RcppCensSpatial: Spatial Estimation and Prediction for Censored/Missing Responses
It provides functions to estimate parameters in linear spatial models with
censored/missing responses via the Expectation-Maximization (EM), the Stochastic
Approximation EM (SAEM), or the Monte Carlo EM (MCEM) algorithm. These algorithms
are widely used to compute the maximum likelihood (ML) estimates in problems with
incomplete data. The EM algorithm computes the ML estimates when a closed expression
for the conditional expectation of the complete-data log-likelihood function is
available. In the MCEM algorithm, the conditional expectation is substituted by a
Monte Carlo approximation based on many independent simulations of the missing data.
In contrast, the SAEM algorithm splits the E-step into simulation and integration
steps. This package also approximates the standard error of the estimates using the
Louis method. Moreover, it has a function that performs spatial prediction in new
locations.
| Version: |
0.3.0 |
| Depends: |
R (≥ 2.10) |
| Imports: |
ggplot2, gridExtra, MomTrunc, mvtnorm, Rcpp, Rdpack, relliptical, stats, StempCens |
| LinkingTo: |
RcppArmadillo, Rcpp, RcppProgress, roptim |
| Published: |
2022-06-27 |
| DOI: |
10.32614/CRAN.package.RcppCensSpatial |
| Author: |
Katherine A. L. Valeriano
 [aut, cre],
Alejandro Ordonez Cuastumal
[ctb],
Christian Galarza Morales
[ctb],
Larissa Avila Matos
[ctb] |
| Maintainer: |
Katherine A. L. Valeriano <katandreina at gmail.com> |
| License: |
GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: |
yes |
| Materials: |
README NEWS |
| In views: |
MissingData |
| CRAN checks: |
RcppCensSpatial results |
Documentation:
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