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StempCens
View on CRAN: Click
here
Download and install StempCens package within the R console
Install from CRAN:
install.packages("StempCens")
Install from Github:
library("remotes")
install_github("cran/StempCens") Install by package version:
library("remotes")
install_version("StempCens", "1.2.0") Attach the package and use:
library("StempCens")
Maintained by
Larissa A. Matos
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[Scholar Profile | Author Map]
All associated links for this package
First Published: 2019-02-08
Latest Update: 2025-06-11
Description:
It estimates the parameters of a censored or missing data in spatio-temporal models using the SAEM algorithm (Delyon et al., 1999). This algorithm is a stochastic approximation of the widely used EM algorithm and an important tool for models in which the E-step does not have an analytic form. Besides the expressions obtained to estimate the parameters to the proposed model, we include the calculations for the observed information matrix using the method developed by Louis (1982). To examine the performance of the fitted model, case-deletion measure are provided.
How to cite:
Larissa A. Matos (2019). StempCens: Spatio-Temporal Estimation and Prediction for Censored/Missing Responses. R package version 1.2.0, https://cran.r-project.org/web/packages/StempCens. Accessed 13 Aug. 2026.
Previous versions and publish date:
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Other R packages that StempCens depends,
imports, suggests or enhances
Complete documentation for StempCens
Functions, R codes and Examples using
the StempCens R package
Some associated functions: CovarianceM . CrossStempCens . DiagStempCens . EffectiveRange . EstStempCens . PredStempCens . StempCens-package .
Some associated R codes: CovarianceM.R . CrossStempCens.R . DiagStempCens.R . EffectiveRange.R . EstStempCens.R . PredStempCens.R . RcppExports.R . StempCens-package.R . utils_SpatioTemporal_Rcpp.R . Full StempCens package functions and examples
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