A framework for defining 'pipelines' of functions for applying data transformations,
model estimation and inverse-transformations, resulting in predicted value generation (or
model-scoring) functions that automatically apply the entire pipeline of functions required to go
from input to predicted output.
knitr, testthat, rmarkdown, modelr, tidyverse

Diagnostics, Reliability and Predictive Maintenance of Pipeline
Systems
person("Yuri", "Possokhov", email = "possokhoff@gmail.com",
role = c("aut", "cre"), comment = c(ORCID = "0000-0002-3570-4337"))
Functions representing some useful empirical and data-driven
models of heat losses, corrosion diagnostics, reliability and predictive
maintenance of pipeline systems. The package is an option for digital
transformation of technical engineering departments of heat generating and
heat transferring companies. Methods are described in
Timashev et al. (2016) <doi:10.1007/978-3-319-25307-7>,
A.C.Reddy (2017) <doi:10.1016/j.matpr.2017.07.081>,
Minenergo (2008) <https://docs.cntd.ru/document/902148459>,
Minenergo (2005) <http://www.complexdoc.ru/ntdtext/547103>.
Yuri Possokhov [aut, cre] (<https://orcid.org/0000-0002-3570-4337>)

An implementation of the generated effect modifier (GEM) method. This method constructs composite variables by linearly combining pre-treatment scalar patient characteristics to create optimal treatment effect modifiers in linear models. The optimal linear combination is called a GEM. Treatment is assumed to have been assigned at random. For reference, see E Petkova, T Tarpey, Z Su, and RT Ogden. Generated effect modifiers (GEMs) in randomized clinical trials. Biostatistics (First published online: July 27, 2016, <doi:10.1093/biostatistics/kxw035>).
plyr, MASS, ggplot2, Rcpp, RcppArmadillo

Calculate Pi Ratings for Teams Competing in Sport Matches
Calculate and optimize dynamic performance ratings of association football
teams competing in matches, in accordance with the method used in
the research paper "Determining the level of ability of football teams by
dynamic ratings based on the relative discrepancies in scores between adversaries",
by Constantinou and Fenton (2013)
<doi:10.1515/jqas-2012-0036>
This dynamic rating system has proven to provide superior
results for predicting association football outcomes.