Annotate text with entities and the relations between them. Annotate areas of interest in images with your labels. 
    Providing 'htmlwidgets' bindings to the 'recogito' <https://github.com/recogito/recogito-js> and 'annotorious' <https://github.com/recogito/annotorious> libraries.

Automatic, semi-automatic, and manual functions for
    generating color maps from images. The idea is to simplify
    the colors of an image according to a metric that is useful for
    the user, using deterministic methods whenever possible.
    Many images will be clustered well using the out-of-the-box
    functions, but the package also includes a toolbox of functions
    for making manual adjustments (layer merging/isolation, blurring,
    fitting to provided color clusters or those from another image, etc).
    Also includes export methods for other color/pattern analysis packages
    (pavo, patternize, colordistance).

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>.