From 1b93917f01e4d1ba499e0108a96042331ba9fcbe Mon Sep 17 00:00:00 2001
From: Jun Yan <1536656+jun-yan@users.noreply.github.com>
Date: Sat, 9 Nov 2024 10:43:58 -0500
Subject: [PATCH 01/17] initial version with one function demo_clt
---
DESCRIPTION | 24 ++++++
LICENSE | 155 +++++++++++++++++++++------------------
Makefile | 74 +++++++++++++++++++
NAMESPACE | 11 +++
R/demo_clt.R | 118 +++++++++++++++++++++++++++++
inst/bib/refs.bib | 17 +++++
man/demo_clt.Rd | 48 ++++++++++++
misc/copyright.R | 17 +++++
misc/deploy_docs.sh | 29 ++++++++
misc/revdep_check.R | 5 ++
misc/update_timestamp.sh | 67 +++++++++++++++++
vignettes/clt.Rmd | 81 ++++++++++++++++++++
12 files changed, 575 insertions(+), 71 deletions(-)
create mode 100644 DESCRIPTION
create mode 100644 Makefile
create mode 100644 NAMESPACE
create mode 100644 R/demo_clt.R
create mode 100644 inst/bib/refs.bib
create mode 100644 man/demo_clt.Rd
create mode 100644 misc/copyright.R
create mode 100755 misc/deploy_docs.sh
create mode 100644 misc/revdep_check.R
create mode 100755 misc/update_timestamp.sh
create mode 100644 vignettes/clt.Rmd
diff --git a/DESCRIPTION b/DESCRIPTION
new file mode 100644
index 0000000..1fbf528
--- /dev/null
+++ b/DESCRIPTION
@@ -0,0 +1,24 @@
+Package: ibist
+Title: Companion to Introduction to Biostatistics with R
+Version: 0.0-1
+Authors@R: c(
+ person(given = "Elizabeth", family = "Schifano",
+ role = c("aut"),
+ comment = c(ORCID = "0000-0002-9793-332X")),
+ person(given = "Jun", family = "Yan",
+ email = "jun.yan@uconn.edu",
+ role = c("aut", "cre"),
+ comment = c(ORCID = "0000-0003-4401-7296"))
+ )
+Description: Datasets and functions in the book
+ "Introduction to Biostatistics with R" by
+ Schifano and Yan (2025), Taylor Francis.
+Depends:
+ R (>= 4.4.1)
+VignetteBuilder: knitr
+Suggests: knitr, rmarkdown
+License: GPL (>= 3)
+URL: https://github.com/statds/ibist-R
+BugReports: https://github.com/statds/ibist-R/issues
+Imports: ggplot2
+RoxygenNote: 7.3.2
diff --git a/LICENSE b/LICENSE
index 0ad25db..94a9ed0 100644
--- a/LICENSE
+++ b/LICENSE
@@ -1,21 +1,23 @@
- GNU AFFERO GENERAL PUBLIC LICENSE
- Version 3, 19 November 2007
+ GNU GENERAL PUBLIC LICENSE
+ Version 3, 29 June 2007
- Copyright (C) 2007 Free Software Foundation, Inc.
+ Copyright (C) 2007 Free Software Foundation, Inc.
Everyone is permitted to copy and distribute verbatim copies
of this license document, but changing it is not allowed.
Preamble
- The GNU Affero General Public License is a free, copyleft license for
-software and other kinds of works, specifically designed to ensure
-cooperation with the community in the case of network server software.
+ The GNU General Public License is a free, copyleft license for
+software and other kinds of works.
The licenses for most software and other practical works are designed
to take away your freedom to share and change the works. By contrast,
-our General Public Licenses are intended to guarantee your freedom to
+the GNU General Public License is intended to guarantee your freedom to
share and change all versions of a program--to make sure it remains free
-software for all its users.
+software for all its users. We, the Free Software Foundation, use the
+GNU General Public License for most of our software; it applies also to
+any other work released this way by its authors. You can apply it to
+your programs, too.
When we speak of free software, we are referring to freedom, not
price. Our General Public Licenses are designed to make sure that you
@@ -24,34 +26,44 @@ them if you wish), that you receive source code or can get it if you
want it, that you can change the software or use pieces of it in new
free programs, and that you know you can do these things.
- Developers that use our General Public Licenses protect your rights
-with two steps: (1) assert copyright on the software, and (2) offer
-you this License which gives you legal permission to copy, distribute
-and/or modify the software.
-
- A secondary benefit of defending all users' freedom is that
-improvements made in alternate versions of the program, if they
-receive widespread use, become available for other developers to
-incorporate. Many developers of free software are heartened and
-encouraged by the resulting cooperation. However, in the case of
-software used on network servers, this result may fail to come about.
-The GNU General Public License permits making a modified version and
-letting the public access it on a server without ever releasing its
-source code to the public.
-
- The GNU Affero General Public License is designed specifically to
-ensure that, in such cases, the modified source code becomes available
-to the community. It requires the operator of a network server to
-provide the source code of the modified version running there to the
-users of that server. Therefore, public use of a modified version, on
-a publicly accessible server, gives the public access to the source
-code of the modified version.
-
- An older license, called the Affero General Public License and
-published by Affero, was designed to accomplish similar goals. This is
-a different license, not a version of the Affero GPL, but Affero has
-released a new version of the Affero GPL which permits relicensing under
-this license.
+ To protect your rights, we need to prevent others from denying you
+these rights or asking you to surrender the rights. Therefore, you have
+certain responsibilities if you distribute copies of the software, or if
+you modify it: responsibilities to respect the freedom of others.
+
+ For example, if you distribute copies of such a program, whether
+gratis or for a fee, you must pass on to the recipients the same
+freedoms that you received. You must make sure that they, too, receive
+or can get the source code. And you must show them these terms so they
+know their rights.
+
+ Developers that use the GNU GPL protect your rights with two steps:
+(1) assert copyright on the software, and (2) offer you this License
+giving you legal permission to copy, distribute and/or modify it.
+
+ For the developers' and authors' protection, the GPL clearly explains
+that there is no warranty for this free software. For both users' and
+authors' sake, the GPL requires that modified versions be marked as
+changed, so that their problems will not be attributed erroneously to
+authors of previous versions.
+
+ Some devices are designed to deny users access to install or run
+modified versions of the software inside them, although the manufacturer
+can do so. This is fundamentally incompatible with the aim of
+protecting users' freedom to change the software. The systematic
+pattern of such abuse occurs in the area of products for individuals to
+use, which is precisely where it is most unacceptable. Therefore, we
+have designed this version of the GPL to prohibit the practice for those
+products. If such problems arise substantially in other domains, we
+stand ready to extend this provision to those domains in future versions
+of the GPL, as needed to protect the freedom of users.
+
+ Finally, every program is threatened constantly by software patents.
+States should not allow patents to restrict development and use of
+software on general-purpose computers, but in those that do, we wish to
+avoid the special danger that patents applied to a free program could
+make it effectively proprietary. To prevent this, the GPL assures that
+patents cannot be used to render the program non-free.
The precise terms and conditions for copying, distribution and
modification follow.
@@ -60,7 +72,7 @@ modification follow.
0. Definitions.
- "This License" refers to version 3 of the GNU Affero General Public License.
+ "This License" refers to version 3 of the GNU General Public License.
"Copyright" also means copyright-like laws that apply to other kinds of
works, such as semiconductor masks.
@@ -537,45 +549,35 @@ to collect a royalty for further conveying from those to whom you convey
the Program, the only way you could satisfy both those terms and this
License would be to refrain entirely from conveying the Program.
- 13. Remote Network Interaction; Use with the GNU General Public License.
-
- Notwithstanding any other provision of this License, if you modify the
-Program, your modified version must prominently offer all users
-interacting with it remotely through a computer network (if your version
-supports such interaction) an opportunity to receive the Corresponding
-Source of your version by providing access to the Corresponding Source
-from a network server at no charge, through some standard or customary
-means of facilitating copying of software. This Corresponding Source
-shall include the Corresponding Source for any work covered by version 3
-of the GNU General Public License that is incorporated pursuant to the
-following paragraph.
+ 13. Use with the GNU Affero General Public License.
Notwithstanding any other provision of this License, you have
permission to link or combine any covered work with a work licensed
-under version 3 of the GNU General Public License into a single
+under version 3 of the GNU Affero General Public License into a single
combined work, and to convey the resulting work. The terms of this
License will continue to apply to the part which is the covered work,
-but the work with which it is combined will remain governed by version
-3 of the GNU General Public License.
+but the special requirements of the GNU Affero General Public License,
+section 13, concerning interaction through a network will apply to the
+combination as such.
14. Revised Versions of this License.
The Free Software Foundation may publish revised and/or new versions of
-the GNU Affero General Public License from time to time. Such new versions
-will be similar in spirit to the present version, but may differ in detail to
+the GNU General Public License from time to time. Such new versions will
+be similar in spirit to the present version, but may differ in detail to
address new problems or concerns.
Each version is given a distinguishing version number. If the
-Program specifies that a certain numbered version of the GNU Affero General
+Program specifies that a certain numbered version of the GNU General
Public License "or any later version" applies to it, you have the
option of following the terms and conditions either of that numbered
version or of any later version published by the Free Software
Foundation. If the Program does not specify a version number of the
-GNU Affero General Public License, you may choose any version ever published
+GNU General Public License, you may choose any version ever published
by the Free Software Foundation.
If the Program specifies that a proxy can decide which future
-versions of the GNU Affero General Public License can be used, that proxy's
+versions of the GNU General Public License can be used, that proxy's
public statement of acceptance of a version permanently authorizes you
to choose that version for the Program.
@@ -633,29 +635,40 @@ the "copyright" line and a pointer to where the full notice is found.
Copyright (C)
This program is free software: you can redistribute it and/or modify
- it under the terms of the GNU Affero General Public License as published
- by the Free Software Foundation, either version 3 of the License, or
+ it under the terms of the GNU General Public License as published by
+ the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
- GNU Affero General Public License for more details.
+ GNU General Public License for more details.
- You should have received a copy of the GNU Affero General Public License
- along with this program. If not, see .
+ You should have received a copy of the GNU General Public License
+ along with this program. If not, see .
Also add information on how to contact you by electronic and paper mail.
- If your software can interact with users remotely through a computer
-network, you should also make sure that it provides a way for users to
-get its source. For example, if your program is a web application, its
-interface could display a "Source" link that leads users to an archive
-of the code. There are many ways you could offer source, and different
-solutions will be better for different programs; see section 13 for the
-specific requirements.
+ If the program does terminal interaction, make it output a short
+notice like this when it starts in an interactive mode:
+
+ Copyright (C)
+ This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
+ This is free software, and you are welcome to redistribute it
+ under certain conditions; type `show c' for details.
+
+The hypothetical commands `show w' and `show c' should show the appropriate
+parts of the General Public License. Of course, your program's commands
+might be different; for a GUI interface, you would use an "about box".
You should also get your employer (if you work as a programmer) or school,
if any, to sign a "copyright disclaimer" for the program, if necessary.
-For more information on this, and how to apply and follow the GNU AGPL, see
-.
+For more information on this, and how to apply and follow the GNU GPL, see
+.
+
+ The GNU General Public License does not permit incorporating your program
+into proprietary programs. If your program is a subroutine library, you
+may consider it more useful to permit linking proprietary applications with
+the library. If this is what you want to do, use the GNU Lesser General
+Public License instead of this License. But first, please read
+.
diff --git a/Makefile b/Makefile
new file mode 100644
index 0000000..7ed9975
--- /dev/null
+++ b/Makefile
@@ -0,0 +1,74 @@
+objects := $(wildcard R/*.R) DESCRIPTION
+version := $(shell grep -E "^Version:" DESCRIPTION | awk '{print $$NF}')
+pkg := $(shell grep -E "^Package:" DESCRIPTION | awk '{print $$NF}')
+tar := $(pkg)_$(version).tar.gz
+tinytest := $(wildcard inst/tinytest/*.R)
+checkLog := $(pkg).Rcheck/00check.log
+rmd := $(wildcard vignettes/*.Rmd)
+vignettes := $(patsubst %.Rmd,%.html,$(rmd))
+
+
+.PHONY: check
+check: $(checkLog)
+
+.PHONY: build
+build: $(tar)
+
+.PHONY: install
+install:
+ R CMD build .
