Preprocessing Algorithms for Imbalanced Datasets (imbalance)
Algorithms to treat imbalanced datasets. Imbalanced datasets usually damage the performance of the classifiers. Thus, it is important to treat data before applying a classifier algorithm. This package includes recent preprocessing algorithms in the literature.

Joint Models for Longitudinal Measurements and Competing Risks Failure Time Data (JMcmprsk)
Parameter models for the joint modeling of longitudinal (continuous or ordinal) data and time-to-event data with competing risks. For a detailed information, see Robert Elashoff, Gang li and Ning Li (2016, ISBN:9781439807828) ; Robert M. Elashoff,Gang Li and Ning Li (2008) <doi:10.1111/j.1541-0420.2007.00952.x> ; Ning Li, Robert Elashoff, Gang Li and Jeffrey Saver (2010) <doi:10.1002/sim.3798> .

Pull Track Audio Features from the ‘Spotify’ Web API (spotifyr)
A wrapper for pulling track audio features from the ‘Spotify’ Web API <http://…/web-api> in bulk. By automatically batching API requests, it allows you to enter an artist’s name and retrieve their entire discography in seconds, along with audio features and track/album popularity metrics. You can also pull song and playlist information for a given ‘Spotify’ user (including yourself!).

Make R behave a little more strictly (strict)
The goal of strict to make R behave a little more strictly, making base functions more likely to throw an error rather than returning potentially ambiguous results. library(strict) forces you to confront potential problems now, instead of in the future. This has both pros and cons: often you can most easily fix a potential ambiguity when you’re working on the code (rather than in six months time when you’ve forgotten how it works), but it also forces you to resolve ambiguities that might never occur with your code/data.

Discrete Distribution Approximations (distcrete)
Creates discretised versions of continuous distribution functions by mapping continuous values to an underlying discrete grid, based on a (uniform) frequency of discretisation, a valid discretisation point, and an integration range. For a review of discretisation methods, see Chakraborty (2015) <doi:10.1186/s40488-015-0028-6>.

Time-Dependent Prognostic Accuracy with Multiply Evaluated Bio Markers or Scores (longROC)
Time-dependent Receiver Operating Characteristic curves, Area Under the Curve, and Net Reclassification Indexes for repeated measures. It is based on methods in Barbati and Farcomeni (2017) <doi:10.1007/s10260-017-0410-2>.