Assertive Programming for R Analysis Pipelines
person("Tony", "Fischetti", email="tony.fischetti@gmail.com",
role = c("aut", "cre"))
Provides functionality to assert conditions
that have to be met so that errors in data used in
analysis pipelines can fail quickly. Similar to
'stopifnot()' but more powerful, friendly, and easier
for use in pipelines.

person("Tony", "Fischetti",
email="tony.fischetti@gmail.com",
role = c("aut", "cre"))
dplyr (>= 0.7.0), MASS, lazyeval, stats, utils, rlang (>=
0.1.2)

dplyr (>= 0.7.0), MASS, stats, utils, rlang (>= 0.3.0)

dplyr (>= 0.7.0), MASS, methods, stats, utils, rlang (>=
0.3.0)
knitr, testthat, magrittr, rmarkdown, tibble, plyr

assertthat is an extension to stopifnot() that makes it
easy to declare the pre and post conditions that you code should
satisfy, while also producing friendly error messages so that your
users know what they've done wrong.
'assert-that.r' 'on-failure.r' 'assertions-file.r'
'assertions-scalar.R' 'assertions.r' 'base.r'
'base-comparison.r' 'base-is.r' 'base-logical.r' 'base-misc.r'
'utils.r' 'validate-that.R'

person("Hadley", "Wickham", , "hadley@rstudio.com", c("aut", "cre"))

An extension to stopifnot() that makes it easy to declare
the pre and post conditions that you code should satisfy, while also
producing friendly error messages so that your users know what's gone
wrong.

Population Assignment using Genomic, Non-Genetic or Integrated
Data in a Machine-Learning Framework
Perform population assignment using a machine learning framework. It employs supervised machine learning methods to evaluate the discriminatory power of your known data set, and is capable of analyzing large genetic, non-genetic, or integrated (genetic plus non-genetic) data sets. This framework is also designed for solving the upwardly biased issue that was discussed in previous studies.
caret, doParallel, e1071, foreach, ggplot2, MASS, parallel,
randomForest, reshape2, stringr, tree,

Population Assignment using Genetic, Non-Genetic or Integrated
Data in a Machine Learning Framework
Kuan-Yu (Alex) Chen [aut, cre], Elizabeth A. Marschall [aut], Michael G. Sovic [aut], Anthony C. Fries [aut], H. Lisle Gibbs [aut], Stuart A. Ludsin [aut]
Use Monte-Carlo and K-fold cross-validation coupled with machine-learning classification algorithms to perform population assignment, with functionalities of evaluating discriminatory power of independent training samples, identifying informative loci, reducing data dimensionality for genomic data, integrating genetic and non-genetic data, and visualizing results.
gtable, iterators, klaR, stringi, knitr, rmarkdown