Package: vtreat
Type: Package
Title: A Statistically Sound 'data.frame' Processor/Conditioner
Version: 1.2.0
Date: 2018-06-19
Authors@R: c(
    person("John", "Mount", email = "jmount@win-vector.com", role = c("aut", "cre")),
    person("Nina", "Zumel", email = "nzumel@win-vector.com", role = c("aut")),
    person(family = "Win-Vector LLC", role = c("cph"))
    )
URL: https://github.com/WinVector/vtreat/,
        https://winvector.github.io/vtreat/
BugReports: https://github.com/WinVector/vtreat/issues
Maintainer: John Mount <jmount@win-vector.com>
Description: A 'data.frame' processor/conditioner that prepares real-world data for predictive modeling in a statistically sound manner.
    'vtreat' prepares variables so that data has fewer exceptional cases, making
    it easier to safely use models in production. Common problems 'vtreat' defends
    against: 'Inf', 'NA', too many categorical levels, rare categorical levels, and new
    categorical levels (levels seen during application, but not during training). Reference: 
    "'vtreat': a data.frame Processor for Predictive Modeling", 'Zumel', 'Mount', 2016, DOI:10.5281/zenodo.1173314.
License: GPL-3
Depends: R (>= 3.2.1)
Imports: stats, wrapr (>= 1.5.0)
Suggests: rquery (>= 0.5.0), rqdatatable, testthat, knitr, parallel,
        rmarkdown, data.table, ggplot2, DBI, RSQLite, datasets
LazyData: true
VignetteBuilder: knitr
RoxygenNote: 6.0.1
ByteCompile: true
NeedsCompilation: no
Packaged: 2018-06-19 14:16:57 UTC; johnmount
Author: John Mount [aut, cre],
  Nina Zumel [aut],
  Win-Vector LLC [cph]
Repository: CRAN
Date/Publication: 2018-06-19 14:52:58 UTC
