Turns nested lists into data.frames in an orderly manner.

Calculates evaluation metrics for implicit-feedback recommender systems
  that are based on low-rank matrix factorization models, given the fitted model
  matrices and data, thus allowing to compare models from a variety of libraries.
  Metrics include P@K (precision-at-k, for top-K recommendations), R@K (recall at k),
  AP@K (average precision at k), NDCG@K (normalized discounted cumulative gain at k),
  Hit@K (from which the 'Hit Rate' is calculated), RR@K (reciprocal rank at k, from
  which the 'MRR' or 'mean reciprocal rank' is calculated), ROC-AUC (area under the
  receiver-operating characteristic curve), and PR-AUC (area under the
  precision-recall curve).
  These are calculated on a per-user basis according to the ranking of items induced
  by the model, using efficient multi-threaded routines. Also provides functions
  for creating train-test splits for model fitting and evaluation.

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.