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tidyfit  

Regularized Linear Modeling with Tidy Data
View on CRAN: Click here


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

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

Install by package version:
library("remotes")
install_version("tidyfit", "0.7.2")



Attach the package and use:
library("tidyfit")
Maintained by
Johann Pfitzinger
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2022-10-31
Latest Update: 2023-05-20
Description:
An extension to the 'R' tidy data environment for automated machine learning. The package allows fitting and cross validation of linear regression and classification algorithms on grouped data.
How to cite:
Johann Pfitzinger (2022). tidyfit: Regularized Linear Modeling with Tidy Data. R package version 0.7.2, https://cran.r-project.org/web/packages/tidyfit. Accessed 22 Dec. 2024.
Previous versions and publish date:
0.5.1 (2022-10-31 14:52), 0.6.0 (2022-11-25 13:40), 0.6.1 (2023-01-21 20:10), 0.6.2 (2023-02-19 20:20), 0.6.3 (2023-03-15 20:20), 0.6.4 (2023-05-20 17:40), 0.6.5 (2023-11-20 21:30), 0.7.0 (2024-02-26 17:20), 0.7.1 (2024-03-23 16:20)
Other packages that cited tidyfit R package
View tidyfit citation profile
Other R packages that tidyfit depends, imports, suggests or enhances
Complete documentation for tidyfit
Functions, R codes and Examples using the tidyfit R package
Some associated functions: Factor_Industry_Returns . classify . coef.tidyfit.models . dot-fit.adalasso . dot-fit.bayes . dot-fit.blasso . dot-fit.bma . dot-fit.boost . dot-fit.bridge . dot-fit.chisq . dot-fit.cor . dot-fit.enet . dot-fit.genetic . dot-fit.gets . dot-fit.glm . dot-fit.glmm . dot-fit.hfr . dot-fit.lasso . dot-fit.lm . dot-fit.mrmr . dot-fit.mslm . dot-fit.pcr . dot-fit.plsr . dot-fit.quantile . dot-fit.quantile_rf . dot-fit.relief . dot-fit.rf . dot-fit.ridge . dot-fit.robust . dot-fit.spikeslab . dot-fit.subset . dot-fit.svm . dot-fit.tvp . fitted.tidyfit.models . m . predict.tidyfit.models . regress . residuals.tidyfit.models . 
Some associated R codes: check_method.R . check_package_name.R . classify.R . coef.R . coef.tidyfit.models.R . coef_methods.R . data.R . default_hp_grid.R . eval_metrics.R . fit.R . fit.adalasso.R . fit.bayes.R . fit.blasso.R . fit.bma.R . fit.boost.R . fit.bridge.R . fit.chisq.R . fit.cor.R . fit.enet.R . fit.genetic.R . fit.gets.R . fit.glm.R . fit.glmm.R . fit.hfr.R . fit.lasso.R . fit.lm.R . fit.mrmr.R . fit.mslm.R . fit.pcr.R . fit.plsr.R . fit.quantile.R . fit.quantile_rf.R . fit.relief.R . fit.rf.R . fit.ridge.R . fit.robust.R . fit.spikeslab.R . fit.subset.R . fit.svm.R . fit.tvp.R . fit_groups.R . fitted.R . fitted.tidyfit.models.R . fitted_methods.R . generics.R . m.R . make_cross_val.R . make_rsample_bootstraps.R . model_definition.R . post_process.R . predict.R . predict.tidyfit.models.R . predict_methods.R . regress.R . resid.R . resid_methods.R . residuals.tidyfit.models.R . unnest.tidyfit.models.R . unnest_args.R . utils.R .  Full tidyfit package functions and examples
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