rebuild

DESCRIPTION

@@ -3,10 +3,10 @@ Type: Package
 Title: Ensembles of Caret Models
 Version: 4.0.0
 Date: 2024-08-12
-Authors@R: c(person(c("Zachary", "A."), "Deane-Mayer", role = c("aut", "cre"),
-    email = "zach.mayer@gmail.com"),
-    person(c("Jared", "E."), "Knowles", role=c("aut"),
-    email="jknowles@gmail.com"))
+Authors@R: c(person(c("Zachary", "A."), "Deane-Mayer", role = c("aut", "cre", "cph"), email = "zach.mayer@gmail.com"),
+    person(c("Jared", "E.", "Knowles", role="ctb", email="jknowles@gmail.com"),
+    person("Antón", "López", role="ctb", email="zach.mayer+get-correct-email@gmail.com")
+    )
 URL: https://github.com/zachmayer/caretEnsemble
 BugReports: https://github.com/zachmayer/caretEnsemble/issues
 Description: Functions for creating ensembles of caret models: caretList()

LICENSE

@@ -1,2 +1,2 @@
 YEAR: 2013-2024
-COPYRIGHT HOLDER: Zachary Albert Mayer
+COPYRIGHT HOLDER: Zachary Albert Mayer

README.md

@@ -24,19 +24,24 @@ Size](https://img.shields.io/github/languages/code-size/zachmayer/caretEnsemble.
 [![Discord](https://img.shields.io/discord/1255535052578619524)](https://discord.gg/zgPeSm8a3u)
 <!-- badges: end -->
 
-caretEnsemble is a framework for fitting multiple [caret
-models](https://github.com/topepo/caret) using the same re-sampling
-strategy as well as creating ensembles of such models. Use `caretList`
-to fit multiple models, and then use `caretStack` to stack them using a
-caret model.
+caretEnsemble is a framework for
+[stacking](https://en.wikipedia.org/wiki/Ensemble_learning#Stacking)
+models fit with the [caret](https://topepo.github.io/caret/) package.
+
+Use `caretList` to fit multiple models, and then use `caretStack` to
+stack them with another caret model.
 
 First, use caretList to fit many models to the same data:
 
 ``` r
 data(diamonds, package = "ggplot2")
 dat <- data.table::data.table(diamonds)
 dat <- dat[sample.int(nrow(diamonds), 500L), ]
-models <- caretEnsemble::caretList(price ~ ., data = dat, methodList = c("rf", "glmnet"))
+models <- caretEnsemble::caretList(
+  price ~ .,
+  data = dat,
+  methodList = c("rf", "glmnet")
+)
 ```
 
 Then, use caretEnsemble to make a greedy ensemble of these models
@@ -51,21 +56,21 @@ print(greedy_stack)
 #> 
 #> No pre-processing
 #> Resampling: Cross-Validated (5 fold) 
-#> Summary of sample sizes: 400, 400, 400, 401, 399 
+#> Summary of sample sizes: 400, 400, 400, 400, 400 
 #> Resampling results:
 #> 
 #>   RMSE      Rsquared   MAE     
-#>   946.5929  0.9468516  506.8786
+#>   1019.069  0.9401024  530.9788
 #> 
 #> Tuning parameter 'max_iter' was held constant at a value of 100
 #> 
 #> Final model:
 #> Greedy MSE
-#> RMSE:  963.298 
+#> RMSE:  1013.725 
 #> Weights:
 #>        [,1]
-#> rf      0.7
-#> glmnet  0.3
+#> rf      0.9
+#> glmnet  0.1
 ggplot2::autoplot(greedy_stack, training_data = dat, xvars = c("carat", "table"))
 ```
 
@@ -84,11 +89,11 @@ print(rf_stack)
 #> 
 #> No pre-processing
 #> Resampling: Cross-Validated (5 fold) 
-#> Summary of sample sizes: 400, 401, 400, 400, 399 
+#> Summary of sample sizes: 400, 400, 400, 400, 400 
 #> Resampling results:
 #> 
 #>   RMSE      Rsquared   MAE     
-#>   968.4043  0.9467661  499.4089
+#>   1076.369  0.9338026  531.9601
 #> 
 #> Tuning parameter 'mtry' was held constant at a value of 2
 #> 
@@ -100,8 +105,8 @@ print(rf_stack)
 #>                      Number of trees: 500
 #> No. of variables tried at each split: 2
 #> 
-#>           Mean of squared residuals: 881047.1
-#>                     % Var explained: 94.87
+#>           Mean of squared residuals: 1136154
+#>                     % Var explained: 93.38
 ggplot2::autoplot(rf_stack, training_data = dat, xvars = c("carat", "table"))
 ```
 
