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DSL::English::QuantileRegressionWorkflows zef:antononcube last updated on 2022-10-03


Quantile Regression Workflows

In brief

This Raku Perl 6 package has grammar classes and action classes for the parsing and interpretation of spoken commands that specify Quantile Regression (QR) workflows.

It is envisioned that the interpreters (actions) are going to target different programming languages: R, Mathematica, Python, etc.

The generated pipelines are for the software monads QRMon-R and QRMon-WL, [AA1, AA2].


1. Install Raku (Perl 6) : .

2. Make sure you have Zef Module Installer.

3. Open a command line program. (E.g. Terminal on Mac OS X.)

4. Run the commands:

zef install
zef install


Open a Raku IDE or type raku in the command line program. Try this Raku code:

use DSL::English::QuantileRegressionWorkflows;

say ToQuantileRegressionWorkflowCode(
    "compute quantile regression with 16 knots and probabilities 0.25, 0.5 and 0.75",
# QRMonQuantileRegression(df = 16, probabilities = c(0.25, 0.5, 0.75))

Here is a more complicated pipeline specification:

say ToQuantileRegressionWorkflowCode(
    "create from dfTemperatureData;
     compute quantile regression with 16 knots and probability 0.5;
     show date list plot with date origin 1900-01-01;
     show absolute errors plot;
     echo text anomalies finding follows;
     find anomalies by the threshold 5;
     take pipeline value;", "R-QRMon")

The command above should print out R code for the R package QRMon-R, [AA1]:

r QRMonUnit( data = dfTemperatureData) %>% QRMonQuantileRegression(df = 16, probabilities = c(0.5)) %>% QRMonPlot( datePlotQ = TRUE, dateOrigin = '1900-01-01') %>% QRMonErrorsPlot( relativeErrorsQ = FALSE) %>% QRMonEcho( "anomalies finding follows" ) %>% QRMonFindAnomaliesByResiduals( threshold = 5) %>% QRMonTakeValue


The original version of this Raku package was developed/hosted at [ AA3 ].

A dedicated GitHub repository was made in order to make the installation with Raku's zef more direct. (As shown above.)


[AA1] Anton Antonov, Quantile Regression Monad in R, (2019), QRMon-R at GitHub.

[AA2] Anton Antonov, Monadic Quantile Regression Mathematica package, (2018), MathematicaForPrediction at GitHub.

[AA3] Anton Antonov, Quantile Regression Workflows, (2019), ConversationalAgents at GitHub.