CRAN/E | RcppCensSpatial

RcppCensSpatial

Spatial Estimation and Prediction for Censored/Missing Responses

Installation

About

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.

Key Metrics

Version 0.3.0
R ≥ 2.10
Published 2022-06-27 722 days ago
Needs compilation? yes
License GPL-2
License GPL-3
CRAN checks RcppCensSpatial results

Downloads

Yesterday 3 0%
Last 7 days 35 -27%
Last 30 days 171 -24%
Last 90 days 572 -17%
Last 365 days 2.283 -6%

Maintainer

Maintainer

Katherine A. L. Valeriano

Authors

Katherine A. L. Valeriano

aut / cre

Alejandro Ordonez Cuastumal

ctb

Christian Galarza Morales

ctb

Larissa Avila Matos

ctb

Material

README
NEWS
Reference manual
Package source

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

RcppCensSpatial archive

Depends

R ≥ 2.10

Imports

ggplot2
gridExtra
MomTrunc
mvtnorm
Rcpp
Rdpack
relliptical
stats
StempCens

LinkingTo

RcppArmadillo
Rcpp
RcppProgress
roptim