Package: Rforestry
Type: Package
Title: Random Forests, Linear Trees, and Gradient Boosting for
        Inference and Interpretability
Version: 0.9.0.95
Authors@R: c(
    person("Sören", "Künzel", role = "aut"),
    person("Theo", "Saarinen", role = c("aut","cre"), email = "theo_s@berkeley.edu"),
    person("Simon", "Walter", role = "aut"),
    person("Edward", "Liu", role = "aut"),
    person("Allen", "Tang", role = "aut"),
    person("Jasjeet", "Sekhon", role = "aut")
    )
Maintainer: Theo Saarinen <theo_s@berkeley.edu>
BugReports: https://github.com/forestry-labs/Rforestry/issues
URL: https://github.com/forestry-labs/Rforestry
Description: Provides fast implementations of Honest Random Forests, 
    Gradient Boosting, and Linear Random Forests, with an emphasis on inference 
    and interpretability. Additionally contains methods for variable 
    importance, out-of-bag prediction, regression monotonicity, and
    several methods for missing data imputation. Soren R. Kunzel, 
    Theo F. Saarinen, Edward W. Liu, Jasjeet S. Sekhon (2019) <arXiv:1906.06463>.
License: GPL (>= 3)
Encoding: UTF-8
Imports: Rcpp (>= 0.12.9), parallel, methods, visNetwork, glmnet (>=
        4.1), grDevices, onehot, dplyr
LinkingTo: Rcpp, RcppArmadillo, RcppThread
RoxygenNote: 7.1.2
SystemRequirements: C++11
Suggests: testthat, knitr, rmarkdown, mvtnorm
Collate: 'R_preprocessing.R' 'RcppExports.R' 'forestry.R'
        'adaptive_forestry.R' 'backwards_compatible.R'
        'compute_rf_lp.R' 'neighborhood_imputation.R' 'plottree.R'
NeedsCompilation: yes
Packaged: 2022-03-09 06:29:28 UTC; theosaa
Author: Sören Künzel [aut],
  Theo Saarinen [aut, cre],
  Simon Walter [aut],
  Edward Liu [aut],
  Allen Tang [aut],
  Jasjeet Sekhon [aut]
Repository: CRAN
Date/Publication: 2022-03-09 10:10:02 UTC
Built: R 4.1.3; x86_64-w64-mingw32; 2022-07-18 15:09:47 UTC; windows
Archs: i386, x64
