Package: nestedcv
Title: Nested Cross-Validation with 'glmnet' and 'caret'
Version: 0.6.9
Authors@R: 
    c(person(given = "Myles",family = "Lewis",
    role = c("aut", "cre"),
    email = "myles.lewis@qmul.ac.uk",
    comment = c(ORCID = "0000-0001-9365-5345")),
    person(given = "Athina",family = "Spiliopoulou",
    role = c("aut"),
    comment = c(ORCID = "0000-0002-5929-6585")),
    person(given = "Katriona",family = "Goldmann",
    role = c("aut"),
    comment = c(ORCID = "0000-0002-9073-6323")))
Maintainer: Myles Lewis <myles.lewis@qmul.ac.uk>
BugReports: https://github.com/myles-lewis/nestedcv/issues
URL: https://github.com/myles-lewis/nestedcv
Description: Implements nested k*l-fold cross-validation for lasso and elastic-net regularised linear models via the 'glmnet' package and other machine learning models via the 'caret' package. Cross-validation of 'glmnet' alpha mixing parameter and embedded fast filter functions for feature selection are provided. Described as double cross-validation by Stone (1977) <doi:10.1111/j.2517-6161.1977.tb01603.x>. Also implemented is a method using outer CV to measure unbiased model performance metrics when fitting Bayesian linear and logistic regression shrinkage models using the horseshoe prior over parameters to encourage a sparse model as described by Piironen & Vehtari (2017) <doi:10.1214/17-EJS1337SI>.
Language: en-gb
License: MIT + file LICENSE
Encoding: UTF-8
Imports: caret, data.table, doParallel, foreach, ggplot2, glmnet,
        matrixStats, matrixTests, methods, parallel, pROC, Rfast, rlang
RoxygenNote: 7.2.3
Suggests: Boruta, CORElearn, fastshap, gbm, ggbeeswarm, ggpubr, hsstan,
        mda, mlbench, pbapply, randomForest, ranger, RcppEigen,
        rmarkdown, knitr, SuperLearner
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2023-08-22 18:51:02 UTC; myles
Author: Myles Lewis [aut, cre] (<https://orcid.org/0000-0001-9365-5345>),
  Athina Spiliopoulou [aut] (<https://orcid.org/0000-0002-5929-6585>),
  Katriona Goldmann [aut] (<https://orcid.org/0000-0002-9073-6323>)
Repository: CRAN
Date/Publication: 2023-08-22 19:10:02 UTC
Built: R 4.3.1; ; 2023-08-25 23:48:48 UTC; unix