+ R CMD INSTALL $(tar)
+
+.PHONY: preview
+preview: $(vignettes)
+
+.PHONY: pkgdown
+pkgdown:
+ Rscript -e "library(methods); pkgdown::build_site();"
+
+.PHONY: deploy-pkgdown
+deploy-pkgdown:
+ @bash misc/deploy_docs.sh
+
+.PHONY: check-rcpp
+check-rcpp: $(tar)
+ R CMD INSTALL $(tar)
+ Rscript inst/run_rcpp_test.R > check-rcpp.Rout &
+
+.PHONY: check-revdep
+check-revdep: $(tar)
+ @mkdir -p revdep
+ @rm -rf revdep/{*.Rcheck,*.tar.gz}
+ @cp $(tar) revdep
+ nohup R CMD BATCH --no-save --no-restore misc/revdep_check.R &
+
+$(tar): $(objects)
+ @Rscript -e "library(methods);" \
+ -e "devtools::document();";
+ @$(MAKE) update-timestamp
+ R CMD build .
+
+$(checkLog): $(tar) $(tinytest)
+ R CMD check --as-cran $(tar)
+
+vignettes/%.html: vignettes/%.Rmd
+ Rscript -e "library(methods); rmarkdown::render('$?')"
+
+.PHONY: readme
+readme: README.md
+README.md: README.Rmd
+ @Rscript -e "rmarkdown::render('$<')"
+
+## update copyright year
+.PHONY: update-timestamp
+update-timestamp:
+ @bash misc/update_timestamp.sh
+
+.PHONY: tags
+tags:
+ Rscript -e "utils::rtags(path = 'R', ofile = 'TAGS')"
+
+.PHONY: clean
+clean:
+ @$(RM) -r *~ */*~ *.Rhistroy *.tar.gz src/*.so src/*.o \
+ *.Rcheck/ *.Rout .\#* *_cache
diff --git a/NAMESPACE b/NAMESPACE
new file mode 100644
index 0000000..e0edefd
--- /dev/null
+++ b/NAMESPACE
@@ -0,0 +1,11 @@
+# Generated by roxygen2: do not edit by hand
+
+export(demo_clt)
+importFrom(ggplot2,aes)
+importFrom(ggplot2,after_stat)
+importFrom(ggplot2,facet_wrap)
+importFrom(ggplot2,geom_histogram)
+importFrom(ggplot2,geom_line)
+importFrom(ggplot2,ggplot)
+importFrom(ggplot2,labs)
+importFrom(ggplot2,theme_minimal)
diff --git a/R/demo_clt.R b/R/demo_clt.R
new file mode 100644
index 0000000..7dfbf15
--- /dev/null
+++ b/R/demo_clt.R
@@ -0,0 +1,118 @@
+
+##' Demonstrate the Central Limit Theorem
+##'
+##' The `demo_clt()` function generates plots to illustrate the
+##' Central Limit Theorem (CLT) using a specified random number
+##' generator. The function displays the sampling distributions
+##' for different sample sizes, standardized and compared against
+##' the standard normal distribution.
+##'
+##' @param rng A random number generator function (e.g., `runif`,
+##' `rnorm`, `rgamma`).
+##' @param n A numeric vector of sample sizes (e.g., `c(5, 10,
+##' 20, 40)`).
+##' @param nrep The number of repetitions for generating sample
+##' means (default is 10000).
+##' @param ... Additional arguments passed to the random number
+##' generator (e.g., `shape` and `rate` for `rgamma`).
+##' @param mean The theoretical mean of the distribution. If not
+##' provided, it is estimated from a large sample.
+##' @param sd The theoretical standard deviation of the
+##' distribution. If not provided, it is estimated from a large
+##' sample.
+##'
+##' @return A `ggplot2` plot showing the standardized sampling
+##' distributions for different sample sizes, compared against
+##' the standard normal curve.
+##' @examples
+##' set.seed(123)
+##' demo_clt(runif, n = c(5, 10, 20, 40), nrep = 10000, min = 0,
+##' max = 1)
+##' demo_clt(rgamma, n = c(5, 10, 20, 40), nrep = 10000, shape = 2,
+##' rate = 1, mean = 2, sd = sqrt(2))
+##'
+##' @importFrom ggplot2 ggplot geom_histogram geom_line aes
+##' @importFrom ggplot2 facet_wrap labs theme_minimal after_stat
+##' @export
+demo_clt <- function(rng, n, nrep = 10000, ..., mean = NULL,
+ sd = NULL) {
+ ## Validate that 'rng' is a function
+ if (!is.function(rng)) {
+ stop("The argument 'rng' must be a function.")
+ }
+
+ ## Validate that 'n' is a numeric vector of positive values
+ if (!is.numeric(n) || any(n <= 0)) {
+ stop("The argument 'n' must be a numeric vector of positive values.")
+ }
+
+ ## Validate that 'nrep' is a positive integer
+ if (!is.numeric(nrep) || nrep <= 0) {
+ stop("The argument 'nrep' must be a positive integer.")
+ }
+
+ ## Estimate the theoretical mean and standard deviation if not provided
+ if (is.null(mean) || is.null(sd)) {
+ sample_data <- rng(100000, ...)
+ mean <- mean(sample_data)
+ sd <- sd(sample_data)
+ }
+
+ ## Helper function to generate random samples
+ generate_sample <- function(size) {
+ rng(size, ...)
+ }
+
+ ## Initialize an empty list to store standardized sample means
+ results <- list()
+
+ ## Loop through each sample size and generate standardized sample means
+ for (size in n) {
+ ## Generate 'nrep' sample means for the current sample size
+ sample_means <- replicate(nrep, mean(generate_sample(size)))
+
+ ## Standardize the sample means using the theoretical mean and standard deviation
+ standardized_means <- (sample_means - mean) / (sd / sqrt(size))
+
+ ## Store the standardized means in a data frame
+ results[[as.character(size)]] <- data.frame(
+ StandardizedMean = standardized_means,
+ SampleSize = factor(size)
+ )
+ }
+
+ ## Combine all results into a single data frame
+ data <- do.call(rbind, results)
+
+ ## Create data for the standard normal curve overlay
+ x_vals <- seq(-4, 4, length.out = 100)
+ normal_data <- data.frame(
+ x = x_vals,
+ y = dnorm(x_vals)
+ )
+
+ ## Create the ggplot object
+ plot <- ggplot2::ggplot(data, aes(x = StandardizedMean)) +
+ ## Plot the histogram in density mode
+ ggplot2::geom_histogram(aes(y = after_stat(density)),
+ bins = 30, color = "black",
+ fill = "skyblue") +
+ ## Overlay the standard normal density curve
+ ggplot2::geom_line(data = normal_data,
+ aes(x = x, y = y),
+ color = "red",
+ linetype = "dashed",
+ linewidth = 0.8) +
+ ## Facet the plot by sample size
+ ggplot2::facet_wrap(~ SampleSize) +
+ ## Add labels and theme
+ ggplot2::labs(
+ title = "Demonstrating the Central Limit Theorem",
+ x = "Standardized Sample Mean",
+ y = "Density"
+ ) +
+ ggplot2::theme_minimal()
+
+ ## Return the ggplot object
+ return(plot)
+}
diff --git a/inst/bib/refs.bib b/inst/bib/refs.bib
new file mode 100644
index 0000000..5b7901a
--- /dev/null
+++ b/inst/bib/refs.bib
@@ -0,0 +1,17 @@
+
+
+@article{wang2021shape,
+ author = {Wang, Wenjie and Yan, Jun},
+ title = {Shape-Restricted Regression Splines with {R} Package
+ {splines2}},
+ journal = {Journal of Data Science},
+ volume = 19,
+ number = 3,
+ year = 2021,
+ pages = {498--517},
+ doi = {10.6339/21-JDS1020},
+ issn = {1680-743X},
+ publisher = {School of Statistics, Renmin University of China},
+}
+
+
diff --git a/man/demo_clt.Rd b/man/demo_clt.Rd
new file mode 100644
index 0000000..6a8faa8
--- /dev/null
+++ b/man/demo_clt.Rd
@@ -0,0 +1,48 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/demo_clt.R
+\name{demo_clt}
+\alias{demo_clt}
+\title{Demonstrate the Central Limit Theorem}
+\usage{
+demo_clt(rng, n, nrep = 10000, ..., mean = NULL, sd = NULL)
+}
+\arguments{
+\item{rng}{A random number generator function (e.g., `runif`,
+`rnorm`, `rgamma`).}
+
+\item{n}{A numeric vector of sample sizes (e.g., `c(5, 10,
+20, 40)`).}
+
+\item{nrep}{The number of repetitions for generating sample
+means (default is 10000).}
+
+\item{...}{Additional arguments passed to the random number
+generator (e.g., `shape` and `rate` for `rgamma`).}
+
+\item{mean}{The theoretical mean of the distribution. If not
+provided, it is estimated from a large sample.}
+
+\item{sd}{The theoretical standard deviation of the
+distribution. If not provided, it is estimated from a large
+sample.}
+}
+\value{
+A `ggplot2` plot showing the standardized sampling
+distributions for different sample sizes, compared against
+the standard normal curve.
+}
+\description{
+The `demo_clt()` function generates plots to illustrate the
+Central Limit Theorem (CLT) using a specified random number
+generator. The function displays the sampling distributions
+for different sample sizes, standardized and compared against
+the standard normal distribution.
+}
+\examples{
+set.seed(123)
+demo_clt(runif, n = c(5, 10, 20, 40), nrep = 10000, min = 0,
+max = 1)
+demo_clt(rgamma, n = c(5, 10, 20, 40), nrep = 10000, shape = 2,
+rate = 1, mean = 2, sd = sqrt(2))
+
+}
diff --git a/misc/copyright.R b/misc/copyright.R
new file mode 100644
index 0000000..2aca1be
--- /dev/null
+++ b/misc/copyright.R
@@ -0,0 +1,17 @@
+##
+## R package ibist by Elizabeth D. Schifano and Jun Yan
+## Copyright (C) 2024-2025
+##
+## This file is part of the R package ibist.
+##
+## The R package ibist is free software: You can redistribute it and/or
+## modify it under the terms of the GNU General Public License as published by
+## the Free Software Foundation, either version 3 of the License, or any later
+## version (at your option). See the GNU General Public License at
+## for details.
