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: 1.1.1
Imports: zoo, raster, reticulate
Published: 2023-09-20
DOI: 10.32614/CRAN.package.sisireg
Author: Lars Metzner [aut, cre]
Maintainer: Lars Metzner <lars.metzner at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
CRAN checks: sisireg results


Reference manual: sisireg.pdf


Package source: sisireg_1.1.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): sisireg_1.1.1.tgz, r-oldrel (arm64): sisireg_1.1.1.tgz, r-release (x86_64): sisireg_1.1.1.tgz, r-oldrel (x86_64): sisireg_1.1.1.tgz
Old sources: sisireg archive


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