rego: Automatic Time Series Forecasting and Missing Value Imputation

Machine learning algorithm for predicting and imputing time series. It can automatically set all the parameters needed, thus in the minimal configuration it only requires the target variable and the dependent variables if present. It can address large problems with hundreds or thousands of dependent variables and problems in which the number of dependent variables is greater than the number of observations. Moreover it can be used not only for time series but also for any other real valued target variable. The algorithm implemented includes a Bayesian stochastic search methodology for model selection and a robust estimation based on bootstrapping. 'rego' is fast because all the code is C++.

Version: 1.5.2
Depends: R (≥ 3.5.0)
Imports: Rcpp
LinkingTo: Rcpp
Published: 2022-05-26
Author: Davide Altomare [cre, aut], David Loris [aut]
Maintainer: Davide Altomare <info at channelattribution.io>
BugReports: https://github.com/DavideAltomare/rego/issues
License: MIT + file LICENSE
Copyright: see file COPYRIGHTS
URL: https://channelattribution.io/docs/rego
NeedsCompilation: yes
SystemRequirements: GNU make
CRAN checks: rego results

Documentation:

Reference manual: rego.pdf

Downloads:

Package source: rego_1.5.2.tar.gz
Windows binaries: r-devel: rego_1.5.2.zip, r-release: rego_1.4.1.zip, r-oldrel: rego_1.5.1.zip
macOS binaries: r-release (arm64): rego_1.5.2.tgz, r-oldrel (arm64): rego_1.5.2.tgz, r-release (x86_64): rego_1.4.1.tgz, r-oldrel (x86_64): rego_1.4.1.tgz
Old sources: rego archive

Linking:

Please use the canonical form https://CRAN.R-project.org/package=rego to link to this page.