+##
+## The R package ibist is distributed in the hope that it will be useful,
+## but WITHOUT ANY WARRANTY without even the implied warranty of
+## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
+##
+
diff --git a/misc/deploy_docs.sh b/misc/deploy_docs.sh
new file mode 100755
index 0000000..b5732e9
--- /dev/null
+++ b/misc/deploy_docs.sh
@@ -0,0 +1,29 @@
+#!/bin/bash
+
+set -e
+
+## run locally
+PKG=$(grep "Package" DESCRIPTION | awk '{print $NF}')
+BUILD_DIR=$(pwd)
+DOCS_REPO=$HOME/git/wwenjie.org
+TARGET_DIR=$DOCS_REPO/static/$PKG
+GIT_COMMIT=$(git rev-parse --short HEAD)
+
+## update docs by pkgdown
+make pkgdown
+
+# go to the repository for wwenjie.org
+cd $DOCS_REPO
+git checkout -f
+git checkout main
+git pull gitlab main
+mkdir -p $TARGET_DIR
+cp -r $BUILD_DIR/docs/* $TARGET_DIR
+git add static/$PKG/
+if [ -n "$(git diff --cached --exit-code)" ]
+then
+ git commit -m "deploy pkgdown for $PKG $GIT_COMMIT"
+ git push gitlab main
+else
+ printf "The docs was not updated.\n"
+fi
diff --git a/misc/revdep_check.R b/misc/revdep_check.R
new file mode 100644
index 0000000..6495008
--- /dev/null
+++ b/misc/revdep_check.R
@@ -0,0 +1,5 @@
+revdep_dir <- "revdep"
+res <- tools::check_packages_in_dir(revdep_dir,
+ clean = FALSE,
+ reverse = list(recursively = FALSE))
+summary(res)
diff --git a/misc/update_timestamp.sh b/misc/update_timestamp.sh
new file mode 100755
index 0000000..3003f1e
--- /dev/null
+++ b/misc/update_timestamp.sh
@@ -0,0 +1,67 @@
+#!/bin/bash
+
+# Note: this script is should be sourced from the project root directory
+
+set -e
+
+if [ "$(uname)" != "Linux" ]; then
+ printf "Remeber to update date and version number.\n"
+else
+ printf "Updating date, version, and copyright year.\n"
+
+ # define some variables
+ yr=$(date +%Y)
+ dt=$(date +%Y-%m-%d)
+ cprt_R=misc/copyright.R
+ cprt_cpp=misc/copyright.cpp
+ citation=inst/CITATION
+ version=$(grep "Version" DESCRIPTION | awk '{print $NF}')
+
+ # update copyright year in the template headers
+ regexp1="s/Copyright \(C\) 2016-[0-9]+/Copyright \(C\) 2016-$yr/"
+ sed -i -E "$regexp1" $cprt_R
+ sed "s_#_/_g" $cprt_R > $cprt_cpp
+
+ # update copyright year in all R scripts
+ for Rfile in R/*.R
+ do
+ if ! grep -q 'Copyright (C)' $Rfile;
+ then
+ if [ $Rfile != "R/RcppExports.R" ];
+ then
+ cat $cprt_R $Rfile > .tmp
+ mv .tmp $Rfile
+ fi
+ fi
+ sed -i -E "$regexp1" $Rfile
+ done
+
+ # update copyright year in all C++ scripts
+ for cppfile in src/*.cpp inst/include/*.h inst/include/*/*.h
+ do
+ if ! grep -q 'Copyright (C)' $cppfile;
+ then
+ if [ $cppfile != "src/RcppExports.cpp" ];
+ then
+ cat $cprt_cpp $cppfile > .tmp
+ mv .tmp $cppfile
+ fi
+ fi
+ sed -i -E "$regexp1" $cppfile
+ done
+ rm $cprt_cpp
+
+ # update date in DESCRIPTION
+ # regexp2="s/Date: [0-9]{4}-[0-9]{1,2}-[0-9]{1,2}/Date: $dt/"
+ # sed -i -E "$regexp2" DESCRIPTION
+
+ # update version and year in citation
+ regexp3="s/version ([0-9]+\.*)+/version $version/"
+ sed -i -E "$regexp3" $citation
+ # restrict the search and only update the year of package
+ regexp4="/splines2-package/,/^\)$/ s/20[0-9]{2}/$yr/"
+ sed -i -E "$regexp4" $citation
+
+ # done
+ printf "All updated.\n"
+fi
diff --git a/vignettes/clt.Rmd b/vignettes/clt.Rmd
new file mode 100644
index 0000000..dfeb09a
--- /dev/null
+++ b/vignettes/clt.Rmd
@@ -0,0 +1,81 @@
+---
+title: "Demonstrating the Central Limit Theorem"
+author: "Companion Package for Biostatistical Analysis of Proportions and Rates"
+date: "`r Sys.Date()`"
+output: rmarkdown::html_vignette
+vignette: >
+ %\VignetteIndexEntry{Central Limit Theorem Demonstration}
+ %\VignetteEngine{knitr::rmarkdown}
+ %\VignetteEncoding{UTF-8}
+---
+
+# Introduction
+
+In this vignette, we demonstrate the Central Limit Theorem (CLT)
+using the `cltdemo()` function. The CLT states that, regardless
+of the original population distribution, the distribution of the
+sample mean approaches a normal distribution as the sample size
+increases.
+
+Here, we use the Gamma distribution with varying skewness, which
+is controlled by the `shape` parameter:
+
+- **Shape = 0.5**: Highly skewed distribution
+- **Shape = 1**: Exponential distribution (moderately skewed)
+- **Shape = 2**: Less skewed distribution
+
+We explore the behavior of the sample mean for different sample
+sizes (`n = 5, 10, 20, 40`) to illustrate the convergence towards
+the normal distribution.
+
+# Load Required Packages
+
+```{r setup, include = FALSE}
+set.seed(123)
+library("ibist")
+```
+
+# Gamma Distribution with Shape = 0.5
+
+The Gamma distribution with `shape = 0.5` is highly skewed. We
+expect to see the sample mean distribution becoming more normal
+as the sample size increases.
+
+```{r gamma-shape-0.5}
+demo_clt(rgamma, n = c(5, 10, 20, 40), nrep = 10000,
+ shape = 0.5, rate = 1, mean = 0.5, sd = sqrt(0.5))
+```
+
+# Gamma Distribution with Shape = 1
+
+The Gamma distribution with `shape = 1` is equivalent to the
+Exponential distribution. This example has moderate skewness,
+and we observe the effect of increasing the sample size.
+
+```{r gamma-shape-1}
+demo_clt(rgamma, n = c(5, 10, 20, 40), nrep = 10000,
+ shape = 1, rate = 1, mean = 1, sd = sqrt(1))
+```
+
+# Gamma Distribution with Shape = 2
+
+The Gamma distribution with `shape = 2` has less skewness. Here,
+the sample mean distribution converges more quickly to a normal
+distribution even for smaller sample sizes.
+
+```{r gamma-shape-2}
+demo_clt(rgamma, n = c(5, 10, 20, 40), nrep = 10000,
+ shape = 2, rate = 1, mean = 2, sd = sqrt(2))
+```
+
+# Conclusion
+
+The plots above demonstrate the Central Limit Theorem in action.
+As the sample size increases, the distribution of the sample mean
+approaches a normal distribution, even for highly skewed
+underlying distributions like the Gamma distribution with
+`shape = 0.5`.
+
+This vignette illustrates the robustness of the CLT and its
+importance in statistical analysis, especially when dealing with
+non-normal data in biostatistical contexts.
From 289e09d575c2ae92286dee9d85e6a430e83ea359 Mon Sep 17 00:00:00 2001
From: Jun Yan <1536656+jun-yan@users.noreply.github.com>
Date: Sat, 9 Nov 2024 11:23:11 -0500
Subject: [PATCH 02/17] added a mixture to clt demo
---
vignettes/clt.Rmd | 18 ++++++++++++++++++
1 file changed, 18 insertions(+)
diff --git a/vignettes/clt.Rmd b/vignettes/clt.Rmd
index dfeb09a..5660d04 100644
--- a/vignettes/clt.Rmd
+++ b/vignettes/clt.Rmd
@@ -68,6 +68,24 @@ demo_clt(rgamma, n = c(5, 10, 20, 40), nrep = 10000,
shape = 2, rate = 1, mean = 2, sd = sqrt(2))
```
+# A General Distribution Constructed from a Mixture
+
+When the population `mean` and `sd` are unspecified, the will be
+approximated by a large sample (10,000) and then used in
+standardization. For instance, consider sampling from the following
+mixture distribution.
+
+```{r mixture}
+mymix_rng <- function(n, rate = 0.5) {
+ ifelse(runif(n) < rate,
+ rgamma(n, shape = 0.5), rgamma(n, shape = 4))
+}
+
+demo_clt(mymix_rng, n = c(5, 10, 20, 40))
+
+```
+
+
# Conclusion
The plots above demonstrate the Central Limit Theorem in action.
From f5acb38ad6c3110fa9f5d44d4a5b992744f92cda Mon Sep 17 00:00:00 2001
From: Jun Yan <1536656+jun-yan@users.noreply.github.com>
Date: Sat, 9 Nov 2024 11:33:55 -0500
Subject: [PATCH 03/17] added installation notes to README
---
README.md | 27 ++++++++++++++++++++++++++-
1 file changed, 26 insertions(+), 1 deletion(-)
diff --git a/README.md b/README.md
index 26cc5b3..0c2b914 100644
--- a/README.md
+++ b/README.md
@@ -1 +1,26 @@
-# ibist
\ No newline at end of file
+# R Package **ibist**
+
+The R package **ibist** is a companion to the book
+"Introduction to Biostatistical Analysis of Proportions and Rates"
+by Elizabeth D. Schifano and Jun Yan
+
+## Features
+
+The package **splines2** provides functions, datasets, and demos
+used in the book.
+
+## Development
+
+The latest version of the package is under development at
+[GitHub](https://github.com/wenjie2wang/splines2). If it is able to pass
+the automated package checks, one may install it by
+
+``` r
+if (! require(remotes)) install.packages("remotes")
+remotes::install_github("statds/ibist-R", upgrade = "never")
+```
+
+## License
+
+[GNU General Public License](https://www.gnu.org/licenses/) (≥ 3)
+
From 5f8533a4dd1cd6bcac553cf63faea0d388da052c Mon Sep 17 00:00:00 2001
From: Jun Yan <1536656+jun-yan@users.noreply.github.com>
Date: Wed, 7 Jan 2026 20:51:15 -0500
Subject: [PATCH 04/17] added power.p1s.test()
---
R/power.p1s.test.R | 271 +++++++++++++++++++++++++++++++++++++++++++++
1 file changed, 271 insertions(+)
create mode 100644 R/power.p1s.test.R
diff --git a/R/power.p1s.test.R b/R/power.p1s.test.R
new file mode 100644
index 0000000..be86187
--- /dev/null
+++ b/R/power.p1s.test.R
@@ -0,0 +1,271 @@
+#' One-sample proportion power and sample size calculation
+#'
+#' Computes power, sample size, or detectable effect size for a
+#' one-sample binomial test. The interface mirrors
+#' \code{stats::power.prop.test()}, but for the one-sample setting.
+#'
+#' Power can be computed using a normal approximation or exact binomial
+#' methods. When \code{exact = TRUE}, the exact test is determined by
+#' \code{exact.method}.
+#'
+#' Exactly one of \code{n}, \code{p0}, \code{p1}, \code{power}, or
+#' \code{sig.level} must be \code{NULL}; the missing quantity is solved
+#' numerically.
+#'
+#' @param n Sample size for the single group.
+#' @param p0 Null hypothesis proportion.
+#' @param p1 Alternative hypothesis proportion.
+#' @param power Desired power.
+#' @param sig.level Significance level.
+#' @param alternative Character string specifying the alternative
+#' hypothesis; one of \code{"two.sided"}, \code{"less"}, or
+#' \code{"greater"}.
+#' @param cc Logical; if \code{TRUE}, apply continuity correction in the
+#' normal approximation.
+#' @param exact Logical; if \code{TRUE}, use an exact binomial method.
+#' @param exact.method Method used for exact binomial power calculation.
+#' \code{"quantile"} uses fixed binomial rejection regions for stable
+#' power and sample-size inversion; \code{"midp"} applies the mid-p
+#' adjustment; \code{"cp"} inverts Clopper--Pearson acceptance regions
+#' to guarantee size not exceeding \code{sig.level}.
+#' @param tol Numerical tolerance used in root finding.
+#' @param max_n Maximum allowable sample size when solving for \code{n}.
+#'
+#' @return An object of class \code{"power.htest"} containing the
+#' computed quantity and test specifications.
