ape, ggthemes, knitr, philr, propr, reshape2, rmarkdown,
robCompositions, testthat, vegan

Access to Current and Historical Basketball Data
person("Ryan", "Elmore", email = "rtelmore@gmail.com",
role = c("aut", "cre"))
Provides simple functions for accessing data/tables on
<http://www.basketball.reference.com>.
dplyr (>= 0.7.0), lubridate, magrittr, rlang, rvest, XML,
xml2, janitor, ggplot2, scales, devtools

c(
person("Ryan", "Elmore", email = "rtelmore@gmail.com", role = c("cre", "aut")),
person("Peter", "DeWitt", email = "dewittpe@gmail.com", role = "ctb"))
Provides simple functions for accessing data/tables on
<http://www.basketball-reference.com>.
dplyr (>= 0.7.0), janitor, lubridate, magrittr, rlang, rvest,
xml2
covr, devtools, knitr, ggplot2, rmarkdown, scales, XML
Ryan Elmore [cre, aut],
Peter DeWitt [ctb]

covr, devtools, knitr, ggplot2, rmarkdown, scales

High Dimensional Bayesian Mediation Analysis
c(person(given = "Alexander",
family = "Rix",
role = c("aut", "cre"),
email = "alexrix@umich.edu"),
person(given = "Yanyi",
family = "Song",
role = c("aut"),
email = "yanys@umich.edu"))
Perform mediation analysis in the presence of high-dimensional
mediators based on the potential outcome framework. Bayesian Mediation
Analysis (BAMA), developed by Song et al (2018) <doi:10.1101/467399>, relies
on two Bayesian sparse linear mixed models to simultaneously analyze a
relatively large number of mediators for a continuous exposure and outcome
assuming a small number of mediators are truly active. This sparsity
assumption also allows the extension of univariate mediator analysis by
casting the identification of active mediators as a variable selection
problem and applying Bayesian methods with continuous shrinkage priors on
the effects.
Alexander Rix [aut, cre],
Yanyi Song [aut]

Perform mediation analysis in the presence of high-dimensional
mediators based on the potential outcome framework. Bayesian Mediation
Analysis (BAMA), developed by Song et al (2019) <doi:10.1111/biom.13189>,
relies on two Bayesian sparse linear mixed models to simultaneously analyze
a relatively large number of mediators for a continuous exposure and outcome
assuming a small number of mediators are truly active. This sparsity
assumption also allows the extension of univariate mediator analysis by
casting the identification of active mediators as a variable selection
problem and applying Bayesian methods with continuous shrinkage priors on
the effects.