@@ -121,13 +126,9 @@ install.packages("caretEnsemble")
 devtools::install_github("zachmayer/caretEnsemble")
 ```
 
-There are also tagged versions of caretEnsemble on github you can
-install via devtools. For example, to install the original draft of the
-API:
-
 There are also tagged versions of caretEnsemble on github you can
 install via devtools. For example, to install the [previous release of
-caretEnsemble](https://github.com/zachmayer/caretEnsemble/releases/tag/)
+caretEnsemble](https://github.com/zachmayer/caretEnsemble/releases/)
 use:
 
 ``` r
@@ -149,7 +150,7 @@ For iterating while writing code, run `make dev`. This runs just
 `make clean fix-style document lint spell test`, for a quicker local dev
 loop. Please still run `make all` before making a PR.
 
-use `make all` before making a pull request, which will also run R CMD
+Use `make all` before making a pull request, which will also run R CMD
 CHECK and a code coverage check. This runs
 `make clean fix-style document install build-readme build-vignettes lint spell test check coverage preview-site`.
 

README.rmd

@@ -39,7 +39,11 @@ First, use caretList to fit many models to the same data:
 data(diamonds, package = "ggplot2")
 dat <- data.table::data.table(diamonds)
 dat <- dat[sample.int(nrow(diamonds), 500L), ]
-models <- caretEnsemble::caretList(price ~ ., data = dat, methodList = c("rf", "glmnet"))
+models <- caretEnsemble::caretList(
+  price ~ .,
+  data = dat,
+  methodList = c("rf", "glmnet")
+)
 ```
 
 Then, use caretEnsemble to make a greedy ensemble of these models
@@ -67,9 +71,7 @@ install.packages("caretEnsemble")
 devtools::install_github("zachmayer/caretEnsemble")
 ```
 
-There are also tagged versions of caretEnsemble on github you can install via devtools. For example, to install the original draft of the API:
-
-There are also tagged versions of caretEnsemble on github you can install via devtools. For example, to install the [previous release of caretEnsemble](https://github.com/zachmayer/caretEnsemble/releases/tag/) use:
+There are also tagged versions of caretEnsemble on github you can install via devtools. For example, to install the [previous release of caretEnsemble](https://github.com/zachmayer/caretEnsemble/releases/) use:
 ```{r, eval=FALSE}
 devtools::install_github("zachmayer/caretEnsemble@2.0.3")
 ```
@@ -82,15 +84,15 @@ Use `make fix-style` to fix simple linting errors.
 
 For iterating while writing code, run `make dev`. This runs just `make clean fix-style document lint spell test`, for a quicker local dev loop. Please still run `make all` before making a PR.
 
-use `make all` before making a pull request, which will also run R CMD CHECK and a code coverage check. This runs `make clean fix-style document install build-readme build-vignettes lint spell test check coverage preview-site`.
+Use `make all` before making a pull request, which will also run R CMD CHECK and a code coverage check. This runs `make clean fix-style document install build-readme build-vignettes lint spell test check coverage preview-site`.
 
 ## First time dev setup:
 run `make install` from the git repository to install the dev version of caretEnsemble, along with the necessary package dependencies.
 
 # Inspiration and similar packages:
 caretEnsemble was inspired by [medley](https://github.com/mewo2/medley), which in turn was inspired by Caruana et. al.'s (2004) paper [Ensemble Selection from Libraries of Models.](http://www.cs.cornell.edu/~caruana/ctp/ct.papers/caruana.icml04.icdm06long.pdf)
 
-If you want to do something similar in python, check out [vecstack](https://github.com/vecxoz/vecstack)
+If you want to do something similar in python, check out [vecstack](https://github.com/vecxoz/vecstack).
 
 # Code of Conduct:
 Please note that this project is released with a [Contributor Code of Conduct](https://github.com/zachmayer/caretEnsemble/blob/master/.github/CONTRIBUTING.md). By participating in this project you agree to abide by its terms.

inst/WORDLIST

@@ -8,6 +8,7 @@ Caruana
 classProbs
 CMD
 CodeFactor
+codebase
 coercible
 Deane
 defaultControl
@@ -27,8 +28,10 @@ importances
 kable
 knitr
 Ksikes
+linter
 linters
 Makefile
+makefile
 methodList
 Mizil
 modelInfo
@@ -41,30 +44,28 @@ Niculescu
 nnet
 observeds
 optimizers
+pkgdown
 pred
 preds
 prob
 probs
+readme
 resid
 roxygen
 rpart
+'s
 savePredictions
+scalability
 scikit
 SDs
 trainControl
 travis
 tuneGrid
 tuneList
+un
 unintuitive
 varImp
 vecstack
-yhat
-’s
-codebase
-linter
-makefile
-pkgdown
-readme
-scalability
-un
-Verions
+verions
+xvars
+yhat