ProjectMOSAIC/mosaicModel: Create, Visualize, and Predict with Models

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.

Getting started

Package details

MaintainerDaniel Kaplan <[email protected]>
LicenseMIT + file LICENSE
Version0.3.1.9000
URL https://github.com/ProjectMOSAIC/mosaicModel
Package repositoryView on GitHub
Installation Install the latest version of this package by entering the following in R:
install.packages("remotes")
remotes::install_github("ProjectMOSAIC/mosaicModel")
ProjectMOSAIC/mosaicModel documentation built on May 13, 2019, 1:35 a.m.