+#'
+#' @examples
+#' ## Normal approximation (default)
+#' power.p1s.test(n = 50, p0 = 0.1, p1 = 0.25)
+#'
+#' ## Exact binomial power (quantile-based, default exact method)
+#' power.p1s.test(n = 50, p0 = 0.1, p1 = 0.25, exact = TRUE)
+#'
+#' ## Exact mid-p power
+#' power.p1s.test(
+#' n = 50, p0 = 0.1, p1 = 0.25,
+#' exact = TRUE, exact.method = "midp"
+#' )
+#'
+#' ## Exact Clopper--Pearson power (guaranteed size control)
+#' power.p1s.test(
+#' n = 50, p0 = 0.1, p1 = 0.25,
+#' exact = TRUE, exact.method = "cp"
+#' )
+#'
+#' @export
+power.p1s.test <- function(
+ n = NULL,
+ p0 = NULL,
+ p1 = NULL,
+ power = NULL,
+ sig.level = 0.05,
+ alternative = c("two.sided", "less", "greater"),
+ cc = FALSE,
+ exact = FALSE,
+ exact.method = c("quantile", "midp", "cp"),
+ tol = .Machine$double.eps^0.5,
+ max_n = 1e7
+) {
+ alternative <- match.arg(alternative)
+ exact.method <- match.arg(exact.method)
+
+ ## ---- argument geometry ----
+ is_null <- c(is.null(n), is.null(p0), is.null(p1),
+ is.null(power), is.null(sig.level))
+ if (sum(is_null) != 1L)
+ stop("Exactly one of 'n', 'p0', 'p1', 'power', 'sig.level' must be NULL",
+ call. = FALSE)
+
+ ## ---- basic validation ----
+ if (!is.null(n)) {
+ if (!is.finite(n) || n <= 0)
+ stop("'n' must be positive", call. = FALSE)
+ n <- as.integer(ceiling(n))
+ }
+ if (!is.null(p0) && !(p0 > 0 && p0 < 1))
+ stop("'p0' must be in (0,1)", call. = FALSE)
+ if (!is.null(p1) && !(p1 > 0 && p1 < 1))
+ stop("'p1' must be in (0,1)", call. = FALSE)
+ if (!is.null(sig.level) && !(sig.level > 0 && sig.level < 1))
+ stop("'sig.level' must be in (0,1)", call. = FALSE)
+ if (!is.null(power) && !(power > 0 && power < 1))
+ stop("'power' must be in (0,1)", call. = FALSE)
+
+ if (!exact && exact.method != "quantile") {
+ warning(
+ "'exact.method' ignored when exact = FALSE",
+ call. = FALSE
+ )
+ }
+
+ ## ---- exact quantile rejection region cache ----
+ rr_cache <- new.env(parent = emptyenv())
+
+ get_rr <- function(n, p0, alpha) {
+ key <- paste(n, p0, alpha, alternative, sep = "|")
+ if (exists(key, envir = rr_cache, inherits = FALSE))
+ return(get(key, envir = rr_cache, inherits = FALSE))
+
+ if (alternative == "greater") {
+ rr <- list(type = "greater",
+ k = qbinom(1 - alpha, n, p0))
+ } else if (alternative == "less") {
+ rr <- list(type = "less",
+ k = qbinom(alpha, n, p0))
+ } else {
+ rr <- list(
+ type = "two.sided",
+ k_lo = qbinom(alpha / 2, n, p0),
+ k_hi = qbinom(1 - alpha / 2, n, p0)
+ )
+ }
+
+ assign(key, rr, envir = rr_cache)
+ rr
+ }
+
+ ## ---- power bodies ----
+
+ approx_power_body <- function(n, p0, p1, alpha) {
+ se0 <- sqrt(p0 * (1 - p0) / n)
+ se1 <- sqrt(p1 * (1 - p1) / n)
+ delta <- if (cc) 0.5 / n else 0
+
+ if (alternative == "greater") {
+ z <- qnorm(1 - alpha)
+ 1 - pnorm((p0 + z * se0 - delta - p1) / se1)
+ } else if (alternative == "less") {
+ z <- qnorm(1 - alpha)
+ pnorm((p0 - z * se0 + delta - p1) / se1)
+ } else {
+ z <- qnorm(1 - alpha / 2)
+ pnorm((p0 - z * se0 + delta - p1) / se1) +
+ 1 - pnorm((p0 + z * se0 - delta - p1) / se1)
+ }
+ }
+
+ exact_quantile_power_body <- function(n, p0, p1, alpha, midp) {
+ rr <- get_rr(n, p0, alpha)
+
+ if (rr$type == "greater") {
+ k <- rr$k
+ if (midp)
+ pbinom(k - 1, n, p1, lower.tail = FALSE) +
+ 0.5 * dbinom(k, n, p1)
+ else
+ pbinom(k - 1, n, p1, lower.tail = FALSE)
+ } else if (rr$type == "less") {
+ k <- rr$k
+ if (midp)
+ pbinom(k - 1, n, p1) +
+ 0.5 * dbinom(k, n, p1)
+ else
+ pbinom(k, n, p1)
+ } else {
+ k_lo <- rr$k_lo
+ k_hi <- rr$k_hi
+ if (midp) {
+ pbinom(k_lo - 1, n, p1) +
+ 0.5 * dbinom(k_lo, n, p1) +
+ pbinom(k_hi - 1, n, p1, lower.tail = FALSE) +
+ 0.5 * dbinom(k_hi, n, p1)
+ } else {
+ pbinom(k_lo, n, p1) +
+ pbinom(k_hi - 1, n, p1, lower.tail = FALSE)
+ }
+ }
+ }
+
+ cp_power_body <- function(n, p0, p1, alpha) {
+ x <- 0:n
+ reject <- if (alternative == "greater") {
+ pbinom(x - 1, n, p0, lower.tail = FALSE) <= alpha
+ } else if (alternative == "less") {
+ pbinom(x, n, p0) <= alpha
+ } else {
+ 2 * pmin(
+ pbinom(x, n, p0),
+ pbinom(x - 1, n, p0, lower.tail = FALSE)
+ ) <= alpha
+ }
+ sum(dbinom(x[reject], n, p1))
+ }
+
+ ## ---- dispatch ----
+ if (!exact) {
+ power_body <- approx_power_body
+ midp <- FALSE
+ } else if (exact.method == "cp") {
+ power_body <- cp_power_body
+ midp <- FALSE
+ } else if (exact.method == "midp") {
+ power_body <- function(n, p0, p1, alpha)
+ exact_quantile_power_body(n, p0, p1, alpha, midp = TRUE)
+ midp <- TRUE
+ } else {
+ power_body <- function(n, p0, p1, alpha)
+ exact_quantile_power_body(n, p0, p1, alpha, midp = FALSE)
+ midp <- FALSE
+ }
+
+ ## ---- solve for missing parameter ----
+ if (is.null(power)) {
+ power <- power_body(n, p0, p1, sig.level)
+ }
+
+ if (is.null(n)) {
+ f <- function(nn) power_body(nn, p0, p1, sig.level) - power
+ n_lo <- 1L
+ if (f(n_lo) >= 0) {
+ n <- n_lo
+ } else {
+ n_hi <- 2L
+ while (f(n_hi) < 0) {
+ n_hi <- n_hi * 2L
+ if (n_hi > max_n)
+ stop("Required n exceeds 'max_n'", call. = FALSE)
+ }
+ n <- as.integer(ceiling(uniroot(f, c(n_lo, n_hi), tol = tol)$root))
+ }
+ }
+
+ if (is.null(p1)) {
+ f <- function(pp) power_body(n, p0, pp, sig.level) - power
+ p1 <- uniroot(f, c(tol, 1 - tol), tol = tol)$root
+ }
+
+ if (is.null(p0)) {
+ f <- function(pp) power_body(n, pp, p1, sig.level) - power
+ p0 <- uniroot(f, c(tol, 1 - tol), tol = tol)$root
+ }
+
+ if (is.null(sig.level)) {
+ f <- function(a) power_body(n, p0, p1, a) - power
+ sig.level <- uniroot(f, c(tol, 1 - tol), tol = tol)$root
+ }
+
+ ## ---- return object ----
+ method <- if (!exact)
+ "One-sample proportion power calculation (normal approximation)"
+ else if (exact.method == "cp")
+ "One-sample proportion power calculation (exact Clopper--Pearson)"
+ else if (exact.method == "midp")
+ "One-sample proportion power calculation (exact binomial, mid-p)"
+ else
+ "One-sample proportion power calculation (exact binomial)"
+
+ structure(
+ list(
+ n = n,
+ p0 = p0,
+ p1 = p1,
+ sig.level = sig.level,
+ power = power,
+ alternative = alternative,
+ note = "n is sample size for the single group",
+ method = method
+ ),
+ class = "power.htest"
+ )
+}
From a2f30af9edc0cba9a05d8d063a6593b5ba492d02 Mon Sep 17 00:00:00 2001
From: Jun Yan <1536656+jun-yan@users.noreply.github.com>
Date: Thu, 8 Jan 2026 07:45:45 -0500
Subject: [PATCH 05/17] no need for LICENSE file
---
LICENSE | 674 --------------------------------------------------------
1 file changed, 674 deletions(-)
delete mode 100644 LICENSE
diff --git a/LICENSE b/LICENSE
deleted file mode 100644
index 94a9ed0..0000000
--- a/LICENSE
+++ /dev/null
@@ -1,674 +0,0 @@
- GNU GENERAL PUBLIC LICENSE
- Version 3, 29 June 2007
-
- Copyright (C) 2007 Free Software Foundation, Inc.
- Everyone is permitted to copy and distribute verbatim copies
- of this license document, but changing it is not allowed.
-
- Preamble
-
- The GNU General Public License is a free, copyleft license for
-software and other kinds of works.
-
- The licenses for most software and other practical works are designed
-to take away your freedom to share and change the works. By contrast,
-the GNU General Public License is intended to guarantee your freedom to
-share and change all versions of a program--to make sure it remains free
-software for all its users. We, the Free Software Foundation, use the
-GNU General Public License for most of our software; it applies also to
-any other work released this way by its authors. You can apply it to
-your programs, too.
-
- When we speak of free software, we are referring to freedom, not
-price. Our General Public Licenses are designed to make sure that you
-have the freedom to distribute copies of free software (and charge for
-them if you wish), that you receive source code or can get it if you
-want it, that you can change the software or use pieces of it in new
-free programs, and that you know you can do these things.
-
- To protect your rights, we need to prevent others from denying you
-these rights or asking you to surrender the rights. Therefore, you have
-certain responsibilities if you distribute copies of the software, or if
-you modify it: responsibilities to respect the freedom of others.
-
- For example, if you distribute copies of such a program, whether
-gratis or for a fee, you must pass on to the recipients the same
-freedoms that you received. You must make sure that they, too, receive
-or can get the source code. And you must show them these terms so they
-know their rights.
-
- Developers that use the GNU GPL protect your rights with two steps:
-(1) assert copyright on the software, and (2) offer you this License
-giving you legal permission to copy, distribute and/or modify it.
-
- For the developers' and authors' protection, the GPL clearly explains
-that there is no warranty for this free software. For both users' and
-authors' sake, the GPL requires that modified versions be marked as
-changed, so that their problems will not be attributed erroneously to
-authors of previous versions.
-
- Some devices are designed to deny users access to install or run
-modified versions of the software inside them, although the manufacturer
-can do so. This is fundamentally incompatible with the aim of
-protecting users' freedom to change the software. The systematic
-pattern of such abuse occurs in the area of products for individuals to
-use, which is precisely where it is most unacceptable. Therefore, we
-have designed this version of the GPL to prohibit the practice for those
-products. If such problems arise substantially in other domains, we
-stand ready to extend this provision to those domains in future versions
-of the GPL, as needed to protect the freedom of users.
-
- Finally, every program is threatened constantly by software patents.
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-patents cannot be used to render the program non-free.
-
- The precise terms and conditions for copying, distribution and
-modification follow.
-
- TERMS AND CONDITIONS
-
- 0. Definitions.
-
- "This License" refers to version 3 of the GNU General Public License.
-
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-"recipients" may be individuals or organizations.
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From 11ae747faf40c9d2869dde3fa0d2008a92e2e875 Mon Sep 17 00:00:00 2001
From: Jun Yan <1536656+jun-yan@users.noreply.github.com>
Date: Thu, 8 Jan 2026 07:46:37 -0500
Subject: [PATCH 06/17] fixed indentation
---
vignettes/clt.Rmd | 6 +++---
1 file changed, 3 insertions(+), 3 deletions(-)
diff --git a/vignettes/clt.Rmd b/vignettes/clt.Rmd
index 5660d04..805aad5 100644
--- a/vignettes/clt.Rmd
+++ b/vignettes/clt.Rmd
@@ -43,7 +43,7 @@ as the sample size increases.
```{r gamma-shape-0.5}
demo_clt(rgamma, n = c(5, 10, 20, 40), nrep = 10000,
- shape = 0.5, rate = 1, mean = 0.5, sd = sqrt(0.5))
+ shape = 0.5, rate = 1, mean = 0.5, sd = sqrt(0.5))
```
# Gamma Distribution with Shape = 1
@@ -54,7 +54,7 @@ and we observe the effect of increasing the sample size.
```{r gamma-shape-1}
demo_clt(rgamma, n = c(5, 10, 20, 40), nrep = 10000,
- shape = 1, rate = 1, mean = 1, sd = sqrt(1))
+ shape = 1, rate = 1, mean = 1, sd = sqrt(1))
```
# Gamma Distribution with Shape = 2
@@ -65,7 +65,7 @@ distribution even for smaller sample sizes.
```{r gamma-shape-2}
demo_clt(rgamma, n = c(5, 10, 20, 40), nrep = 10000,
- shape = 2, rate = 1, mean = 2, sd = sqrt(2))
+ shape = 2, rate = 1, mean = 2, sd = sqrt(2))
```
# A General Distribution Constructed from a Mixture
From e7182df5c9b1544d9003ae8ad6bb1a97d02b6dd6 Mon Sep 17 00:00:00 2001
From: Jun Yan <1536656+jun-yan@users.noreply.github.com>
Date: Thu, 8 Jan 2026 07:47:49 -0500
Subject: [PATCH 07/17] fixed NSE from ggplot for cran check
---
R/demo_clt.R | 188 +++++++++++++++++++++++++-----------------------
man/demo_clt.Rd | 53 +++++++-------
2 files changed, 127 insertions(+), 114 deletions(-)
diff --git a/R/demo_clt.R b/R/demo_clt.R
index 7dfbf15..9332983 100644
--- a/R/demo_clt.R
+++ b/R/demo_clt.R
@@ -1,118 +1,128 @@
-
-##' Demonstrate the Central Limit Theorem
-##'
-##' The `demo_clt()` function generates plots to illustrate the
-##' Central Limit Theorem (CLT) using a specified random number
-##' generator. The function displays the sampling distributions
-##' for different sample sizes, standardized and compared against
-##' the standard normal distribution.
