Package: embed 1.2.2.9000

Emil Hvitfeldt

embed: Extra Recipes for Encoding Predictors

Predictors can be converted to one or more numeric representations using a variety of methods. Effect encodings using simple generalized linear models <doi:10.48550/arXiv.1611.09477> or nonlinear models <doi:10.48550/arXiv.1604.06737> can be used. There are also functions for dimension reduction and other approaches.

Authors:Emil Hvitfeldt [aut, cre], Max Kuhn [aut], Posit Software, PBC [cph, fnd]

embed_1.2.2.9000.tar.gz
embed_1.2.2.9000.zip(r-4.7-any)embed_1.2.2.9000.zip(r-4.6-any)embed_1.2.2.9000.zip(r-4.5-any)
embed_1.2.2.9000.tgz(r-4.6-any)embed_1.2.2.9000.tgz(r-4.5-any)
embed_1.2.2.9000.tar.gz(r-4.7-any)embed_1.2.2.9000.tar.gz(r-4.6-any)
embed_1.2.2.9000.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
embed/json (API)

# Install 'embed' in R:
install.packages('embed', repos = c('https://tidymodels.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/tidymodels/embed/issues

Pkgdown/docs site:https://embed.tidymodels.org

Datasets:

On CRAN:

Conda:

9.20 score 146 stars 1.4k scripts 1.1k downloads 1 mentions 21 exports 77 dependencies

Last updated from:09044d5f47. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK298
source / vignettesOK331
linux-releaseOK317
macos-releaseOK225
macos-oldrelOK201
windows-develOK293
windows-releaseOK297
windows-oldrelOK326
wasm-releaseOK162

Exports:add_woedictionaryembed_controlrequired_pkgsstep_collapse_cartstep_collapse_stringdiststep_discretize_cartstep_discretize_xgbstep_embedstep_feature_hashstep_lencodestep_lencode_bayesstep_lencode_glmstep_lencode_mixedstep_pca_sparsestep_pca_sparse_bayesstep_pca_truncatedstep_umapstep_woetidytunable

Dependencies:BHclasscliclockcodetoolscpp11data.tablediagramdigestdplyrdqrngfarverFNNfurrrfuturefuture.applygenericsggplot2globalsgluegowergtablehardhatipredirlbaisobandKernSmoothlabelinglatticelavalifecyclelistenvlubridatemagrittrMASSMatrixnnetnumDerivparallellypillarpkgconfigprodlimprogressrpurrrR6RColorBrewerRcppRcppAnnoyRcppEigenRcppProgressrecipesrlangrpartrsampleRSpectraS7scalesshapesitmoslidersparsevctrsSQUAREMstringistringrsurvivaltibbletidyrtidyselecttimechangetimeDatetzdbutf8uwotvctrsviridisLitewarpwithr

Readme and manuals

Help Manual

Help pageTopics
Add WoE in a data frameadd_woe
Weight of evidence dictionarydictionary
Compound solubility datasolubility
Supervised Collapsing of Factor Levelsstep_collapse_cart tidy.step_collapse_cart
collapse factor levels using stringdiststep_collapse_stringdist tidy.step_collapse_stringdist
Discretize numeric variables with CARTstep_discretize_cart tidy.step_discretize_cart
Discretize numeric variables with XgBooststep_discretize_xgb tidy.step_discretize_xgb
Encoding Factors into Multiple Columnsembed_control step_embed tidy.step_embed
Likelihood encoding using analytical formulastep_lencode tidy.step_lencode
Supervised Factor Conversions into Linear Functions using Bayesian Likelihood Encodingsstep_lencode_bayes tidy.step_lencode_bayes
Supervised Factor Conversions into Linear Functions using Likelihood Encodingsstep_lencode_glm tidy.step_lencode_glm
Supervised Factor Conversions into Linear Functions using Bayesian Likelihood Encodingsstep_lencode_mixed tidy.step_lencode_mixed
Sparse PCA Signal Extractionstep_pca_sparse tidy.step_pca_sparse
Sparse Bayesian PCA Signal Extractionstep_pca_sparse_bayes tidy.step_pca_sparse_bayes
Truncated PCA Signal Extractionstep_pca_truncated tidy.step_pca_truncated
Supervised and unsupervised uniform manifold approximation and projection (UMAP)step_umap tidy.step_umap
Weight of evidence transformationstep_woe tidy.step_woe
Crosstable with woe between a binary outcome and a predictor variable.woe_table