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msaedb

Difference Benchmarking for Multivariate Small Area Estimation (https://rdocumentation.org/packages/msaeDB/versions/0.2.1)

https://github.com/zazaperwira/msaedb

Science Score: 13.0%

This score indicates how likely this project is to be science-related based on various indicators:

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    CITATION.cff file
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    codemeta.json file
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    .zenodo.json file
  • ✓
    DOI references
    Found 3 DOI reference(s) in README
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    Low similarity (13.3%) to scientific vocabulary

Keywords

area-estimation benchmarking eblup fay-herriot-model mse produces-eblups sae small-area-estimation
Last synced: 6 months ago · JSON representation

Repository

Difference Benchmarking for Multivariate Small Area Estimation (https://rdocumentation.org/packages/msaeDB/versions/0.2.1)

Basic Info
  • Host: GitHub
  • Owner: zazaperwira
  • Language: R
  • Default Branch: master
  • Homepage:
  • Size: 229 KB
Statistics
  • Stars: 0
  • Watchers: 1
  • Forks: 1
  • Open Issues: 0
  • Releases: 0
Topics
area-estimation benchmarking eblup fay-herriot-model mse produces-eblups sae small-area-estimation
Created about 5 years ago · Last pushed almost 5 years ago
Metadata Files
Readme

README.Rmd 

---
output: github_document
---




```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
```

# msaeDB


[![Travis build status](https://travis-ci.com/zazaperwira/msaeDB.svg?branch=master)](https://travis-ci.com/zazaperwira/msaeDB)
[![AppVeyor build status](https://ci.appveyor.com/api/projects/status/github/zazaperwira/msaeDB?branch=master&svg=true)](https://ci.appveyor.com/project/zazaperwira/msaeDB)
[![Codecov test coverage](https://codecov.io/gh/zazaperwira/msaeDB/branch/master/graph/badge.svg)](https://codecov.io/gh/zazaperwira/msaeDB?branch=master)


Implements Benchmarking Method for Multivariate Small Area Estimation under Fay Herriot Model. Multivariate Small Area Estimation (MSAE) is a development of Univariate Small Area Estimation that considering the correlation among response variables and borrowing the strength from related areas and auxiliary variables to increase the effectiveness of sample size, the multivariate model in this package is based on multivariate model 1 proposed by Roberto Benavent and Domingo Morales (2016).  Benchmarking in Small Area Estimation is a modification of Small Area Estimation model to guarantee that the aggregate weighted mean of the county predictors equals the corresponding weighted mean of survey estimates. Difference Benchmarking is the simplest benchmarking method but widely used by multiplying empirical best linear unbiased prediction (EBLUP) estimator by the common adjustment factors (J.N.K Rao and Isabel Molina, 2015).

## Authors

Zaza Yuda Perwira, Azka Ubaidillah

## Maintainer 
Zaza Yuda Perwira <221710086@stis.ac.id>

## Functions
* `msaedb()` Produces EBLUPs, MSE, and Aggregation of Multivariate SAE with Difference Benchmarking
* `saedb()` Produces EBLUPs, MSE, and Aggregation of Univariate SAE with Difference Benchmarking
* `msaefh()` Produces EBLUPs and MSE of Multivariate SAE
* `saefh()` Produces EBLUPs and MSE of Univariate SAE


## References
* Benavent, Roberto & Morales, Domingo. (2016). Multivariate Fay-Herriot models for small area estimation. Computational Statistics and Data Analysis 94 2016 372-390. DOI: 10.1016/j.csda.2015.07.013.
* Rao, J.N.K & Molina. (2015). Small Area Estimation 2nd Edition. New York: John Wiley and Sons, Inc.
* Steorts, Rebecca & Ghosh, Malay. (2013). On estimation of mean square Errors of Benchmarked Empirical Bayes Estimators. Article in Statistics Sinica April 2013. DOI: 10.5705/ss.2012.053.
* Ubaidillah, Azka et al. (2019). Multivariate Fay-Herriot models for small area estimation with application to household consumption per capita expenditure in Indonesia. Journal of Applied Statistics. 46:15. 2845-2861. DOI: 10.1080/02664763.2019.1615420.
* Permatasari, Novia. (2020). Pembangunan paket R pada model Fay Herriot multivariat untuk pendugaan area kecil (Bachelor Thesis). Jakarta: Polytechnic Statistics of STIS

GitHub Events

Total
Last Year

Committers

Last synced: almost 3 years ago

All Time
  • Total Commits: 28
  • Total Committers: 2
  • Avg Commits per committer: 14.0
  • Development Distribution Score (DDS): 0.107
Top Committers
Name Email Commits
zazaperwira 2****6@s****d 25
zazaperwira 7****a@u****m 3
Committer Domains (Top 20 + Academic)
stis.ac.id: 1

Issues and Pull Requests

Last synced: over 2 years ago

All Time
  • Total issues: 1
  • Total pull requests: 0
  • Average time to close issues: 26 minutes
  • Average time to close pull requests: N/A
  • Total issue authors: 1
  • Total pull request authors: 0
  • Average comments per issue: 2.0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
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Top Authors
Issue Authors
  • myarist (1)
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Top Labels
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Packages

  • Total packages: 1
  • Total downloads:
    • cran 177 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 0
  • Total versions: 6
  • Total maintainers: 1
cran.r-project.org: msaeDB

Difference Benchmarking for Multivariate Small Area Estimation

  • Versions: 6
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 177 Last month
Rankings
Forks count: 21.9%
Dependent packages count: 29.8%
Stargazers count: 35.2%
Dependent repos count: 35.5%
Average: 37.5%
Downloads: 65.0%
Maintainers (1)
221710086@stis.ac.id
Last synced: over 2 years ago

Dependencies

DESCRIPTION cran
  • R >= 2.10 depends
  • MASS * imports
  • magic * imports
  • stats * imports
  • covr * suggests
  • knitr * suggests
  • rmarkdown * suggests