    = c("aut", "cre", "cph"),
comment = c(ORCID = "0000-0003-0489-3822")),
person(given   = "Nicolas",
family  = "Loiseau",
email   = "nicolas.loiseau1@gmail.com",
role    = "aut",
comment = c(ORCID = "0000-0002-2469-1980")),
person(given   = "Camille",
family  = "Albouy",
email   = "albouycamille@gmail.com",
role    = "aut",
comment = c(ORCID = "0000-0003-1629-2389")),
person(given   = "Nicolas",
family  = "Casajus",
email   = "nicolas.casajus@fondationbiodiversite.fr",
role    = "aut",
comment = c(ORCID = "0000-0002-5537-5294")),
person(given   = "Thomas",
family  = "Claverie",
email   = "tclaverie@gmail.com",
role    = "aut",
comment = c(ORCID = "0000-0002-6258-4991")),
person(given   = "Arthur",
family  = "Escalas",
email   = "arthur.escalas@gmail.com",
role    = "aut",
comment = c(ORCID = "0000-0002-6450-5716")),
person(given   = "Fabien",
family  = "Leprieur",
email   = "fabien.leprieur@umontpellier.fr",
role    = "aut",
comment = c(ORCID = "0000-0001-6869-342X")),
person(given   = "Eva",
family  = "Maire",
email   = "emg.maire@gmail.com",
role    = "aut",
comment = c(ORCID = "0000-0002-1032-3394")),
person(given   = "David",
family  = "Mouillot",
email   = "david.mouillot@umontpellier.fr",
role    = "aut",
comment = c(ORCID = "0000-0003-0402-2605")),
person(given   = "Sebastien",
family  = "Villeger",
email   = "sebastien.villeger@cnrs.fr",
role    = "aut",
comment = c(ORCID = "0000-0002-2362-7178")))
Computing functional traits-based distances between pairs of
species for species gathered in assemblages allowing to build several
functional spaces. The package allows to compute functional diversity
indices assessing the distribution of species (and of their dominance) in a
given functional space for each assemblage and the overlap between
assemblages in a given functional space, see: Chao et al. (2018)
<doi:10.1002/ecm.1343>, Maire et al. (2015) <doi:10.1111/geb.12299>,
Mouillot et al. (2013) <doi:10.1016/j.tree.2012.10.004>, Mouillot et al.
(2014) <doi:10.1073/pnas.1317625111>, Ricotta and Szeidl (2009)
<doi:10.1016/j.tpb.2009.10.001>. Graphical outputs are included.
Visit the 'mFD' website for more information, documentation and examples.
ade4, ape, betapart (>= 1.5.4), cluster, dendextend,
FactoMineR, gawdis, geometry, ggplot2, ggrepel, grid, Hmisc,
patchwork, reshape2, rstatix, stats, utils, vegan
dplyr (>= 1.0.6), knitr, magrittr, rmarkdown (>= 2.6),
stringr, tibble, tidyr