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].

[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.

[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.

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

[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.