Quantile Regression Monad in R

This repository is for the R implementation of a software monad for Quantile Regression
workflows called Quantile Regression Monad (QRMon).

The R-implementation follows the Mathematica QRMon package
"MonadicQuantileRegression.m",
[AAp1].
The Mathematica QRMon package is extensively documented with
"A monad for Quantile Regression workflows",
[AA1].

The usage of this R implementation is explained in detail in the vignette
"Rapid making of Quantile Regression workflows".

Here is how to install the package:

devtools::install_github("antononcube/QRMon-R")

Here is a workflow (pipeline) example:

qrmon <-
  QRMonUnit( dfTemperatureData ) %>%
  QRMonEchoDataSummary() %>%
  QRMonQuantileRegression( df = 16, degree = 3, probabilities = seq(0.1,0.9,0.2) ) %>%
  QRMonPlot( datePlotQ = TRUE, dateOrigin = "1900-01-01" )

There is a Domain Specific Language (DSL) parser-interpreter implemented in Raku
that can be used to generate QRMon code using natural language commands; see
[AAr1].

References

Articles, books

[RK1] Roger Koenker,
Quantile Regression,
Cambridge University Press, 2005.

[RK2] Roger Koenker,
"Quantile Regression in R: a vignette",
(2006),
CRAN.

[AA1] Anton Antonov,
"A monad for Quantile Regression workflows",
(2018),
MathematicaForPrediction at GitHub.

Packages

[RKp1] Roger Koenker,
quantreg,
CRAN.

[AAp1] Anton Antonov,
Quantile Regression Mathematica package,
(2014),
MathematicaForPrediction at GitHub.

[AAp2] Anton Antonov,
Monadic Quantile Regression Mathematica package,
(2018),
MathematicaForPrediction at GitHub.

[AAp3] Anton Antonov,
QuantileRegression,
(2019),
Wolfram Function Repository.

Repositories

[AAr1] Anton Antonov,
DSL::English::QuantileRegressionWorkflows in Raku,
(2020),
GitHub/antononcube.

Videos

[AAv1] Anton Antonov,
"Boston useR! QuantileRegression Workflows 2019-04-18",
(2019),
Anton Antonov at YouTube.

[AAv2] Anton Antonov,
"useR! 2020: How to simplify Machine Learning workflows specifications",
(2020),
R Consortium at YouTube.