Package: NSAE 0.4.0
Saurav Guha
NSAE: Nonstationary Small Area Estimation
Executes nonstationary Fay-Herriot model and nonstationary generalized linear mixed model for small area estimation.The empirical best linear unbiased predictor (EBLUP) under stationary and nonstationary Fay-Herriot models and empirical best predictor (EBP) under nonstationary generalized linear mixed model along with the mean squared error estimation are included. EBLUP for prediction of non-sample area is also included under both stationary and nonstationary Fay-Herriot models. This extension to the Fay-Herriot model that accounts for the presence of spatial nonstationarity was developed by Hukum Chandra, Nicola Salvati and Ray Chambers (2015) <doi:10.1093/jssam/smu026> and nonstationary generalized linear mixed model was developed by Hukum Chandra, Nicola Salvati and Ray Chambers (2017) <doi:10.1016/j.spasta.2017.01.004>. This package is dedicated to the memory of Dr. Hukum Chandra who passed away while the package creation was in progress.
Authors:
NSAE_0.4.0.tar.gz
NSAE_0.4.0.tar.gz(r-4.5-noble)NSAE_0.4.0.tar.gz(r-4.4-noble)
NSAE_0.4.0.tgz(r-4.4-emscripten)NSAE_0.4.0.tgz(r-4.3-emscripten)
NSAE.pdf |NSAE.html✨
NSAE/json (API)
# Install 'NSAE' in R: |
install.packages('NSAE', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
- Wmatrix - Proximity matrix
- headcount - Head count data
- paddy - Yield data of paddy
- paddysample - Yield data of paddy for sample area
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 3 years agofrom:0526288fea. Checks:OK: 1 NOTE: 1. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 20 2024 |
R-4.5-linux | NOTE | Nov 20 2024 |
Exports:eblupFH1eblupFH2eblupNSFH1eblupNSFH2ebpebpNPebpNSebpSPNS.testNSglm.test
Dependencies:clusterdata.tablejsonlitelatticeMASSMatrixnlmenumDerivrlistSemiParspspDataspgwrXMLyaml