-##'
-##' @param rng A random number generator function (e.g., `runif`,
-##' `rnorm`, `rgamma`).
-##' @param n A numeric vector of sample sizes (e.g., `c(5, 10,
-##' 20, 40)`).
-##' @param nrep The number of repetitions for generating sample
-##' means (default is 10000).
-##' @param ... Additional arguments passed to the random number
-##' generator (e.g., `shape` and `rate` for `rgamma`).
-##' @param mean The theoretical mean of the distribution. If not
-##' provided, it is estimated from a large sample.
-##' @param sd The theoretical standard deviation of the
-##' distribution. If not provided, it is estimated from a large
-##' sample.
-##'
-##' @return A `ggplot2` plot showing the standardized sampling
-##' distributions for different sample sizes, compared against
-##' the standard normal curve.
-##' @examples
-##' set.seed(123)
-##' demo_clt(runif, n = c(5, 10, 20, 40), nrep = 10000, min = 0,
-##' max = 1)
-##' demo_clt(rgamma, n = c(5, 10, 20, 40), nrep = 10000, shape = 2,
-##' rate = 1, mean = 2, sd = sqrt(2))
-##'
-##' @importFrom ggplot2 ggplot geom_histogram geom_line aes
-##' @importFrom ggplot2 facet_wrap labs theme_minimal after_stat
-##' @export
-demo_clt <- function(rng, n, nrep = 10000, ..., mean = NULL,
- sd = NULL) {
- ## Validate that 'rng' is a function
+#' Demonstrate the Central Limit Theorem
+#'
+#' The \code{demo_clt()} function generates plots to illustrate the
+#' Central Limit Theorem (CLT) using a specified random number generator.
+#' The function displays standardized sampling distributions for
+#' different sample sizes and overlays the standard normal density.
+#'
+#' @param rng A random number generator function taking the sample size
+#' as its first argument (e.g., \code{runif}, \code{rnorm},
+#' \code{rgamma}).
+#' @param n A numeric vector of sample sizes (e.g., \code{c(5, 10, 20,
+#' 40)}).
+#' @param nrep The number of repetitions for generating sample means
+#' (default is 10000).
+#' @param ... Additional arguments passed to the random number generator
+#' (e.g., \code{shape} and \code{rate} for \code{rgamma}).
+#' @param mean The theoretical mean of the distribution. If \code{NULL},
+#' it is estimated from a large Monte Carlo sample.
+#' @param sd The theoretical standard deviation of the distribution.
+#' If \code{NULL}, it is estimated from a large Monte Carlo sample.
+#'
+#' @return A \code{ggplot2} object showing the standardized sampling
+#' distributions for different sample sizes, compared against the
+#' standard normal curve.
+#'
+#' @examples
+#' set.seed(123)
+#' demo_clt(runif, n = c(5, 10, 20, 40), min = 0, max = 1)
+#'
+#' demo_clt(
+#' rgamma,
+#' n = c(5, 10, 20, 40),
+#' shape = 2, rate = 1,
+#' mean = 2, sd = sqrt(2)
+#' )
+#'
+#' @importFrom rlang .data
+#' @importFrom ggplot2 ggplot geom_histogram geom_line aes
+#' @importFrom ggplot2 facet_wrap labs theme_minimal after_stat
+#' @export
+demo_clt <- function(
+ rng,
+ n,
+ nrep = 10000,
+ ...,
+ mean = NULL,
+ sd = NULL
+) {
+ ## ---- basic validation ----
if (!is.function(rng)) {
- stop("The argument 'rng' must be a function.")
+ stop("The argument 'rng' must be a function.", call. = FALSE)
}
- ## Validate that 'n' is a numeric vector of positive values
if (!is.numeric(n) || any(n <= 0)) {
- stop("The argument 'n' must be a numeric vector of positive values.")
+ stop(
+ "The argument 'n' must be a numeric vector of positive values.",
+ call. = FALSE
+ )
}
- ## Validate that 'nrep' is a positive integer
- if (!is.numeric(nrep) || nrep <= 0) {
- stop("The argument 'nrep' must be a positive integer.")
+ if (!is.numeric(nrep) || length(nrep) != 1L || nrep <= 0) {
+ stop(
+ "The argument 'nrep' must be a positive integer.",
+ call. = FALSE
+ )
}
- ## Estimate the theoretical mean and standard deviation if not provided
+ ## ---- estimate mean and sd if needed ----
if (is.null(mean) || is.null(sd)) {
sample_data <- rng(100000, ...)
- mean <- mean(sample_data)
- sd <- sd(sample_data)
+ mean <- base::mean(sample_data)
+ sd <- stats::sd(sample_data)
}
- ## Helper function to generate random samples
- generate_sample <- function(size) {
- rng(size, ...)
- }
+ ## ---- generate standardized sample means ----
+ results <- vector("list", length(n))
+ names(results) <- as.character(n)
- ## Initialize an empty list to store standardized sample means
- results <- list()
-
- ## Loop through each sample size and generate standardized sample means
for (size in n) {
- ## Generate 'nrep' sample means for the current sample size
- sample_means <- replicate(nrep, mean(generate_sample(size)))
+ sample_means <- replicate(
+ nrep,
+ base::mean(rng(size, ...))
+ )
- ## Standardize the sample means using the theoretical mean and standard deviation
- standardized_means <- (sample_means - mean) / (sd / sqrt(size))
+ standardized_means <-
+ (sample_means - mean) / (sd / sqrt(size))
- ## Store the standardized means in a data frame
results[[as.character(size)]] <- data.frame(
StandardizedMean = standardized_means,
SampleSize = factor(size)
)
}
- ## Combine all results into a single data frame
data <- do.call(rbind, results)
- ## Create data for the standard normal curve overlay
- x_vals <- seq(-4, 4, length.out = 100)
+ ## ---- standard normal reference ----
+ x_vals <- seq(-4, 4, length.out = 200)
normal_data <- data.frame(
x = x_vals,
- y = dnorm(x_vals)
+ y = stats::dnorm(x_vals)
)
- ## Create the ggplot object
- plot <- ggplot2::ggplot(data, aes(x = StandardizedMean)) +
- ## Plot the histogram in density mode
- ggplot2::geom_histogram(aes(y = after_stat(density)),
- bins = 30, color = "black",
- fill = "skyblue") +
- ## Overlay the standard normal density curve
- ggplot2::geom_line(data = normal_data,
- aes(x = x, y = y),
- color = "red",
- linetype = "dashed",
- linewidth = 0.8) +
- ## Facet the plot by sample size
- ggplot2::facet_wrap(~ SampleSize) +
- ## Add labels and theme
- ggplot2::labs(
- title = "Demonstrating the Central Limit Theorem",
- x = "Standardized Sample Mean",
- y = "Density"
- ) +
- ggplot2::theme_minimal()
-
- ## Return the ggplot object
- return(plot)
+ ## ---- plot ----
+ ggplot2::ggplot(
+ data,
+ ggplot2::aes(x = .data$StandardizedMean)
+ ) +
+ ggplot2::geom_histogram(
+ ggplot2::aes(y = ggplot2::after_stat(density)),
+ bins = 30,
+ color = "black",
+ fill = "skyblue"
+ ) +
+ ggplot2::geom_line(
+ data = normal_data,
+ ggplot2::aes(x = .data$x, y = .data$y),
+ color = "red",
+ linetype = "dashed",
+ linewidth = 0.8
+ ) +
+ ggplot2::facet_wrap(~ SampleSize) +
+ ggplot2::labs(
+ title = "Demonstrating the Central Limit Theorem",
+ x = "Standardized Sample Mean",
+ y = "Density"
+ ) +
+ ggplot2::theme_minimal()
}
diff --git a/man/demo_clt.Rd b/man/demo_clt.Rd
index 6a8faa8..7780b55 100644
--- a/man/demo_clt.Rd
+++ b/man/demo_clt.Rd
@@ -7,42 +7,45 @@
demo_clt(rng, n, nrep = 10000, ..., mean = NULL, sd = NULL)
}
\arguments{
-\item{rng}{A random number generator function (e.g., `runif`,
-`rnorm`, `rgamma`).}
+\item{rng}{A random number generator function taking the sample size
+as its first argument (e.g., \code{runif}, \code{rnorm},
+\code{rgamma}).}
-\item{n}{A numeric vector of sample sizes (e.g., `c(5, 10,
-20, 40)`).}
+\item{n}{A numeric vector of sample sizes (e.g., \code{c(5, 10, 20,
+40)}).}
-\item{nrep}{The number of repetitions for generating sample
-means (default is 10000).}
+\item{nrep}{The number of repetitions for generating sample means
+(default is 10000).}
-\item{...}{Additional arguments passed to the random number
-generator (e.g., `shape` and `rate` for `rgamma`).}
+\item{...}{Additional arguments passed to the random number generator
+(e.g., \code{shape} and \code{rate} for \code{rgamma}).}
-\item{mean}{The theoretical mean of the distribution. If not
-provided, it is estimated from a large sample.}
+\item{mean}{The theoretical mean of the distribution. If \code{NULL},
+it is estimated from a large Monte Carlo sample.}
-\item{sd}{The theoretical standard deviation of the
-distribution. If not provided, it is estimated from a large
-sample.}
+\item{sd}{The theoretical standard deviation of the distribution.
+If \code{NULL}, it is estimated from a large Monte Carlo sample.}
}
\value{
-A `ggplot2` plot showing the standardized sampling
-distributions for different sample sizes, compared against
-the standard normal curve.
+A \code{ggplot2} object showing the standardized sampling
+ distributions for different sample sizes, compared against the
+ standard normal curve.
}
\description{
-The `demo_clt()` function generates plots to illustrate the
-Central Limit Theorem (CLT) using a specified random number
-generator. The function displays the sampling distributions
-for different sample sizes, standardized and compared against
-the standard normal distribution.
+The \code{demo_clt()} function generates plots to illustrate the
+Central Limit Theorem (CLT) using a specified random number generator.
+The function displays standardized sampling distributions for
+different sample sizes and overlays the standard normal density.
}
\examples{
set.seed(123)
-demo_clt(runif, n = c(5, 10, 20, 40), nrep = 10000, min = 0,
-max = 1)
-demo_clt(rgamma, n = c(5, 10, 20, 40), nrep = 10000, shape = 2,
-rate = 1, mean = 2, sd = sqrt(2))
+demo_clt(runif, n = c(5, 10, 20, 40), min = 0, max = 1)
+
+demo_clt(
+ rgamma,
+ n = c(5, 10, 20, 40),
+ shape = 2, rate = 1,
+ mean = 2, sd = sqrt(2)
+)
}
From 0a401c539caf459693cd7e115b47f3ae2ce97db1 Mon Sep 17 00:00:00 2001
From: Jun Yan <1536656+jun-yan@users.noreply.github.com>
Date: Thu, 8 Jan 2026 07:49:30 -0500
Subject: [PATCH 08/17] added exact methods for power.p1s.test and generated
man page
---
R/power.p1s.test.R | 4 ++
man/power.p1s.test.Rd | 88 +++++++++++++++++++++++++++++++++++++++++++
2 files changed, 92 insertions(+)
create mode 100644 man/power.p1s.test.Rd
diff --git a/R/power.p1s.test.R b/R/power.p1s.test.R
index be86187..bd84c9c 100644
--- a/R/power.p1s.test.R
+++ b/R/power.p1s.test.R
@@ -53,6 +53,10 @@
#' exact = TRUE, exact.method = "cp"
#' )
#'
+#' @importFrom stats dbinom pbinom qbinom
+#' @importFrom stats dnorm pnorm qnorm
+#' @importFrom stats uniroot density
+#
#' @export
power.p1s.test <- function(
n = NULL,
diff --git a/man/power.p1s.test.Rd b/man/power.p1s.test.Rd
new file mode 100644
index 0000000..7e032c7
--- /dev/null
+++ b/man/power.p1s.test.Rd
@@ -0,0 +1,88 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/power.p1s.test.R
+\name{power.p1s.test}
+\alias{power.p1s.test}
+\title{One-sample proportion power and sample size calculation}
+\usage{
+power.p1s.test(
+ n = NULL,
+ p0 = NULL,
+ p1 = NULL,
+ power = NULL,
+ sig.level = 0.05,
+ alternative = c("two.sided", "less", "greater"),
+ cc = FALSE,
+ exact = FALSE,
+ exact.method = c("quantile", "midp", "cp"),
+ tol = .Machine$double.eps^0.5,
+ max_n = 1e+07
+)
+}
+\arguments{
+\item{n}{Sample size for the single group.}
+
+\item{p0}{Null hypothesis proportion.}
+
+\item{p1}{Alternative hypothesis proportion.}
+
+\item{power}{Desired power.}
+
+\item{sig.level}{Significance level.}
+
+\item{alternative}{Character string specifying the alternative
+hypothesis; one of \code{"two.sided"}, \code{"less"}, or
+\code{"greater"}.}
+
+\item{cc}{Logical; if \code{TRUE}, apply continuity correction in the
+normal approximation.}
+
+\item{exact}{Logical; if \code{TRUE}, use an exact binomial method.}
+
+\item{exact.method}{Method used for exact binomial power calculation.
