sisireg: Sign-Simplicity-Regression-Solver

Implementation of the SSR-Algorithm. The Sign-Simplicity-Regression model is a nonparametric statistical model which is based on residual signs and simplicity assumptions on the regression function. Goal is to calculate the most parsimonious regression function satisfying the statistical adequacy requirements. Theory and functions are specified in Metzner (2020, ISBN: 979-8-68239-420-3, "Trendbasierte Prognostik") and Metzner (2021, ISBN: 979-8-59347-027-0, "Adäquates Maschinelles Lernen").

Version:	1.0.0
Imports:	zoo, raster
Published:	2022-01-04
Author:	Lars Metzner [aut, cre]
Maintainer:	Lars Metzner <lars.metzner at ppi.de>
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation:	yes
CRAN checks:	sisireg results

Documentation:

Reference manual:

sisireg.pdf

Downloads:

Package source:	sisireg_1.0.0.tar.gz
Windows binaries:	r-devel: sisireg_1.0.0.zip, r-release: sisireg_1.0.0.zip, r-oldrel: sisireg_1.0.0.zip
macOS binaries:	r-release (arm64): sisireg_1.0.0.tgz, r-oldrel (arm64): sisireg_1.0.0.tgz, r-release (x86_64): sisireg_1.0.0.tgz, r-oldrel (x86_64): sisireg_1.0.0.tgz
Old sources:	sisireg archive

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