sr: Smooth Regression - The Gamma Test and Tools
Finds causal connections in precision data, finds lags and embeddings in
time series, guides training of neural networks and other smooth models, evaluates
their performance, gives a mathematically grounded answer to the over-training
problem. Smooth regression is based on the Gamma test, which measures smoothness
in a multivariate relationship. Causal relations are smooth, noise is not.
'sr' includes the Gamma test and search techniques that use it.
References: Evans & Jones (2002) <doi:10.1098/rspa.2002.1010>,
AJ Jones (2004) <doi:10.1007/s10287-003-0006-1>.
| Version: |
0.1.0 |
| Depends: |
R (≥ 3.5.0) |
| Imports: |
ggplot2, dplyr, progress, RANN, stats, vdiffr |
| Suggests: |
knitr, magrittr, nnet, rmarkdown, testthat (≥ 3.0.0) |
| Published: |
2023-03-10 |
| Author: |
Wayne Haythorn [aut, cre],
Antonia Jones [aut] (Principal creator of the Gamma test),
Sam Kemp [ctb] (Wrote the original code for the Gamma test in R) |
| Maintainer: |
Wayne Haythorn <support at smoothregression.com> |
| BugReports: |
https://github.com/haythorn/sr/issues |
| License: |
GPL (≥ 3) |
| URL: |
https://smoothregression.com, https://github.com/haythorn/sr/ |
| NeedsCompilation: |
no |
| Language: |
en-US |
| Materials: |
README NEWS |
| CRAN checks: |
sr results |
Documentation:
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