Sparse Linear Method(SLIM) predicts ratings and top-n
    recommendations suited for sparse implicit positive feedback systems. SLIM
    is decomposed into multiple elasticnet optimization problems which are solved
    in parallel over multiple cores. The package is based on "SLIM: Sparse Linear
    Methods for Top-N Recommender Systems" by Xia Ning and George Karypis <doi:10.1109/ICDM.2011.134>.

Provides functions for performing common tasks when working with
  slippy map tile service APIs e.g. Google maps, Open Street Map, Mapbox, Stamen,
  among others. Functionality includes converting from latitude and longitude to
  tile numbers, determining tile bounding boxes, and compositing tiles to a
  georeferenced raster image.

Provides statistical procedures for linear regression in the general context where the errors are assumed to be correlated. Different ways to estimate the asymptotic covariance matrix of the least squares estimators are available. Starting from this estimation of the covariance matrix, the confidence intervals and the usual tests on the parameters are modified. The functions of this package are very similar to those of 'lm': it contains methods such as summary(), plot(), confint() and predict(). The 'slm' package is described in the paper by E. Caron, J. Dedecker and B. Michel (2019), "Linear regression with stationary errors: the R package slm", arXiv preprint <arXiv:1906.06583>.

Performance evaluation metrics for supervised and unsupervised machine learning, statistical learning and artificial intelligence applications. Core computations are implemented in 'C++' for scalability and efficiency.

Stepwise models for the optimal linear combination of continuous variables in binary classification problems under Youden Index optimisation. Information on the models implemented can be found at Aznar-Gimeno et al. (2021) <doi:10.3390/math9192497>.

A collection of helper functions designed to help
    you to better understand object oriented programming in R,
    particularly using 'S3'.