The ecocrop model estimates environmental suitability for plants using a limiting factor approach for plant growth following Hackett (1991) <doi:10.1007/BF00045728>. The implementation in this package is fast and flexible: it allows for the use of any (environmental) predictor variable. Predictors can be either static (for example, soil pH) or dynamic (for example, monthly precipitation).

Contains functions to interface with variables and variable details sheets, including recoding variables and converting them to PMML.

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.