Other packages > Find by keyword >

vtreat  

A Statistically Sound 'data.frame' Processor/Conditioner
View on CRAN: Click here


Download and install vtreat package within the R console
Install from CRAN:
install.packages("vtreat")

Install from Github:
library("remotes")
install_github("cran/vtreat")

Install by package version:
library("remotes")
install_version("vtreat", "1.6.5")



Attach the package and use:
library("vtreat")
Maintained by
John Mount
[Scholar Profile | Author Map]
First Published: 2015-09-06
Latest Update: 2023-08-19
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.1173313>.
How to cite:
John Mount (2015). vtreat: A Statistically Sound 'data.frame' Processor/Conditioner. R package version 1.6.5, https://cran.r-project.org/web/packages/vtreat. Accessed 05 Apr. 2025.
Previous versions and publish date:
0.5.14 (2015-09-06 19:31), 0.5.16 (2015-09-13 19:32), 0.5.18 (2015-10-07 22:43), 0.5.20 (2015-11-05 20:47), 0.5.21 (2015-11-11 00:39), 0.5.22 (2016-01-08 00:27), 0.5.23 (2016-04-29 12:12), 0.5.25 (2016-05-03 08:20), 0.5.26 (2016-07-12 07:49), 0.5.27 (2016-08-17 21:45), 0.5.28 (2016-10-25 00:18), 0.5.30 (2017-01-21 23:09), 0.5.31 (2017-04-14 15:59), 0.5.32 (2017-06-14 03:37), 0.6.0 (2017-09-20 22:37), 1.0.0 (2017-10-04 17:51), 1.0.1 (2017-10-17 05:44), 1.0.2 (2018-01-20 16:32), 1.0.3 (2018-03-11 00:50), 1.0.4 (2018-05-05 19:42), 1.2.0 (2018-06-19 16:53), 1.2.1 (2018-06-27 06:36), 1.2.2 (2018-07-04 17:30), 1.2.3 (2018-07-11 19:20), 1.3.0 (2018-07-20 17:20), 1.3.1 (2018-09-10 18:00), 1.3.2 (2018-11-05 18:50), 1.3.3 (2018-12-17 19:20), 1.3.4 (2019-01-02 18:20), 1.3.5 (2019-01-27 19:10), 1.3.6 (2019-02-09 21:00), 1.3.7 (2019-02-21 07:20), 1.3.8 (2019-03-31 19:30), 1.4.0 (2019-05-05 17:20), 1.4.2 (2019-07-01 23:30), 1.4.3 (2019-07-18 13:20), 1.4.4 (2019-07-27 23:40), 1.4.5 (2019-09-11 18:00), 1.4.6 (2019-09-23 07:40), 1.4.7 (2019-10-01 20:40), 1.4.8 (2019-12-08 20:00), 1.5.0 (2020-01-08 23:30), 1.5.1 (2020-01-16 18:40), 1.5.2 (2020-02-08 20:10), 1.6.0 (2020-03-11 12:40), 1.6.1 (2020-08-12 20:20), 1.6.2 (2020-10-17 17:40), 1.6.3 (2021-06-11 17:10), 1.6.4 (2023-08-19 22:00)
Other packages that cited vtreat R package
View vtreat citation profile
Other R packages that vtreat depends, imports, suggests or enhances
Complete documentation for vtreat
Functions, R codes and Examples using the vtreat R package
Some associated functions: BinomialOutcomeTreatment . MultinomialOutcomeTreatment . NumericOutcomeTreatment . UnsupervisedTreatment . apply_transform . as_rquery_plan . buildEvalSets . center_scale . classification_parameters . designTreatmentsC . designTreatmentsN . designTreatmentsZ . design_missingness_treatment . dot-wmean . fit . fit_prepare . fit_transform . flatten_fn_list . format.vtreatment . getSplitPlanAppLabels . get_feature_names . get_score_frame . get_transform . kWayCrossValidation . kWayStratifiedY . kWayStratifiedYReplace . makeCustomCoderCat . makeCustomCoderNum . makekWayCrossValidationGroupedByColumn . mkCrossFrameCExperiment . mkCrossFrameMExperiment . mkCrossFrameNExperiment . multinomial_parameters . novel_value_summary . oneWayHoldout . patch_columns_into_frame . ppCoderC . ppCoderN . pre_comp_xval . prepare.multinomial_plan . prepare . prepare.simple_plan . prepare.treatmentplan . print.multinomial_plan . print.simple_plan . print.treatmentplan . print.vtreatment . problemAppPlan . regression_parameters . rqdatatable_prepare . rquery_prepare . solveIsotone . solveNonDecreasing . solveNonIncreasing . solve_piecewise . solve_piecewisec . spline_variable . spline_variablec . square_window . square_windowc . track_values . unsupervised_parameters . value_variables_C . value_variables_N . variable_values . vnames . vorig . vtreat-package . 
Some associated R codes: center_scale.R . cleanTreatment.R . customCoder.R . design_missing_Z.R . deviationFact.R . effectTreatmentC.R . effectTreatmentN.R . ft.R . indicatorTreatment.R . isBadTreatment.R . isotone.R . mult_class.R . outOfSample.R . partial_pooling.R . patch_columns_into_frame.R . pre_comp_xval.R . prevalenceFact.R . rquery_treatment.R . segmented_variable.R . spline_variable.R . square_window.R . utils.R . variable_importance.R . vtreat.R . vtreatImpl.R . vtreat_pipes.R . wrapr_exports.R . zzz.R .  Full vtreat package functions and examples
Downloads during the last 30 days
03/0603/0703/0803/0903/1003/1103/1203/1303/1403/1503/1603/1703/1803/1903/2003/2103/2203/2303/2403/2503/2603/2703/2803/2903/3003/3104/0104/0204/03Downloads for vtreat20406080100120140160180200220240TrendBars

Today's Hot Picks in Authors and Packages

nextGenShinyApps  
Craft Exceptional 'R Shiny' Applications and Dashboards with Novel Responsive Tools
Nove responsive tools for designing and developing 'Shiny' dashboards and applications. The scripts ...
Download / Learn more Package Citations See dependency  
leapp  
Latent Effect Adjustment After Primary Projection
These functions take a gene expression value matrix, a primary covariate vector, an additional know ...
Download / Learn more Package Citations See dependency  
whitestrap  
White Test and Bootstrapped White Test for Heteroskedasticity
Formal implementation of White test of heteroskedasticity and a bootstrapped version of it, develope ...
Download / Learn more Package Citations See dependency  
deepdive  
Deep Learning for General Purpose
Aims to provide simple intuitive functions to create quick prototypes of artificial neural network o ...
Download / Learn more Package Citations See dependency  
IGST  
Informative Gene Selection Tool
Mining informative genes with certain biological meanings are important for clinical diagnosis of di ...
Download / Learn more Package Citations See dependency  
gllvm  
Generalized Linear Latent Variable Models
Analysis of multivariate data using generalized linear latent variable models (gllvm). Estimation i ...
Download / Learn more Package Citations See dependency  

23,990

R Packages

207,311

Dependencies

64,809

Author Associations

23,991

Publication Badges

© Copyright since 2022. All right reserved, rpkg.net.  Based in Cambridge, Massachusetts, USA