A 'sparklyr' <https://spark.posit.co/> extension that provides an R
  interface for 'XGBoost' <https://github.com/dmlc/xgboost> on 'Apache Spark'. 
  'XGBoost' is an optimized distributed gradient boosting library.

Provides functions to estimate kernel-smoothed spatial and spatio-temporal densities and relative risk functions, and perform subsequent inference. Methodological details can be found in the accompanying tutorial: Davies et al. (2018) <DOI:10.1002/sim.7577>.

The SPARRA risk score (Scottish Patients At Risk of admission and Re-Admission) estimates yearly risk of emergency hospital admission using electronic health records on a monthly basis for most of the Scottish population. This package implements a suite of functions used to analyse the behaviour and performance of the score, focusing particularly on differential performance over demographically-defined groups. It includes useful utility functions to plot receiver-operator-characteristic, precision-recall and calibration curves, draw stock human figures, estimate counterfactual quantities without the need to re-compute risk scores, to simulate a semi-realistic dataset.

Calculate the statistical power to detect clusters using kernel-based 
        spatial relative risk functions that are estimated using the 'sparr' package.
        Details about the 'sparr' package methods can be found in the tutorial: Davies
        et al. (2018) <doi:10.1002/sim.7577>. Details about kernel density estimation 
        can be found in J. F. Bithell (1990) <doi:10.1002/sim.4780090616>. More 
        information about relative risk functions using kernel density estimation can 
        be found in J. F. Bithell (1991) <doi:10.1002/sim.4780101112>.

Implements the sparse biclustering proposal of Tan and Witten (2014), Sparse biclustering of transposable data. Journal of Computational and Graphical Statistics  23(4):985-1008.

'HJ-Biplot' is a multivariate method that allow represent multivariate data on a subspace of low dimension, in such a way that most of the variability of the information is captured in a few dimensions. This package implements three new techniques and constructs in each case the 'HJ-Biplot', adapting restrictions to reduce weights and / or produce zero weights in the dimensions, based on the regularization theories. It implements three methods of regularization: Ridge, LASSO and Elastic Net.