+\code{"quantile"} uses fixed binomial rejection regions for stable
+power and sample-size inversion; \code{"midp"} applies the mid-p
+adjustment; \code{"cp"} inverts Clopper--Pearson acceptance regions
+to guarantee size not exceeding \code{sig.level}.}
+
+\item{tol}{Numerical tolerance used in root finding.}
+
+\item{max_n}{Maximum allowable sample size when solving for \code{n}.}
+}
+\value{
+An object of class \code{"power.htest"} containing the
+ computed quantity and test specifications.
+}
+\description{
+Computes power, sample size, or detectable effect size for a
+one-sample binomial test. The interface mirrors
+\code{stats::power.prop.test()}, but for the one-sample setting.
+}
+\details{
+Power can be computed using a normal approximation or exact binomial
+methods. When \code{exact = TRUE}, the exact test is determined by
+\code{exact.method}.
+
+Exactly one of \code{n}, \code{p0}, \code{p1}, \code{power}, or
+\code{sig.level} must be \code{NULL}; the missing quantity is solved
+numerically.
+}
+\examples{
+## Normal approximation (default)
+power.p1s.test(n = 50, p0 = 0.1, p1 = 0.25)
+
+## Exact binomial power (quantile-based, default exact method)
+power.p1s.test(n = 50, p0 = 0.1, p1 = 0.25, exact = TRUE)
+
+## Exact mid-p power
+power.p1s.test(
+ n = 50, p0 = 0.1, p1 = 0.25,
+ exact = TRUE, exact.method = "midp"
+)
+
+## Exact Clopper--Pearson power (guaranteed size control)
+power.p1s.test(
+ n = 50, p0 = 0.1, p1 = 0.25,
+ exact = TRUE, exact.method = "cp"
+)
+
+}
From c6e1822bd51ba893c38a723f74789eda7b363e0f Mon Sep 17 00:00:00 2001
From: Jun Yan <1536656+jun-yan@users.noreply.github.com>
Date: Thu, 8 Jan 2026 07:50:14 -0500
Subject: [PATCH 09/17] fixed DESCRIPTION and generated NAMESPACE
---
DESCRIPTION | 20 ++++++++++++--------
NAMESPACE | 10 ++++++++++
2 files changed, 22 insertions(+), 8 deletions(-)
diff --git a/DESCRIPTION b/DESCRIPTION
index 1fbf528..33871e1 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
Package: ibist
-Title: Companion to Introduction to Biostatistics with R
-Version: 0.0-1
+Title: Data and Functions for Introduction to Biostatistics with R
+Version: 0.1-0
Authors@R: c(
person(given = "Elizabeth", family = "Schifano",
role = c("aut"),
@@ -10,15 +10,19 @@ Authors@R: c(
role = c("aut", "cre"),
comment = c(ORCID = "0000-0003-4401-7296"))
)
-Description: Datasets and functions in the book
- "Introduction to Biostatistics with R" by
- Schifano and Yan (2025), Taylor Francis.
+Description: Provides datasets and supporting functions for the book
+ Introduction to Biostatistics with R by Schifano and Yan (2026+),
+ published by Taylor & Francis. The package is intended for teaching
+ introductory biostatistics and for reproducing examples in the text.
Depends:
- R (>= 4.4.1)
+ R (>= 4.4.0)
VignetteBuilder: knitr
-Suggests: knitr, rmarkdown
License: GPL (>= 3)
URL: https://github.com/statds/ibist-R
BugReports: https://github.com/statds/ibist-R/issues
-Imports: ggplot2
+Imports: stats, rlang, ggplot2
+Suggests: knitr, testthat (>= 3.0.0)
+LazyData: true
RoxygenNote: 7.3.2
+Encoding: UTF-8
+Config/testthat/edition: 3
diff --git a/NAMESPACE b/NAMESPACE
index e0edefd..5a4ec8d 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -1,6 +1,7 @@
# Generated by roxygen2: do not edit by hand
export(demo_clt)
+export(power.p1s.test)
importFrom(ggplot2,aes)
importFrom(ggplot2,after_stat)
importFrom(ggplot2,facet_wrap)
@@ -9,3 +10,12 @@ importFrom(ggplot2,geom_line)
importFrom(ggplot2,ggplot)
importFrom(ggplot2,labs)
importFrom(ggplot2,theme_minimal)
+importFrom(rlang,.data)
+importFrom(stats,dbinom)
+importFrom(stats,density)
+importFrom(stats,dnorm)
+importFrom(stats,pbinom)
+importFrom(stats,pnorm)
+importFrom(stats,qbinom)
+importFrom(stats,qnorm)
+importFrom(stats,uniroot)
From 55c29065be7896039b5fdba4a46b7fe93f377c54 Mon Sep 17 00:00:00 2001
From: Jun Yan <1536656+jun-yan@users.noreply.github.com>
Date: Thu, 8 Jan 2026 07:50:38 -0500
Subject: [PATCH 10/17] added .Rbuildignore
---
.Rbuildignore | 32 ++++++++++++++++++++++++++++++++
1 file changed, 32 insertions(+)
create mode 100644 .Rbuildignore
diff --git a/.Rbuildignore b/.Rbuildignore
new file mode 100644
index 0000000..6c04d75
--- /dev/null
+++ b/.Rbuildignore
@@ -0,0 +1,32 @@
+\.Rcheck$
+\.Rout$
+\.Rproj$
+\.tar\.gz$
+^GPATH$
+^GRTAGS$
+^GTAGS$
+^LICENSE$
+^Makefile$
+^README\.Rmd$
+^README\.html$
+^README_cache$
+^TAGS$
+^TODO\.org$
+^\#
+^\.Rhistory$
+^\.Rproj\.user$
+^\.\#
+^\.clang_complete$
+^\.clangd$
+^\.git$
+^\.github$
+^\.gitlab-ci.yml$
+^\.travis\.yml$
+^_pkgdown\.yml$
+^appveyor\.yml$
+^docs$
+^misc$
+^revdep$
+^test$
+^working$
+~$
From 8d6da42b52ebc5786e9f93eee762558a3c34d136 Mon Sep 17 00:00:00 2001
From: Jun Yan <1536656+jun-yan@users.noreply.github.com>
Date: Thu, 8 Jan 2026 09:53:00 -0500
Subject: [PATCH 11/17] fixed cran check note; corrected cc sign
---
R/power.p1s.test.R | 96 +++++++++++++++++++---------------------------
1 file changed, 40 insertions(+), 56 deletions(-)
diff --git a/R/power.p1s.test.R b/R/power.p1s.test.R
index bd84c9c..fef54ed 100644
--- a/R/power.p1s.test.R
+++ b/R/power.p1s.test.R
@@ -103,25 +103,27 @@ power.p1s.test <- function(
)
}
+ midp = exact.method == "midp"
+
## ---- exact quantile rejection region cache ----
rr_cache <- new.env(parent = emptyenv())
- get_rr <- function(n, p0, alpha) {
- key <- paste(n, p0, alpha, alternative, sep = "|")
+ get_rr <- function(n, p0, sig.level) {
+ key <- paste(n, p0, sig.level, alternative, sep = "|")
if (exists(key, envir = rr_cache, inherits = FALSE))
return(get(key, envir = rr_cache, inherits = FALSE))
if (alternative == "greater") {
rr <- list(type = "greater",
- k = qbinom(1 - alpha, n, p0))
+ k = qbinom(1 - sig.level, n, p0))
} else if (alternative == "less") {
rr <- list(type = "less",
- k = qbinom(alpha, n, p0))
+ k = qbinom(sig.level, n, p0))
} else {
rr <- list(
type = "two.sided",
- k_lo = qbinom(alpha / 2, n, p0),
- k_hi = qbinom(1 - alpha / 2, n, p0)
+ k_lo = qbinom(sig.level / 2, n, p0),
+ k_hi = qbinom(1 - sig.level / 2, n, p0)
)
}
@@ -131,26 +133,26 @@ power.p1s.test <- function(
## ---- power bodies ----
- approx_power_body <- function(n, p0, p1, alpha) {
+ approx_power_body <- quote({
se0 <- sqrt(p0 * (1 - p0) / n)
se1 <- sqrt(p1 * (1 - p1) / n)
delta <- if (cc) 0.5 / n else 0
if (alternative == "greater") {
- z <- qnorm(1 - alpha)
- 1 - pnorm((p0 + z * se0 - delta - p1) / se1)
+ z <- qnorm(1 - sig.level)
+ 1 - pnorm((p0 + z * se0 + delta - p1) / se1)
} else if (alternative == "less") {
- z <- qnorm(1 - alpha)
- pnorm((p0 - z * se0 + delta - p1) / se1)
+ z <- qnorm(1 - sig.level)
+ pnorm((p0 - z * se0 - delta - p1) / se1)
} else {
- z <- qnorm(1 - alpha / 2)
- pnorm((p0 - z * se0 + delta - p1) / se1) +
- 1 - pnorm((p0 + z * se0 - delta - p1) / se1)
+ z <- qnorm(1 - sig.level / 2)
+ pnorm((p0 - z * se0 - delta - p1) / se1) +
+ 1 - pnorm((p0 + z * se0 + delta - p1) / se1)
}
- }
+ })
- exact_quantile_power_body <- function(n, p0, p1, alpha, midp) {
- rr <- get_rr(n, p0, alpha)
+ exact_quantile_power_body <- quote({
+ rr <- get_rr(n, p0, sig.level)
if (rr$type == "greater") {
k <- rr$k
@@ -179,76 +181,59 @@ power.p1s.test <- function(
pbinom(k_hi - 1, n, p1, lower.tail = FALSE)
}
}
- }
+ })
- cp_power_body <- function(n, p0, p1, alpha) {
+ cp_power_body <- quote({
x <- 0:n
reject <- if (alternative == "greater") {
- pbinom(x - 1, n, p0, lower.tail = FALSE) <= alpha
+ pbinom(x - 1, n, p0, lower.tail = FALSE) <= sig.level
} else if (alternative == "less") {
- pbinom(x, n, p0) <= alpha
+ pbinom(x, n, p0) <= sig.level
} else {
2 * pmin(
pbinom(x, n, p0),
pbinom(x - 1, n, p0, lower.tail = FALSE)
- ) <= alpha
+ ) <= sig.level
}
sum(dbinom(x[reject], n, p1))
- }
+ })
## ---- dispatch ----
if (!exact) {
power_body <- approx_power_body
- midp <- FALSE
+ midp <- NA
} else if (exact.method == "cp") {
power_body <- cp_power_body
- midp <- FALSE
- } else if (exact.method == "midp") {
- power_body <- function(n, p0, p1, alpha)
- exact_quantile_power_body(n, p0, p1, alpha, midp = TRUE)
- midp <- TRUE
+ midp <- NA
} else {
- power_body <- function(n, p0, p1, alpha)
- exact_quantile_power_body(n, p0, p1, alpha, midp = FALSE)
- midp <- FALSE
+ power_body <- exact_quantile_power_body
}
## ---- solve for missing parameter ----
if (is.null(power)) {
- power <- power_body(n, p0, p1, sig.level)
+ power <- eval(power_body)
}
if (is.null(n)) {
- f <- function(nn) power_body(nn, p0, p1, sig.level) - power
- n_lo <- 1L
- if (f(n_lo) >= 0) {
- n <- n_lo
- } else {
- n_hi <- 2L
- while (f(n_hi) < 0) {
- n_hi <- n_hi * 2L
- if (n_hi > max_n)
- stop("Required n exceeds 'max_n'", call. = FALSE)
- }
- n <- as.integer(ceiling(uniroot(f, c(n_lo, n_hi), tol = tol)$root))
- }
+ n <- uniroot(function(n) eval(power_body) - power,
+ c(1, max_n), tol = tol)$root
}
-
+
if (is.null(p1)) {
- f <- function(pp) power_body(n, p0, pp, sig.level) - power
- p1 <- uniroot(f, c(tol, 1 - tol), tol = tol)$root
