Provides a research infrastructure to develop and evaluate
    collaborative filtering recommender algorithms. This includes a sparse 
    representation for user-item matrices, many popular algorithms, top-N recommendations,
    and cross-validation. Hahsler (2022) <doi:10.48550/arXiv.2205.12371>.

Provides the Book-Crossing Dataset for the package recommenderlab.

Provides the Jester Dataset for package recommenderlab.

Implements solutions to canonical models of Economics such as Monopoly Profit Maximization, Cournot's Duopoly, Solow (1956, <doi:10.2307/1884513>) growth model and Mankiw, Romer and Weil (1992, <doi:10.2307/2118477>) growth model.

Functions for reconstructing individual-level data (time, status, arm) from Kaplan-MEIER curves published in academic journals (e.g. NEJM, JCO, JAMA). The individual-level data can be used for re-analysis, meta-analysis, methodology development, etc. This package was used to generate the data for commentary such as Sun, Rich, & Wei (2018) <doi:10.1056/NEJMc1808567>. Please see the vignette for a quickstart guide.

Functions to reconstruct sessions from web log or other user trace data
             and calculate various metrics around them, producing tabular,
             output that is compatible with 'dplyr' or 'data.table' centered processes.

A lightweight toolkit to validate new observations when computing
    their predictions with a predictive model. The validation process 
    consists of two steps: (1) record relevant statistics and meta data of the
    variables in the original training data for the predictive model and
    (2) use these data to run a set of basic validation tests on the new set of 
    observations.

Provides functions for linking and deduplicating data sets.
  Methods based on a stochastic approach are implemented as well as 
  classification algorithms from the machine learning domain. For details, 
  see our paper "The RecordLinkage Package: Detecting Errors in Data" 
  Sariyar M / Borg A (2010) <doi:10.32614/RJ-2010-017>.