vignette updated

DESCRIPTION

@@ -9,16 +9,18 @@ Authors@R: c(
     person("Pierre", "Chausse", role = "rev"),
     person("Alex", "Stringer", role = "rev")
   )
-Description: Performs multiple empirical likelihood tests. It 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 using 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) <doi:10.1080/10485252.2023.2206919>. A companion paper by 
-    Kim, MacEachern, and Peruggia (2024) <doi:10.18637/jss.v108.i05> is 
-    available for further information. This work was supported by the U.S. 
-    National Science Foundation under Grants No. SES-1921523 and DMS-2015552.
+Description: Performs multiple empirical likelihood tests. It 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 using 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)
+    <doi:10.1080/10485252.2023.2206919>. A companion paper by Kim,
+    MacEachern, and Peruggia (2024) <doi:10.18637/jss.v108.i05> is
+    available for further information. This work was supported by the U.S.
+    National Science Foundation under Grants No. SES-1921523 and
+    DMS-2015552.
 License: GPL (>= 2)
 URL: https://docs.ropensci.org/melt/, https://github.com/ropensci/melt
 BugReports: https://github.com/ropensci/melt/issues
@@ -32,10 +34,11 @@ Imports:
     utils
 Suggests: 
     covr,
+    dplyr,
     ggplot2,
     knitr,
+    MASS,
     microbenchmark,
-    R.rsp,
     rmarkdown,
     spelling,
     testthat (>= 3.0.0),
@@ -46,8 +49,7 @@ LinkingTo:
     Rcpp,
     RcppEigen
 VignetteBuilder: 
-    knitr,
-    R.rsp
+    knitr
 Config/testthat/edition: 3
 Encoding: UTF-8
 Language: en-US

melt.Rproj

@@ -19,6 +19,5 @@ BuildType: Package
 PackageUseDevtools: Yes
 PackageCleanBeforeInstall: No
 PackageInstallArgs: --no-multiarch --with-keep.source
-PackageBuildArgs: --compact-vignettes=both --no-build-vignettes
 PackageCheckArgs: --as-cran --ignore-vignettes --no-tests
 PackageRoxygenize: rd,collate,namespace,vignette

vignettes/model.Rmd

@@ -11,53 +11,67 @@ vignette: >
   %\VignetteEncoding{UTF-8}
 ---
 
-```{r, include = FALSE}
+```{r, include=FALSE}
 knitr::opts_chunk$set(
   collapse = TRUE,
   comment = "#>",
   dpi = 300
 )
 ```
-```{r setup, echo=FALSE}
+```{r, echo=FALSE}
 library(melt, warn.conflicts = FALSE)
 ```
+
 ## Fitting an `EL` object
-The melt package provides several functions to construct an `EL` object or an object that inherits from `EL`:
+The melt package provides several functions to construct an `EL` object or an 
+object that inherits from `EL`:
 
 * `el_mean()` for the mean.
 * `el_sd()` for the standard deviation.
 * `el_lm()` for linear models.
 * `el_glm()` for generalized linear models.
 
