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saehb.hnb

https://github.com/rakaikmana/saehb.hnb

Science Score: 13.0%

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Last synced: 7 months ago · JSON representation

Repository

Basic Info
  • Host: GitHub
  • Owner: rakaikmana
  • Language: R
  • Default Branch: main
  • Size: 44.9 KB
Statistics
  • Stars: 0
  • Watchers: 1
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Created about 4 years ago · Last pushed almost 4 years ago
Metadata Files
Readme

README.Rmd 

---
output: github_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```



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

# saeHB.hnb




This package provides a function and datasets for area level of Small Area Estimation under Hurdle Negative Binomial Model using Hierarchical Bayesian (HB) Method.

## Author
Raka Ikmana, Azka Ubaidillah

## Maintaner
Raka Ikmana <221810548@stis.ac.id>

## Function

* `HurdleNB()` This function gives small area estimator under Hurdle Negative Binomial Model and is implemented to variable of interest (y) that assumed to be a HNB Distribution. The value of variable of interest must be a non-negative data count.

## Installation

You can install the development version of `saeHB.hnb` from [GitHub](https://github.com/) with:

``` r
# install.packages("devtools")
devtools::install_github("rakaikmana/saeHB.hnb")
```

## Example

This is a basic example of using `HurdlenNB()` function to make an estimate based on synthetic data in this package

```{r example}
library(saeHB.hnb)
## For data without any non-sampled area
data(dataHNB)       # Load dataset

## For data with non-sampled area use dataHNBNs
## Fitting model
result <- HurdleNB(y ~ x1 + x2, data=dataHNB)
```

Small Area mean Estimates

```r
result$Est
```

Estimated model coefficient

```r
result$coefficient
```

Estimated random effect variances

```{r}
result$refVar
```


## References
* Hilbe, J. M. (2011). Negative Binomial Regression 2nd Edition. New York : Cambridge University Press. .
* Ntzoufras, I. (2009). Bayesian Modelling Using WinBUGS. New Jersey :  John Wiley & Sons, Inc.
* Rao, J.N.K & Molina. (2015). Small Area Estimation 2nd Edition. New Jersey : John Wiley & Sons, Inc. .

Owner

  • Login: rakaikmana
  • Kind: user

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Packages

  • Total packages: 1
  • Total downloads:
    • cran 136 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 0
  • Total versions: 3
  • Total maintainers: 1
cran.r-project.org: saeHB.hnb

Small Area Estimation under Hurdle Negative Binomial Distribution using Hierarchical Bayesian Method

  • Versions: 3
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 136 Last month
Rankings
Forks count: 28.8%
Dependent packages count: 29.8%
Stargazers count: 35.2%
Dependent repos count: 35.5%
Average: 43.2%
Downloads: 87.0%
Maintainers (1)
221810548@stis.ac.id
Last synced: over 2 years ago

Dependencies

DESCRIPTION cran
  • R >= 2.10 depends
  • coda * imports
  • grDevices * imports
  • graphics * imports
  • rjags * imports
  • stats * imports
  • stringr * imports
  • knitr * suggests
  • rmarkdown * suggests