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.

Getting started

Package details

AuthorKatherine A. L. Valeriano [aut, cre] (<https://orcid.org/0000-0001-6388-4753>), Alejandro Ordonez Cuastumal [ctb] (<https://orcid.org/0000-0002-8157-9012>), Christian Galarza Morales [ctb] (<https://orcid.org/0000-0002-4818-6006>), Larissa Avila Matos [ctb] (<https://orcid.org/0000-0002-2635-0901>)
MaintainerKatherine A. L. Valeriano <katandreina@gmail.com>
LicenseGPL (>= 2)
Package repositoryView on CRAN
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RcppCensSpatial documentation built on June 28, 2022, 1:07 a.m.