Imports: assertive.base (>= 0.0-7), assertive.properties, codetools,
Suggests: testthat, data.table, dplyr, xml2
Packaged: 2016-12-30 17:23:20 UTC; richierocks

Title: Assertive Programming for R Analysis Pipelines
Description: 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
Packaged: 2015-03-24 03:17:54 UTC; tonyfischetti

Packaged: 2015-03-21 19:02:19 UTC; tonyfischetti

Packaged: 2015-06-26 06:30:51 UTC; tonyfischetti

Packaged: 2016-08-02 03:24:52 UTC; tonyfischetti

Imports: dplyr, MASS, lazyeval, stats, utils
Packaged: 2017-03-17 01:27:28 UTC; tonyfischetti

Packaged: 2017-05-31 17:28:49 UTC; tonyfischetti

Packaged: 2017-06-05 17:31:06 UTC; tonyfischetti

Packaged: 2017-03-23 03:45:36 UTC; tonyfischetti

Imports: dplyr (>= 0.7.0), MASS, lazyeval, stats, utils, rlang (>=
Packaged: 2018-02-23 21:26:36 UTC; tonyfischetti

Imports: dplyr (>= 0.7.0), MASS, stats, utils, rlang (>= 0.3.0)
Packaged: 2019-01-22 20:24:57 UTC; tonyfischetti

Packaged: 2020-02-05 21:17:23 UTC; tonyfischetti

Suggests: knitr, testthat, magrittr, rmarkdown

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

Authors@R: 'Hadley Wickham <h.wickham@gmail.com> [aut,cre]'
Description: 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

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

Description: 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

Title: Population Assignment using Genomic, Non-Genetic or Integrated
Maintainer: Kuan-Yu Alex Chen <alexkychen@gmail.com>
Description: 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.
Imports: caret, doParallel, e1071, foreach, ggplot2, MASS, parallel,
Suggests: gtable, iterators, klaR, stringi,