Package: NumericEnsembles
Title: Automatically Runs 18 Individual and 14 Ensembles of Models
Version: 1.2
Authors@R: 
    person("Russ", "Conte", , "russconte@mac.com", role = c("aut", "cre", "cph"))
Depends: Cubist, Metrics, arm, brnn, broom, car, caret, corrplot,
        doParallel, dplyr, e1071, earth, gam, gbm, ggplot2, glmnet,
        graphics, grDevices, gridExtra, htmltools, htmlwidgets, ipred,
        leaps, nnet, olsrr, parallel, pls, purrr, randomForest,
        reactable, readr, rpart, scales, stats, tidyr, tree, utils,
        xgboost, R (>= 4.1.0)
Description: Automatically runs 18 individual models and 14 ensembles on numeric data, for a total of 32 models. The package automatically returns complete results on all 32 models,
    25 charts and six tables. The user simply provides the tidy data, and answers a few questions (for example, how many times would you like to resample the data).
    From there the package randomly splits the data into train, test and validation sets as the user requests (for example, train = 0.60, test = 0.20, validation = 0.20),
    fits each of models on the training data, makes predictions on the test and validation sets, measures root mean squared error (RMSE), removes features above a user-set level of Variance Inflation Factor, and has several optional features including scaling
    all numeric data, four different ways to handle strings in the data. Perhaps the most significant feature is the package's ability to make predictions
    using the 32 pre trained models on totally new (untrained) data if the user selects that feature. This feature alone represents a very effective solution
    to the issue of reproducibility of models in data science. The package can also randomly resample the data as many times as the user sets, thus giving more
    accurate results than a single run. The graphs provide many results that are not typically found. For example, the package automatically calculates the Kolmogorov-Smirnov
    test for each of the 32 models and plots a bar chart of the results, a bias bar chart of each of the 32 models, as well as several plots for exploratory data
    analysis (automatic histograms of the numeric data, automatic histograms of the numeric data). The package also automatically creates a summary report
    that can be both sorted and searched for each of the 32 models, including RMSE, bias, train RMSE, test RMSE, validation RMSE, overfitting and duration.
    The best results on the holdout data typically beat the best results in data science competitions and published results for the same data set.
License: MIT + file LICENSE
Encoding: UTF-8
RoxygenNote: 7.3.3
LazyData: true
Suggests: knitr, rmarkdown, testthat (>= 3.0.0)
Config/testthat/edition: 3
VignetteBuilder: knitr
URL: http://www.NumericEnsembles.com,
        https://github.com/InfiniteCuriosity/NumericEnsembles
BugReports: https://github.com/InfiniteCuriosity/NumericEnsembles/issues
NeedsCompilation: no
Packaged: 2026-04-29 14:53:54 UTC; russellconte
Author: Russ Conte [aut, cre, cph]
Maintainer: Russ Conte <russconte@mac.com>
Repository: CRAN
Date/Publication: 2026-04-29 15:10:19 UTC
Built: R 4.5.2; ; 2026-05-01 19:40:49 UTC; unix