 We illustrate the usage of `el_mean()` with the `faithful` data set. 
-```{r, eval = TRUE}
+
+```{r, eval=TRUE}
 data("faithful")
 str(faithful)
 summary(faithful)
 ```
-Suppose we are interested in evaluating empirical likelihood at (3.5, 70).
+
+Suppose we are interested in evaluating empirical likelihood at `c(3.5, 70)`.
+
 ```{r}
 fit <- el_mean(faithful, par = c(3.5, 70))
 class(fit)
 showClass("EL")
 ```
-The `faithful` data frame is coerced to a numeric matrix. Simple print method shows essential information on `fit`. 
+
+The `faithful` data frame is coerced to a numeric matrix. Simple print method 
+shows essential information on `fit`. 
+
 ```{r}
 fit
 ```
-Note that the maximum empirical likelihood estimates are the same as the sample average. 
-The chi-square value shown corresponds to the minus twice the empirical log-likelihood ratio. 
-It has an asymptotic chi-square distribution of 2 degrees of freedom under the null hypothesis. 
-Hence the $p$-value here is not exact. 
-The convergence status at the bottom can be used to check the convex hull constraint. 
-
-Weighted data can be handled by supplying the `weights` argument. 
-For non-`NULL` `weights`, weighted empirical likelihood is computed. 
-Any valid `weights` is re-scaled for internal computation to add up to the total number of observations. 
-For simplicity, we use `faithful$waiting` as our weight vector.
+
+Note that the maximum empirical likelihood estimates are the same as the sample 
+average. The chi-square value shown corresponds to the minus twice the empirical
+log-likelihood ratio. It has an asymptotic chi-square distribution of 2 degrees 
+of freedom under the null hypothesis. Hence the $p$-value here is not exact. The
+convergence status at the bottom can be used to check the convex hull 
+constraint. 
+
+Weighted data can be handled by supplying the `weights` argument. For non-`NULL`
+`weights`, weighted empirical likelihood is computed. Any valid `weights` is 
+re-scaled for internal computation to add up to the total number of 
+observations. For simplicity, we use `faithful$waiting` as our weight vector.
+
 ```{r}
 w <- faithful$waiting
 (wfit <- el_mean(faithful, par = c(3.5, 70), weights = w))
 ```
-We get different results, where the estimates are now the weighted sample average. 
-The chi-square value and the associated $p$-value are based on the same limit theorem, but care must be taken when interpreting the results since they are largely affected by the limiting behavior of the weights.
+
+We get different results, where the estimates are now the weighted sample 
+average. The chi-square value and the associated $p$-value are based on the same 
+limit theorem, but care must be taken when interpreting the results since they 
+are largely affected by the limiting behavior of the weights.

vignettes/performance.Rmd

@@ -7,7 +7,7 @@ vignette: >
   %\VignetteEngine{knitr::rmarkdown}
   %\VignetteEncoding{UTF-8}
 ---
-```{r, include = FALSE}
+```{r, include=FALSE}
 knitr::opts_chunk$set(
   collapse = TRUE,
   comment = "#>",
@@ -18,21 +18,31 @@ knitr::opts_chunk$set(
   out.width = "100%"
 )
 ```
-All the tests were done on an Arch Linux x86_64 machine with an Intel(R) Core(TM) i7 CPU (1.90GHz).
-We first load the necessary packages.
-```{r setup}
-library(melt)
-library(microbenchmark)
-library(ggplot2)
+```{r, echo=FALSE}
+library(melt, warn.conflicts = FALSE)
 ```
 
+All the tests were done on an Arch Linux x86_64 machine with an Intel(R) 
+Core(TM) i7 CPU (1.90GHz).
+
+
 ## Empirical likelihood computation
-We show the performance of computing empirical likelihood with `el_mean()`.
-We test the computation speed with simulated data sets in two different settings: 1) the number of observations increases with the number of parameters fixed, and 2) the number of parameters increases with the number of observations fixed. 
 
-### Increasing the number of observations
-We fix the number of parameters at $p = 10$, and simulate the parameter value and $n \times p$ matrices using `rnorm()`. In order to ensure convergence with a large $n$, we set a large threshold value using `el_control()`.
+We show the performance of computing empirical likelihood with `el_mean()`. We 
+test the computation speed with simulated data sets in two different settings: 
+1) the number of observations increases with the number of parameters fixed, 
+and 2) the number of parameters increases with the number of observations fixed. 
+
+
+## Increasing the number of observations
+
+We fix the number of parameters at \(p = 10\), and simulate the parameter value 
+and \(n \times p\) matrices using `rnorm()`. In order to ensure convergence with 
+a large \(n\), we set a large threshold value using `el_control()`.
+
 ```{r}
+library(ggplot2)
+library(microbenchmark)
 set.seed(3175775)
 p <- 10
 par <- rnorm(p, sd = 0.1)
@@ -46,13 +56,17 @@ result <- microbenchmark(
 ```
 
 Below are the results:
+
 ```{r message=FALSE}
 result
 autoplot(result)
 ```
 
-### Increasing the number of parameters
-This time we fix the number of observations at $n = 1000$, and evaluate empirical likelihood at zero vectors of different sizes.
+
+## Increasing the number of parameters\
+
+This time we fix the number of observations at \(n = 1000\), and evaluate 
+empirical likelihood at zero vectors of different sizes.
 ```{r}
 n <- 1000
 result2 <- microbenchmark(
@@ -79,5 +93,7 @@ result2 <- microbenchmark(
 result2
 autoplot(result2)
 ```
-On average, evaluating empirical likelihood with a 100000×10 or 1000×400 matrix at a parameter value satisfying the convex hull constraint takes less than a second.
 
+On average, evaluating empirical likelihood with a 100000×10 or 1000×400 matrix 
+at a parameter value satisfying the convex hull constraint takes less than a 
+second.

vignettes/references.bib

@@ -0,0 +1,31 @@
+@article{kim2024melt,
+  title   = {{melt}: Multiple Empirical Likelihood Tests in {R}},
+  author  = {Eunseop Kim and Steven N. MacEachern and Mario Peruggia},
+  journal = {Journal of Statistical Software},
+  year    = {2024},
+  volume  = {108},
+  number  = {5},
+  pages   = {1--33},
+  doi     = {10.18637/jss.v108.i05}
+}
+
+@article{kim2023empirical,
+  title     = {Empirical Likelihood for the Analysis of Experimental Designs},
+  author    = {Eunseop Kim and Steven N. MacEachern and Mario Peruggia},
+  journal   = {Journal of Nonparametric Statistics},
+  volume    = {35},
+  number    = {4},
+  pages     = {709--732},
+  year      = {2023},
+  publisher = {Taylor & Francis},
+  doi       = {10.1080/10485252.2023.2206919}
+}
+
+@manual{melt,
+  title  = {\pkg{melt}: Multiple Empirical Likelihood Tests},
+  author = {Eunseop Kim},
+  year   = {2023},
+  note   = {\proglang{R} package version 1.10.0},
+  url    = {https://CRAN.R-project.org/package=melt}
+}
+