mosaicModel: An Interface to Statistical Modeling Independent of Model Architecture

Provides functions for evaluating, displaying, and interpreting statistical models. The goal is to abstract the operations on models from the particular architecture of the model. For instance, calculating effect sizes rather than looking at coefficients. The package includes interfaces to both regression and classification architectures, including lm(), glm(), rlm() in 'MASS', random forests and recursive partitioning, k-nearest neighbors, linear and quadratic discriminant analysis, and models produced by the 'caret' package's train(). It's straightforward to add in other other model architectures.

Version: 0.3.0
Depends: R (≥ 3.1), mosaicCore, splines, dplyr
Imports: caret, ggplot2, ggformula, lazyeval, knitr, MASS, testthat, tibble, tidyr, tidyverse
Suggests: mosaic, mosaicData, randomForest, rpart
Published: 2017-09-22
Author: Kaplan Daniel [aut, cre], Pruim Randall [aut, cre]
Maintainer: Daniel Kaplan <kaplan at macalester.edu>
License: MIT + file LICENSE
NeedsCompilation: no
CRAN checks: mosaicModel results

Downloads:

Reference manual: mosaicModel.pdf
Vignettes: Using mosaicModel
Package source: mosaicModel_0.3.0.tar.gz
Windows binaries: r-devel: mosaicModel_0.3.0.zip, r-release: mosaicModel_0.3.0.zip, r-oldrel: mosaicModel_0.3.0.zip
OS X El Capitan binaries: r-release: mosaicModel_0.3.0.tgz
OS X Mavericks binaries: r-oldrel: mosaicModel_0.3.0.tgz

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