StempCens: Spatio-Temporal Estimation and Prediction for Censored/Missing Responses

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.

Version: 1.1.0
Imports: Rcpp, stats, utils, mvtnorm, tmvtnorm, MCMCglmm, ggplot2, grid, distances, Rdpack
LinkingTo: Rcpp, RcppArmadillo
Suggests: testthat
Published: 2020-10-21
DOI: 10.32614/CRAN.package.StempCens
Author: Larissa A. Matos ORCID iD [aut, cre], Katherine L. Valeriano ORCID iD [aut], Victor H. Lachos ORCID iD [ctb]
Maintainer: Larissa A. Matos <larissa.amatos at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
Citation: StempCens citation info
Materials: README NEWS
In views: MissingData
CRAN checks: StempCens results


Reference manual: StempCens.pdf


Package source: StempCens_1.1.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): StempCens_1.1.0.tgz, r-oldrel (arm64): StempCens_1.1.0.tgz, r-release (x86_64): StempCens_1.1.0.tgz, r-oldrel (x86_64): StempCens_1.1.0.tgz
Old sources: StempCens archive

Reverse dependencies:

Reverse imports: RcppCensSpatial


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