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").
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
1.2.1 |
rolling linux/jammy R-4.5 | sisireg_1.2.1.tar.gz |
190.9 KiB |
1.2.1 |
rolling linux/noble R-4.5 | sisireg_1.2.1.tar.gz |
191.1 KiB |
1.2.1 |
rolling source/ R- | sisireg_1.2.1.tar.gz |
29.1 KiB |
1.2.1 |
latest linux/jammy R-4.5 | sisireg_1.2.1.tar.gz |
190.9 KiB |
1.2.1 |
latest linux/noble R-4.5 | sisireg_1.2.1.tar.gz |
191.1 KiB |
1.2.1 |
latest source/ R- | sisireg_1.2.1.tar.gz |
29.1 KiB |
1.2.1 |
2026-04-26 source/ R- | sisireg_1.2.1.tar.gz |
29.1 KiB |
1.2.1 |
2026-04-23 source/ R- | sisireg_1.2.1.tar.gz |
29.1 KiB |
1.2.1 |
2026-04-09 windows/windows R-4.5 | sisireg_1.2.1.zip |
197.8 KiB |
1.1.2 |
2025-04-20 source/ R- | sisireg_1.1.2.tar.gz |
22.2 KiB |