CRAN/E | cvms

cvms

Cross-Validation for Model Selection

Installation

About

Cross-validate one or multiple regression and classification models and get relevant evaluation metrics in a tidy format. Validate the best model on a test set and compare it to a baseline evaluation. Alternatively, evaluate predictions from an external model. Currently supports regression and classification (binary and multiclass). Described in chp. 5 of Jeyaraman, B. P., Olsen, L. R., & Wambugu M. (2019, ISBN: 9781838550134).

github.com/ludvigolsen/cvms
Bug report File report

Key Metrics

Version 1.6.1
R ≥ 3.5
Published 2024-02-27 52 days ago
Needs compilation? no
License MIT
License File
CRAN checks cvms results

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Maintainer

Maintainer

Ludvig Renbo Olsen

r-pkgs@ludvigolsen.dk

Authors

Ludvig Renbo Olsen

aut / cre

(@ludvigolsen)

Hugh Benjamin Zachariae

aut

Indrajeet Patil

ctb

(@patilindrajeets)

Daniel Lüdecke

ctb

Material

README
NEWS
Reference manual
Package source

Vignettes

creating_confusion_matrix
available_metrics
cross_validating_custom_functions
evaluate_by_id
picking_the_number_of_folds_for_cross-validation

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

cvms archive

Depends

R ≥ 3.5

Imports

checkmate ≥ 2.0.0
data.table ≥ 1.12
dplyr ≥ 0.8.5
ggplot2
groupdata2 ≥ 2.0.2
lifecycle
lme4 ≥ 1.1-23
MuMIn ≥ 1.43.17
parameters ≥ 0.15.0
plyr
pROC ≥1.16.0
purrr
rearrr ≥ 0.3.0
recipes ≥ 0.1.13
rlang ≥ 0.4.7
stats
stringr
tibble ≥ 3.0.3
tidyr ≥1.1.2
utils

Suggests

AUC
covr ≥ 3.3.1
e1071 ≥ 1.7-2
furrr
ggimage ≥0.3.3
ggnewscale ≥ 0.4.3
knitr
merDeriv ≥ 0.2-4
nnet ≥ 7.3-12
randomForest ≥ 4.6-14
rmarkdown
rsvg
testthat ≥ 2.3.2
xpectr ≥ 0.4.1

Reverse Imports

explainer
oHMMed

Reverse Suggests

metabinR