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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: | 1.1.2 |
| Imports: | zoo, reticulate |
| Published: | 2024-12-13 |
| DOI: | 10.32614/CRAN.package.sisireg |
| 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 |
| Reference manual: | sisireg.pdf |
| Package source: | sisireg_1.1.2.tar.gz |
| Windows binaries: | r-devel: sisireg_1.1.2.zip, r-release: sisireg_1.1.2.zip, r-oldrel: sisireg_1.1.2.zip |
| macOS binaries: | r-release (arm64): sisireg_1.1.2.tgz, r-oldrel (arm64): sisireg_1.1.2.tgz, r-release (x86_64): sisireg_1.1.2.tgz, r-oldrel (x86_64): sisireg_1.1.2.tgz |
| Old sources: | sisireg archive |
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These binaries (installable software) and packages are in development.
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