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rego  

Automatic Time Series Forecasting and Missing Value Imputation
View on CRAN: Click here


Download and install rego package within the R console
Install from CRAN:
install.packages("rego")

Install from Github:
library("remotes")
install_github("cran/rego")

Install by package version:
library("remotes")
install_version("rego", "1.6.1")



Attach the package and use:
library("rego")
Maintained by
Davide Altomare
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2022-01-05
Latest Update: 2023-08-09
Description:
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++.
How to cite:
Davide Altomare (2022). rego: Automatic Time Series Forecasting and Missing Value Imputation. R package version 1.6.1, https://cran.r-project.org/web/packages/rego
Previous versions and publish date:
1.2.2 (2022-01-05 11:00), 1.2.3 (2022-01-07 14:52), 1.3.2 (2022-01-28 18:00), 1.3.4 (2022-02-05 01:20), 1.3.5 (2022-03-16 21:20), 1.4.1 (2022-05-10 14:40), 1.5.1 (2022-05-26 14:00), 1.5.2 (2022-05-26 18:10)
Other packages that cited rego R package
View rego citation profile
Other R packages that rego depends, imports, suggests or enhances
Functions, R codes and Examples using the rego R package
Some associated functions: Data . rego-package . regpred . 
Some associated R codes: rego.R .  Full rego package functions and examples
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