+ p1 <- uniroot(function(pp) eval(power_body) - power,
+ c(tol, 1 - tol), tol = tol)$root
}
if (is.null(p0)) {
- f <- function(pp) power_body(n, pp, p1, sig.level) - power
- p0 <- uniroot(f, c(tol, 1 - tol), tol = tol)$root
+ p0 <- uniroot(function(pp) eval(power_body) - power,
+ c(tol, 1 - tol), tol = tol)$root
}
-
+
if (is.null(sig.level)) {
- f <- function(a) power_body(n, p0, p1, a) - power
- sig.level <- uniroot(f, c(tol, 1 - tol), tol = tol)$root
+ sig.level <- uniroot(function(a) eval(power_body) - power,
+ c(tol, 1 - tol), tol = tol)$root
}
-
+
## ---- return object ----
method <- if (!exact)
"One-sample proportion power calculation (normal approximation)"
@@ -267,7 +252,6 @@ power.p1s.test <- function(
sig.level = sig.level,
power = power,
alternative = alternative,
- note = "n is sample size for the single group",
method = method
),
class = "power.htest"
From 99e75102508deaca6728f831d797b997105be82c Mon Sep 17 00:00:00 2001
From: Jun Yan <1536656+jun-yan@users.noreply.github.com>
Date: Thu, 8 Jan 2026 18:59:44 -0500
Subject: [PATCH 12/17] fixed p1s power exact
---
R/power.p1s.test.R | 64 ++++++++++++++++++++++++++++++++++++++++------
1 file changed, 56 insertions(+), 8 deletions(-)
diff --git a/R/power.p1s.test.R b/R/power.p1s.test.R
index fef54ed..d1f1c0a 100644
--- a/R/power.p1s.test.R
+++ b/R/power.p1s.test.R
@@ -115,15 +115,15 @@ power.p1s.test <- function(
if (alternative == "greater") {
rr <- list(type = "greater",
- k = qbinom(1 - sig.level, n, p0))
+ k = qbinom(1 - sig.level, n, p0) + 1L)
} else if (alternative == "less") {
rr <- list(type = "less",
- k = qbinom(sig.level, n, p0))
+ k = qbinom(sig.level, n, p0) - 1L)
} else {
rr <- list(
type = "two.sided",
- k_lo = qbinom(sig.level / 2, n, p0),
- k_hi = qbinom(1 - sig.level / 2, n, p0)
+ k_lo = qbinom(sig.level / 2, n, p0) - 1L,
+ k_hi = qbinom(1 - sig.level / 2, n, p0) + 1L
)
}
@@ -164,8 +164,7 @@ power.p1s.test <- function(
} else if (rr$type == "less") {
k <- rr$k
if (midp)
- pbinom(k - 1, n, p1) +
- 0.5 * dbinom(k, n, p1)
+ pbinom(k - 1, n, p1) + 0.5 * dbinom(k, n, p1)
else
pbinom(k, n, p1)
} else {
@@ -215,9 +214,58 @@ power.p1s.test <- function(
}
if (is.null(n)) {
- n <- uniroot(function(n) eval(power_body) - power,
- c(1, max_n), tol = tol)$root
+ if (!exact) {
+ n <- uniroot(function(n) eval(power_body) - power,
+ c(1, max_n), tol = tol,
+ extendInt = "upX")$root
+ } else {
+ power_at_n <- function(nn) {
+ n <- as.integer(nn)
+ eval(power_body)
+ }
+
+ alpha_at_n <- function(nn) {
+ n <- as.integer(nn)
+ p1 <- p0
+ eval(power_body)
+ }
+
+ if (exact.method == "cp") {
+ feasible <- function(nn) {
+ (power_at_n(nn) >= power)
+ }
+
+ } else {
+ ## quantile / mid-p: enforce size <= nominal (optionally also add
+ ## a tol.alpha rule if you want parity across exact methods).
+ feasible <- function(nn) {
+ (alpha_at_n(nn) <= sig.level) &&
+ (power_at_n(nn) >= power)
+ }
+ }
+
+ ## ---- exponential bracketing + integer bisection ----
+ n_lo <- 1L
+ if (feasible(n_lo)) {
+ n <- n_lo
+ } else {
+ n_hi <- 2L
+ while (!feasible(n_hi)) {
+ n_lo <- n_hi
+ n_hi <- n_hi * 2L
+ if (n_hi > max_n)
+ stop("Required n exceeds 'max_n'", call. = FALSE)
+ }
+ while (n_hi - n_lo > 1L) {
+ n_mid <- (n_lo + n_hi) %/% 2L
+ if (feasible(n_mid)) n_hi <- n_mid else n_lo <- n_mid
+ }
+ n <- n_hi
+ }
+ power <- power_at_n(n)
+ }
}
+
if (is.null(p1)) {
p1 <- uniroot(function(pp) eval(power_body) - power,
From 53f68bc12a0317b42a82573f79a41160f9e11d6b Mon Sep 17 00:00:00 2001
From: Jun Yan <1536656+jun-yan@users.noreply.github.com>
Date: Tue, 13 Jan 2026 12:01:05 -0500
Subject: [PATCH 13/17] fixed argument passing in demo_clt
---
R/demo_clt.R | 40 ++++++++++++++++------------------------
man/demo_clt.Rd | 13 +++++--------
vignettes/clt.Rmd | 9 ++++-----
3 files changed, 25 insertions(+), 37 deletions(-)
diff --git a/R/demo_clt.R b/R/demo_clt.R
index 9332983..9fe0f64 100644
--- a/R/demo_clt.R
+++ b/R/demo_clt.R
@@ -14,9 +14,9 @@
#' (default is 10000).
#' @param ... Additional arguments passed to the random number generator
#' (e.g., \code{shape} and \code{rate} for \code{rgamma}).
-#' @param mean The theoretical mean of the distribution. If \code{NULL},
+#' @param pmean The population mean of the distribution. If \code{NULL},
#' it is estimated from a large Monte Carlo sample.
-#' @param sd The theoretical standard deviation of the distribution.
+#' @param psd The population standard deviation of the distribution.
#' If \code{NULL}, it is estimated from a large Monte Carlo sample.
#'
#' @return A \code{ggplot2} object showing the standardized sampling
@@ -27,11 +27,8 @@
#' set.seed(123)
#' demo_clt(runif, n = c(5, 10, 20, 40), min = 0, max = 1)
#'
-#' demo_clt(
-#' rgamma,
-#' n = c(5, 10, 20, 40),
-#' shape = 2, rate = 1,
-#' mean = 2, sd = sqrt(2)
+#' demo_clt(rgamma, n = c(5, 10, 20, 40), shape = 2, rate = 1,
+#' pmean = 2, psd = sqrt(2)
#' )
#'
#' @importFrom rlang .data
@@ -43,8 +40,8 @@ demo_clt <- function(
n,
nrep = 10000,
...,
- mean = NULL,
- sd = NULL
+ pmean = NULL,
+ psd = NULL
) {
## ---- basic validation ----
if (!is.function(rng)) {
@@ -65,11 +62,11 @@ demo_clt <- function(
)
}
- ## ---- estimate mean and sd if needed ----
- if (is.null(mean) || is.null(sd)) {
+ ## ---- estimate pmean and psd if needed ----
+ if (is.null(pmean) || is.null(psd)) {
sample_data <- rng(100000, ...)
- mean <- base::mean(sample_data)
- sd <- stats::sd(sample_data)
+ if (is.null(pmean)) pmean <- base::mean(sample_data)
+ if (is.null(psd)) psd <- stats::sd(sample_data)
}
## ---- generate standardized sample means ----
@@ -77,17 +74,12 @@ demo_clt <- function(
names(results) <- as.character(n)
for (size in n) {
- sample_means <- replicate(
- nrep,
- base::mean(rng(size, ...))
- )
-
- standardized_means <-
- (sample_means - mean) / (sd / sqrt(size))
+ rng_local <- function() rng(size, ...)
+ sample_means <- replicate(nrep, base::mean(rng_local()))
results[[as.character(size)]] <- data.frame(
- StandardizedMean = standardized_means,
- SampleSize = factor(size)
+ StdMean = (sample_means - pmean) / (psd / sqrt(size)),
+ SampleSize = size
)
}
@@ -103,7 +95,7 @@ demo_clt <- function(
## ---- plot ----
ggplot2::ggplot(
data,
- ggplot2::aes(x = .data$StandardizedMean)
+ ggplot2::aes(x = .data$StdMean)
) +
ggplot2::geom_histogram(
ggplot2::aes(y = ggplot2::after_stat(density)),
@@ -121,7 +113,7 @@ demo_clt <- function(
ggplot2::facet_wrap(~ SampleSize) +
ggplot2::labs(
title = "Demonstrating the Central Limit Theorem",
- x = "Standardized Sample Mean",
+ x = "Standardized sample mean",
y = "Density"
) +
ggplot2::theme_minimal()
diff --git a/man/demo_clt.Rd b/man/demo_clt.Rd
index 7780b55..8ec2dfa 100644
--- a/man/demo_clt.Rd
+++ b/man/demo_clt.Rd
@@ -4,7 +4,7 @@
\alias{demo_clt}
\title{Demonstrate the Central Limit Theorem}
\usage{
-demo_clt(rng, n, nrep = 10000, ..., mean = NULL, sd = NULL)
+demo_clt(rng, n, nrep = 10000, ..., pmean = NULL, psd = NULL)
}
\arguments{
\item{rng}{A random number generator function taking the sample size
@@ -20,10 +20,10 @@ as its first argument (e.g., \code{runif}, \code{rnorm},
\item{...}{Additional arguments passed to the random number generator
(e.g., \code{shape} and \code{rate} for \code{rgamma}).}
-\item{mean}{The theoretical mean of the distribution. If \code{NULL},
+\item{pmean}{The population mean of the distribution. If \code{NULL},
it is estimated from a large Monte Carlo sample.}
-\item{sd}{The theoretical standard deviation of the distribution.
+\item{psd}{The population standard deviation of the distribution.
If \code{NULL}, it is estimated from a large Monte Carlo sample.}
}
\value{
@@ -41,11 +41,8 @@ different sample sizes and overlays the standard normal density.
set.seed(123)
demo_clt(runif, n = c(5, 10, 20, 40), min = 0, max = 1)
-demo_clt(
- rgamma,
- n = c(5, 10, 20, 40),
- shape = 2, rate = 1,
- mean = 2, sd = sqrt(2)
+demo_clt(rgamma, n = c(5, 10, 20, 40), shape = 2, rate = 1,
+ pmean = 2, psd = sqrt(2)
)
}
diff --git a/vignettes/clt.Rmd b/vignettes/clt.Rmd
index 805aad5..820de8d 100644
--- a/vignettes/clt.Rmd
+++ b/vignettes/clt.Rmd
@@ -1,4 +1,4 @@
----
+mean---
title: "Demonstrating the Central Limit Theorem"
author: "Companion Package for Biostatistical Analysis of Proportions and Rates"
date: "`r Sys.Date()`"
@@ -43,7 +43,7 @@ as the sample size increases.
```{r gamma-shape-0.5}
demo_clt(rgamma, n = c(5, 10, 20, 40), nrep = 10000,
- shape = 0.5, rate = 1, mean = 0.5, sd = sqrt(0.5))
+ shape = 0.5, rate = 1, pmean = 0.5, psd = sqrt(0.5))
```
# Gamma Distribution with Shape = 1
@@ -54,7 +54,7 @@ and we observe the effect of increasing the sample size.
```{r gamma-shape-1}
demo_clt(rgamma, n = c(5, 10, 20, 40), nrep = 10000,
- shape = 1, rate = 1, mean = 1, sd = sqrt(1))
+ shape = 1, rate = 1, pmean = 1, psd = sqrt(1))
```
# Gamma Distribution with Shape = 2
@@ -65,7 +65,7 @@ distribution even for smaller sample sizes.
