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>.

An implementation of a phylogenetic comparative method. It can fit univariate among-species Ornstein-Uhlenbeck models of phenotypic trait evolution, where the trait evolves towards a primary optimum. The optimum can be modelled as a single parameter, as multiple discrete regimes on the phylogenetic tree, and/or with continuous covariates. See also Hansen (1997) <doi:10.2307/2411186>, Butler & King (2004) <doi:10.1086/426002>, Hansen et al. (2008) <doi:10.1111/j.1558-5646.2008.00412.x>.

A mostly pure-R implementation of the RAKE algorithm (Rose, S., Engel, D., Cramer, N. and Cowley, W. (2010) <doi:10.1002/9780470689646.ch1>), which can be used to extract keywords from documents without any training data.