Package: rego 1.6.1

Davide Altomare

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++.

Authors:Davide Altomare [cre, aut], David Loris [aut]

rego_1.6.1.tar.gz
rego_1.6.1.tar.gz(r-4.5-noble)rego_1.6.1.tar.gz(r-4.4-noble)
rego_1.6.1.tgz(r-4.4-emscripten)rego_1.6.1.tgz(r-4.3-emscripten)
rego.pdf |rego.html
rego/json (API)

# Install 'rego' in R:
install.packages('rego', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/davidealtomare/rego/issues

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:
  • Data - Airline Passenger Dataset

cpp

1.00 score 9 scripts 211 downloads 1 exports 1 dependencies

Last updated 1 years agofrom:9d861f0989. Checks:OK: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKDec 24 2024
R-4.5-linux-x86_64OKNov 24 2024

Exports:regpred

Dependencies:Rcpp