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README.Rmd
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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
dpi = 300,
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# melt <a href="https://docs.ropensci.org/melt/"><img src="man/figures/logo.png" align="right" height="139" alt="melt website" /></a>
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[](https://app.codecov.io/gh/ropensci/melt?branch=main)
[](https://CRAN.R-project.org/package=melt)
[](http://ropensci.r-universe.dev/ui/#package:melt)
[](https://github.com/ropensci/software-review/issues/550)
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## Overview
melt provides a unified framework for data analysis with empirical likelihood methods.
A collection of functions is available to perform multiple empirical likelihood tests and construct confidence intervals for various models in 'R'.
melt offers an easy-to-use interface and flexibility in specifying hypotheses and calibration methods, extending the framework to simultaneous inferences.
The core computational routines are implemented with the 'Eigen' 'C++' library and 'RcppEigen' interface, with 'OpenMP' for parallel computation.
Details of the testing procedures are provided in [Kim, MacEachern, and Peruggia (2023)](https://doi.org/10.1080/10485252.2023.2206919).
The package has a companion paper by [Kim, MacEachern, and Peruggia (2024)](https://doi.org/10.18637/jss.v108.i05).
This work was supported by the U.S. National Science Foundation under Grants No. [SES-1921523](https://www.nsf.gov/awardsearch/showAward?AWD_ID=1921523) and [DMS-2015552](https://www.nsf.gov/awardsearch/showAward?AWD_ID=2015552).
## Installation
You can install the latest stable release of melt from CRAN.
```{r, eval = FALSE}
install.packages("melt")
```
You can install the development version of melt from GitHub or [R-universe](http://ropensci.r-universe.dev/ui/#package:melt).
```{r, eval = FALSE}
# install.packages("pak")
pak::pak("ropensci/melt")
```
```{r eval = FALSE}
install.packages("melt", repos = "https://ropensci.r-universe.dev")
```
## Main functions
melt provides an intuitive API for performing the most common data analysis tasks:
* `el_mean()` computes empirical likelihood for the mean.
* `el_lm()` fits a linear model with empirical likelihood.
* `el_glm()` fits a generalized linear model with empirical likelihood.
* `confint()` computes confidence intervals for model parameters.
* `confreg()` computes confidence region for model parameters.
* `elt()` tests a hypothesis with various calibration options.
* `elmt()` performs multiple testing simultaneously.
## Usage
```{r usage, eval = TRUE}
library(melt)
set.seed(971112)
## Test for the mean
data("precip")
(fit <- el_mean(precip, par = 30))
## Adjusted empirical likelihood calibration
elt(fit, rhs = 30, calibrate = "ael")
## Bootstrap calibration
elt(fit, rhs = 30, calibrate = "boot")
## F calibration
elt(fit, rhs = 30, calibrate = "f")
## Linear model
data("mtcars")
fit_lm <- el_lm(mpg ~ disp + hp + wt + qsec, data = mtcars)
summary(fit_lm)
cr <- confreg(fit_lm, parm = c("disp", "hp"), npoints = 200)
plot(cr)
data("clothianidin")
fit2_lm <- el_lm(clo ~ -1 + trt, data = clothianidin)
summary(fit2_lm)
confint(fit2_lm)
## Generalized linear model
data("thiamethoxam")
fit_glm <- el_glm(visit ~ log(mass) + fruit + foliage + var + trt,
family = quasipoisson(link = "log"), data = thiamethoxam,
control = el_control(maxit = 100, tol = 1e-08, nthreads = 4)
)
summary(fit_glm)
## Test of no treatment effect
contrast <- c(
"trtNaked - trtFungicide", "trtFungicide - trtLow", "trtLow - trtHigh"
)
elt(fit2_lm, lhs = contrast)
## Multiple testing
contrast2 <- rbind(
c(0, 0, 0, 0, 0, 1, 0, 0),
c(0, 0, 0, 0, 0, 0, 1, 0),
c(0, 0, 0, 0, 0, 0, 0, 1)
)
elmt(fit_glm, lhs = contrast2)
```
---
Please note that this package is released with a [Contributor Code of Conduct](https://ropensci.org/code-of-conduct/). By contributing to this project, you agree to abide by its terms.