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'.

Efficient implementations for Sorted L-One Penalized Estimation
    (SLOPE): generalized linear models regularized with the sorted L1-norm
    (Bogdan et al. 2015). Supported models include
    ordinary least-squares regression, binomial regression, multinomial
    regression, and Poisson regression. Both dense and sparse  predictor
    matrices are supported. In addition, the package features predictor
    screening rules that enable fast and efficient solutions to high-dimensional
    problems.

Calculates the slope (longitudinal gradient or steepness)
  of linear geographic features such as roads (for more details, see Ariza-López et al. (2019) 
  <doi:10.1038/s41597-019-0147-x>) and rivers (for more details, see Cohen et al. (2018) 
  <doi:10.1016/j.jhydrol.2018.06.066>). It can use local Digital Elevation Model (DEM) 
  data or download DEM data via the 'ceramic' package. The package also provides functions 
  to add elevation data to linestrings and visualize elevation profiles.

Provides tools for predicting ICU length of stay and assessing ICU efficiency. 
  It is based on the methodologies proposed by Peres et al. (2022, 2023), which utilize data-driven approaches for modeling and validation, offering insights into ICU performance and patient outcomes. References: Peres et al. (2022)<https://pubmed.ncbi.nlm.nih.gov/35988701/>, Peres et al. (2023)<https://pubmed.ncbi.nlm.nih.gov/37922007/>. 
  More information: <https://github.com/igor-peres/ICU-Length-of-Stay-Prediction>.