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mtsdi  

Multivariate Time Series Data Imputation
View on CRAN: Click here


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

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

Install by package version:
library("remotes")
install_version("mtsdi", "0.3.7")



Attach the package and use:
library("mtsdi")
Maintained by
Washington Junger
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2012-10-29
Latest Update: 2025-04-09
Description:
This is an EM algorithm based method for imputation of missing values in multivariate normal time series. The imputation algorithm accounts for both spatial and temporal correlation structures. Temporal patterns can be modeled using an ARIMA(p,d,q), optionally with seasonal components, a non-parametric cubic spline or generalized additive models with exogenous covariates. This algorithm is specially tailored for climate data with missing measurements from several monitors along a given region.
How to cite:
Washington Junger (2012). mtsdi: Multivariate Time Series Data Imputation. R package version 0.3.7, https://cran.r-project.org/web/packages/mtsdi. Accessed 29 Oct. 2026.
Previous versions and publish date:
(2026-07-09 06:33), 0.3.1 (2012-10-29 08:59), 0.3.3 (2012-11-08 17:53), 0.3.5 (2018-01-23 22:52)
Other packages that cited mtsdi R package
View mtsdi citation profile
Other R packages that mtsdi depends, imports, suggests or enhances
Complete documentation for mtsdi
Functions, R codes and Examples using the mtsdi R package
Some associated functions: edaprep . elapsedtime . getmean . internal . miss . mkjnw . mnimput . mstats . plot.mtsdi . predict.mtsdi . print.mtsdi . print.summary.mtsdi . summary.mtsdi . 
Some associated R codes: Full mtsdi package functions and examples
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