person('David', 'Mayer', email = 'david.mayer@cuanschutz.edu', role = c('cre', 'aut'), comment = c(ORCID = "0000-0002-6056-9771")),
magrittr, purrr, redcapAPI, REDCapR, rlang (>= 0.4.7),
Suggests: fs, gt, here, htmltools, knitr, pkgload, processx, readr,
rmarkdown, rstudioapi, spelling, testthat (>= 2.1.0), usethis

Description: Interaction with "RevBayes" via R. Objects created in "RevBayes" can be passed into the R environment, and many types can be converted into similar R objects. To download "RevBayes", go to <https://revbayes.github.io/download>.
Imports: ape, stringr, stringi, knitr, utils, comprehenr
Packaged: 2021-08-10 22:22:50 UTC; ubuntutest

Imports: ape, stringr, stringi, knitr, utils, comprehenr, phytools
Suggests: markdown, rmarkdown, testthat, ggplot2, devtools
Packaged: 2021-08-14 00:51:01 UTC; ubuntutest

Imports: ape, stringr, knitr, utils, comprehenr, jsonlite, usethis,
Suggests: markdown, rmarkdown, testthat, ggplot2, devtools, RevGadgets,

Authors@R: person("Daniel", "Szeredi", email = "daniel@szeredi.hu", role = c("aut", "cre"))
Description: Provides convenience functions to communicate with
an Experigen server. It is especially helpful with registered experiments, as
authentication with the server has to happen.

an Experigen server: Experigen (<http://github.com/aquincum/experigen>)
is an online framework for creating  linguistic experiments,
and it stores the results on a dedicated server. This package can be
used to retrieve the results from the server, and it is especially
helpful with registered experiments, as authentication with the server

Title: Multiple Imputation using Chained Random Forests
Authors@R: c(person("Shangzhi", "Hong", role = c("aut", "cre"),
person("Henry S.", "Lynn", role = c("ths")))
Description: Functions for methods for multiple imputation using chained random
forests. Implemented algorithms can handle missing data in both continuous
and categorical variables by using prediction-based or node-based
conditional distributions constructed using random forests. For
prediction-based imputation, the method based on the empirical distribution
of out-of-bag prediction errors of random forests and the method based on
normality assumption are provided. For node-based imputation, the method
based on the conditional distribution formed by predicting nodes of random
forests and the method based on measures of proximities of random forests
are provided. More details of the statistical methods can be found in