Suggests: coda, knitr, rmarkdown, ggthemes, gridExtra, Cairo, extrafont

Depends: dplyr (>= 0.4.3), tidyr (>= 0.3.1), ggplot2

Depends: dplyr (>= 0.5.0), tidyr (>= 0.5.1), ggplot2
Suggests: coda, rstan (>= 2.10.1), knitr, rmarkdown, ggthemes,
X-CRAN-Original-Maintainer: Xavier Fernández i Marín

Authors@R: person("Xavier", "Fernández i Marín", email = "xavier.fim@gmail.com", role = c("aut", "cre"))
Suggests: coda, rstan (>= 2.10.1), nimble, knitr, rmarkdown, ggthemes,
Collate: 'data.R' 'functions.R' 'ggmcmc.R' 'ggs.R' 'ggs_Rhat.R'

results against observed variables, and functions to work with
Authors@R: person("Xavier", "Fernández i Marín", email = "xavier.fim@gmail.com", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-9522-8870"))
Suggests: coda, rstan (>= 2.17.1), nimble, knitr, rmarkdown, ggthemes,

Title: Visualizations for 'mice' with 'ggplot2'
person("Hanne", "Oberman", , "h.i.oberman@uu.nl", role = c("aut", "cre"),
Description: Enhance a 'mice' imputation workflow with visualizations for
incomplete and/or imputed data. The plotting functions produce
'ggplot' objects which may be easily manipulated or extended. Use
'ggmice' to inspect missing data, develop imputation models, evaluate
algorithmic convergence, or compare observed versus imputed data.
Imports: dplyr, ggplot2, magrittr, mice, purrr, rlang, stats, stringr,
Suggests: covr, knitr, plotly, rmarkdown, testthat (>= 3.0.0)
Copyright: person("ggmice authors", role = "cph"), person("Utrecht
University", role = "fnd"), person("University Medical Centre
Author: Hanne Oberman [aut, cre] (<https://orcid.org/0000-0003-3276-2141>)

Title: Variable Selection in Linear Mixed Models for SNP Data
role = c("aut", "cre"), comment = "https://sahirbhatnagar.com/"),
person("Karim", "Oualkacha", email = "oualkacha.karim@uqam.ca",
role = c("aut"), comment = "http://karimoualkacha.uqam.ca/"),
role = c("aut"), comment = "http://www.math.mcgill.ca/yyang/"),
person("Celia", "Greenwood", email = "celia.greenwood@mcgill.ca",
role = c("aut"), comment = "http://www.mcgill.ca/statisticalgenetics/")
Description: Fit penalized multivariable linear mixed models with a single
random effect to control for population structure in genetic association
studies. The goal is to simultaneously fit many genetic variants at the
same time, in order to select markers that are independently associated
with the response. Can also handle prior annotation information,
for example, rare variants, in the form of variable weights. For more
information, see the website below and the accompanying paper:
Bhatnagar et al., "Simultaneous SNP selection and adjustment for
population structure in high dimensional prediction models", 2020,
Imports: glmnet, methods, stats, MASS, Matrix
Suggests: RSpectra, popkin, bnpsd, testthat, covr, knitr, rmarkdown
Author: Sahir Bhatnagar [aut, cre] (https://sahirbhatnagar.com/),