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saeMSPE
View on CRAN: Click
here
Download and install saeMSPE package within the R console
Install from CRAN:
install.packages("saeMSPE")
Install from Github:
library("remotes")
install_github("cran/saeMSPE")
Install by package version:
library("remotes")
install_version("saeMSPE", "1.4")
Attach the package and use:
library("saeMSPE")
Maintained by
Peiwen Xiao
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2022-03-29
Latest Update: 2022-10-21
Description:
We describe a new R package entitled 'saeMSPE' for the well-known Fay Herriot model and nested error regression model in small area estimation. Based on this package, it is possible to easily compute various common mean squared predictive error (MSPE) estimators, as well as several existing variance component predictors as a byproduct, for these two models.
How to cite:
Peiwen Xiao (2022). saeMSPE: Computing MSPE Estimates in Small Area Estimation. R package version 1.4, https://cran.r-project.org/web/packages/saeMSPE. Accessed 04 Jan. 2025.
Previous versions and publish date:
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Other R packages that saeMSPE depends,
imports, suggests or enhances
Complete documentation for saeMSPE
Functions, R codes and Examples using
the saeMSPE R package
Some associated functions: mspeFHdb . mspeFHjack . mspeFHlin . mspeFHpb . mspeFHsumca . mspeNERdb . mspeNERjack . mspeNERlin . mspeNERpb . mspeNERsumca . saeMSPE-package . varfh . varner . wheatarea .
Some associated R codes: RcppExports.R . mspeFHlin.R . mspeNERlin.R . Full saeMSPE package functions and examples
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