This sparklyr extension makes 'Flint' time series
    library functionalities (<https://github.com/twosigma/flint>) easily
    accessible through R.

A 'sparklyr' extension adding the capability to work easily with nested data.

Generate syntax for use with the sparklines package for
        LaTeX.

A 'sparklyr' extension that enables reading and writing 'TensorFlow'
  TFRecord files via 'Apache Spark'.

Load WARC (Web ARChive) files into Apache Spark using 'sparklyr'. This
    allows to read files from the Common Crawl project <http://commoncrawl.org/>.

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>.