Angle-Based Outlier Detection (abodOutlier)
Performs angle-based outlier detection on a given dataframe. Three methods are available, a full but slow implementation using all the data that has cubic complexity, a fully randomized one which is way more efficient and another using k-nearest neighbours. These algorithms are specially well suited for high dimensional data outlier detection.
Generate Data Containing Fake Personally Identifiable Information (generator)
Allows users to quickly and easily generate fake data containing Personally Identifiable Information (PII) through convenience functions.
Explanation of Predictions for Classification and Regression Models (ExplainPrediction)
Package contains methods to generate explanations for individual predictions of classification and regression models. Weighted averages of individual explanations form explanation of the whole model. The package extends ‘CORElearn’ package, but other prediction models can also be explained using a wrapper.
Quickly Profile Data in R (profilr)
Allows users to quickly and reliably profile data in R using convenience functions. The profiled data returns as a data.frame and provides a wealth of common and uncommon summary statistics.
Bayesian Model Averaging using Bayesian Adaptive Sampling (BAS)
Package for Bayesian Model Averaging in linear models and generalized linear models using stochastic or deterministic sampling without replacement from posterior distributions. Prior distributions on coefficients are from Zellner’s g-prior or mixtures of g-priors corresponding to the Zellner-Siow Cauchy Priors or the Liang et al hyper-g priors (JASA 2008) or mixtures of g-priors in GLMS of Li and Clyde 2015. Other model selection criteria include AIC and BIC. Sampling probabilities may be updated based on the sampled models using Sampling w/out Replacement or an MCMC algorithm samples models using the BAS tree structure as an efficient hash table. Allows uniform or beta-binomial prior distributions on models, and may force variables to always be included.