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
DOI: 10.32614/
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>
License: GPL (≥ 3)
NeedsCompilation: no
Language: en-US
Materials: README NEWS
CRAN checks: sr results


Reference manual: sr.pdf
Vignettes: Selecting Predictors with the Gamma Test


Package source: sr_0.1.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): sr_0.1.0.tgz, r-oldrel (arm64): sr_0.1.0.tgz, r-release (x86_64): sr_0.1.0.tgz, r-oldrel (x86_64): sr_0.1.0.tgz


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