Displays for model fits of multiple models and their ensembles. For classification models, the plots are heatmaps, for regression, scatterplots.

Elastic net regression models are controlled by two parameters,
  lambda, a measure of shrinkage, and alpha, a metric defining the model's
  location on the spectrum between ridge and lasso regression.  
  glmnet provides tools for selecting lambda via cross
  validation but no automated methods for selection of alpha.  Elastic Net
  SearcheR automates the simultaneous selection of both lambda and alpha.
  Developed, in part, with support by NICHD R03 HD094912.

An assistant built on large language models that helps interpret statistical model outputs in R by generating concise, audience-specific explanations.

Measurement and partitioning of diversity, based on Tsallis entropy, following Marcon and Herault (2015) <doi:10.18637/jss.v067.i08>.
  'entropart' provides functions to calculate alpha, beta and gamma diversity of communities, including phylogenetic and functional diversity.
  Estimation-bias corrections are available.

Methods for data analysis from an entropic perspective. These methods are nonparametric and perform well on non-ordinal data. Currently includes 'HeatMap()' for visualizing distributional characteristics among multiple populations (groups).

Implements various estimators of entropy for discrete random 
  variables, including the shrinkage estimator by Hausser and Strimmer (2009),
  the maximum likelihood and the Millow-Madow estimator, various Bayesian
  estimators, and the Chao-Shen estimator.  It also offers an R interface to the
  NSB estimator.  Furthermore, the package provides functions for estimating the
  Kullback-Leibler divergence, the chi-squared divergence, mutual information,
  and the chi-squared divergence of independence.  It also computes the
  G statistic and the chi-squared statistic and corresponding p-values.
  Furthermore, there are functions for discretizing continuous random variables.