```{r gamma-shape-2}
demo_clt(rgamma, n = c(5, 10, 20, 40), nrep = 10000,
- shape = 2, rate = 1, mean = 2, sd = sqrt(2))
+ shape = 2, rate = 1, pmean = 2, psd = sqrt(2))
```
# A General Distribution Constructed from a Mixture
@@ -85,7 +85,6 @@ demo_clt(mymix_rng, n = c(5, 10, 20, 40))
```
-
# Conclusion
The plots above demonstrate the Central Limit Theorem in action.
From c695270c7e53343a133522ade4fd310fa5da9c11 Mon Sep 17 00:00:00 2001
From: Jun Yan <1536656+jun-yan@users.noreply.github.com>
Date: Sat, 24 Jan 2026 10:41:31 -0500
Subject: [PATCH 14/17] added rate.test() for poisson rate or mean
---
R/rate_test.R | 218 ++++++++++++++++++++++++++++++++++++++++++++++++++
1 file changed, 218 insertions(+)
create mode 100644 R/rate_test.R
diff --git a/R/rate_test.R b/R/rate_test.R
new file mode 100644
index 0000000..61c6f81
--- /dev/null
+++ b/R/rate_test.R
@@ -0,0 +1,218 @@
+#' Test of Poisson Rates Using Normal Approximation
+#'
+#' Performs a large-sample (normal) test for one or two Poisson rates with known
+#' exposures. The test is carried out on the observed count scale and then
+#' translated to rates for reporting.
+#'
+#' @param x a vector of event counts. A single value specifies a one-sample
+#' test; a vector of length two specifies a two-sample comparison.
+#' @param T a vector of exposures corresponding to \code{x} (e.g., person-time).
+#' Must have the same length as \code{x}.
+#' @param r a positive number specifying the null rate per unit exposure,
+#' \eqn{\lambda_0}, for a one-sample test. Ignored for two-sample tests.
+#' @param alternative a character string specifying the alternative hypothesis,
+#' one of \code{"two.sided"}, \code{"less"}, or \code{"greater"}.
+#' @param conf.level confidence level for the confidence interval.
+#' @param correct logical; if \code{TRUE}, applies a continuity correction on
+#' the count scale. For one-sample tests, a \eqn{\pm 0.5} correction is applied
+#' to \eqn{x} in the direction determined by \code{alternative}. For
+#' \code{"two.sided"}, the correction uses the sign of \eqn{x-\mu_0}.
+#'
+#' @details
+#' One-sample test:
+#' Assume \eqn{X \sim \mathrm{Pois}(\mu)} with \eqn{\mu = \lambda T}. The null
+#' hypothesis is \eqn{H_0:\lambda=\lambda_0}, where \eqn{\lambda_0=r}. Let
+#' \eqn{\mu_0=\lambda_0 T}. The normal approximation gives
+#' \deqn{Z = \frac{(x - c) - \mu_0}{\sqrt{\mu_0}} \approx N(0,1),}
+#' where \eqn{c} is 0 if \code{correct=FALSE} and is a continuity correction on
+#' the count scale if \code{correct=TRUE}. Two-sided p-values use
+#' \eqn{2\{1-\Phi(|Z|)\}}.
+#'
+#' #' Two-sample test:
+#' Assume independent \eqn{X_1 \sim \mathrm{Pois}(\lambda_1 T_1)} and
+#' \eqn{X_2 \sim \mathrm{Pois}(\lambda_2 T_2)} with known exposures
+#' \eqn{T_1} and \eqn{T_2}. The null hypothesis is
+#' \eqn{H_0:\lambda_1=\lambda_2}.
+#'
+#' For hypothesis testing, the function uses the exact conditional
+#' representation under \eqn{H_0}:
+#' \deqn{
+#' X_1 \mid (X_1+X_2=n)
+#' \sim
+#' \mathrm{Binom}\!\left(n,\; \frac{T_1}{T_1+T_2}\right),
+#' }
+#' and applies the same large-sample normal approximation as
+#' \code{\link[stats]{prop.test}} (with optional Yates continuity correction)
+#' to obtain the test statistic and p-value.
+#'
+#' For estimation and confidence intervals, the inference target is the
+#' difference between the two rates,
+#' \eqn{\Delta = \lambda_1 - \lambda_2}.
+#' The point estimate is
+#' \eqn{\hat\Delta = X_1/T_1 - X_2/T_2}, and the confidence interval is
+#' constructed using a normal approximation with estimated standard error
+#' \deqn{
+#' \sqrt{X_1/T_1^2 + X_2/T_2^2}.
+#' }
+#' The continuity correction affects the hypothesis test but not the
+#' confidence interval for \eqn{\Delta}.
+#'
+#' Confidence intervals:
+#' For one-sample tests, the confidence interval is for \eqn{\lambda} and is
+#' obtained by inverting the same normal approximation on the count scale for
+#' \eqn{\mu=\lambda T}, then dividing by \eqn{T}.
+#' For two-sample tests, the confidence interval is for the difference in rates
+#' \eqn{\lambda_1-\lambda_2}, analogous to the difference in proportions in
+#' \code{\link[stats]{prop.test}}.
+#'
+#' @return
+#' An object of class \code{"htest"} containing:
+#' \item{statistic}{the standardized normal statistic \code{z}.}
+#' \item{parameter}{degrees of freedom (\code{df = 1}).}
+#' \item{p.value}{the p-value.}
+#' \item{conf.int}{a confidence interval for the difference between the two
+#' rates, \eqn{\lambda_1-\lambda_2}, for two-sample tests.}
+#' \item{estimate}{estimated rate (one-sample) or estimated rates (two-sample).}
+#' \item{null.value}{the null rate (one-sample) or the null rate difference
+#' \eqn{\lambda_1-\lambda_2=0} (two-sample).}
+#' \item{alternative}{the alternative hypothesis.}
+#' \item{method}{a character string describing the test.}
+#' \item{data.name}{a character string describing the data.}
+#'
+#' @seealso
+#' \code{\link[stats]{prop.test}}, \code{\link[stats]{poisson.test}}
+#'
+#' @examples
+#' ## One-sample test: compare observed rate to a reference unit rate
+#' rate.test(x = 411, T = 25800, r = 0.0119)
+#' rate.test(x = 411, T = 25800, r = 0.0119, correct = FALSE)
+#'
+#' ## Two-sample test: compare two Poisson rates
+#' rate.test(x = c(12, 5), T = c(100, 80))
+#' rate.test(x = c(12, 5), T = c(100, 80), correct = FALSE)
+#'
+#' ## One-sided alternative
+#' rate.test(x = 411, T = 25800, r = 0.0119, alternative = "greater")
+#'
+#' @export
+rate.test <- function(x, T, r = NULL,
+ alternative = c("two.sided", "less", "greater"),
+ conf.level = 0.95,
+ correct = TRUE)
+{
+ alternative <- match.arg(alternative)
+
+ if (length(x) != length(T)) stop("'x' and 'T' must have the same length")
+ if (!length(x) %in% c(1L, 2L)) stop("'x' must have length 1 or 2")
+ if (any(!is.finite(x)) || any(!is.finite(T))) stop("'x' and 'T' must be finite")
+ if (any(x < 0) || any(abs(x - round(x)) > 0)) stop("'x' must be nonnegative integers")
+ if (any(T <= 0)) stop("'T' must be positive")
+ if (!is.numeric(conf.level) || length(conf.level) != 1L ||
+ conf.level <= 0 || conf.level >= 1) {
+ stop("'conf.level' must be a single number in (0, 1)")
+ }
+
+ zcrit <- qnorm(1 - (1 - conf.level) / 2)
+
+ if (length(x) == 1L) {
+ ## --- One-sample: H0: lambda = r (unit rate)
+ if (is.null(r) || length(r) != 1L || !is.finite(r) || r <= 0) {
+ stop("a single positive null unit rate 'r' must be specified")
+ }
+
+ mu0 <- r * T
+
+ ## CC on count scale
+ cc <- 0
+ if (isTRUE(correct)) {
+ cc <- switch(alternative,
+ "greater" = 0.5,
+ "less" = -0.5,
+ "two.sided" = if (x == mu0) 0 else 0.5 * sign(x - mu0)
+ )
+ }
+
+ ## Standardize observed count against null mean (score-style)
+ z <- (x - cc - mu0) / sqrt(mu0)
+
+ p.value <- switch(alternative,
+ "two.sided" = 2 * pnorm(-abs(z)),
+ "less" = pnorm(z),
+ "greater" = pnorm(z, lower.tail = FALSE)
+ )
+
+ ## CI for lambda by inverting |(x-c)-mu|/sqrt(mu) <= zcrit, then /T
+ x_adj <- x - cc
+ disc <- zcrit^2 + 4 * x_adj
+ mu_lo <- 0.5 * (zcrit^2 + 2 * x_adj - zcrit * sqrt(disc))
+ mu_hi <- 0.5 * (zcrit^2 + 2 * x_adj + zcrit * sqrt(disc))
+ mu_lo <- max(0, mu_lo)
+ conf.int <- c(mu_lo, mu_hi) / T
+
+ estimate <- c(rate = x / T)
+ null.value <- c(rate = r)
+
+ ## Ingredients (shared names)
+ statistic <- c(z = as.numeric(z))
+ parameter <- c(df = 1)
+ method <- "Normal approximation test for a Poisson rate"
+ data.name <- paste0("x = ", x, ", T = ", T)
+
+ } else {
+
+ ## --- Two-sample: H0: lambda1 = lambda2
+ x1 <- x[1]; x2 <- x[2]
+ T1 <- T[1]; T2 <- T[2]
+
+ if (x1 + x2 == 0) {
+ stop("at least one event is required for the 2-sample test")
+ }
+
+ ## Conditional binomial test under H0 via prop.test()
+ n <- x1 + x2
+ p0 <- T1 / (T1 + T2)
+
+ pt <- stats::prop.test(x = x1, n = n,p = p0, alternative = alternative,
+ conf.level = conf.level, correct = correct)
+
+ ## Estimation target: difference in rates
+ rate1 <- x1 / T1
+ rate2 <- x2 / T2
+ diff_hat <- rate1 - rate2
+
+ se_diff <- sqrt(x1 / T1^2 + x2 / T2^2)
+ conf.int <- diff_hat + c(-1, 1) * zcrit * se_diff
+
+ estimate <- c("rate 1" = rate1, "rate 2" = rate2)
+ null.value <- c("rate difference" = 0)
+
+ ## Ingredients (shared names)
+ statistic <- pt$statistic
+ parameter <- pt$parameter
+ p.value <- pt$p.value
+ method <- paste(
+ "2-sample test for equality of Poisson rates",
+ "(conditional binomial normal approximation);",
+ "CI for rate difference by normal approximation"
+ )
+ data.name <- paste0(
+ "x = c(", x1, ", ", x2, "), ",
+ "T = c(", T1, ", ", T2, ")"
+ )
+ }
+
+ ## --- Shared return block (single place)
+ rval <- list(
+ statistic = statistic,
+ parameter = parameter,
+ p.value = as.numeric(p.value),
+ conf.int = structure(conf.int, conf.level = conf.level),
+ estimate = estimate,
+ null.value = null.value,
+ alternative = alternative,
+ method = method,
+ data.name = data.name
+ )
+ class(rval) <- "htest"
+ rval
+}
From 973c3e9581b91ea9ad80f5746b37ddfc0291b1d8 Mon Sep 17 00:00:00 2001
From: Jun Yan <1536656+jun-yan@users.noreply.github.com>
Date: Thu, 5 Feb 2026 09:27:29 -0500
Subject: [PATCH 15/17] added rate.test to man and NAMESPACE
---
man/rate.test.Rd | 117 +++++++++++++++++++++++++++++++++++++++++++++++
1 file changed, 117 insertions(+)
create mode 100644 man/rate.test.Rd
diff --git a/man/rate.test.Rd b/man/rate.test.Rd
new file mode 100644
index 0000000..bcb58c3
--- /dev/null
+++ b/man/rate.test.Rd
@@ -0,0 +1,117 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/rate_test.R
+\name{rate.test}
+\alias{rate.test}
+\title{Test of Poisson Rates Using Normal Approximation}
+\usage{
+rate.test(
+ x,
+ T,
+ r = NULL,
+ alternative = c("two.sided", "less", "greater"),
+ conf.level = 0.95,
+ correct = TRUE
+)
+}
+\arguments{
+\item{x}{a vector of event counts. A single value specifies a one-sample
+test; a vector of length two specifies a two-sample comparison.}
+
+\item{T}{a vector of exposures corresponding to \code{x} (e.g., person-time).
+Must have the same length as \code{x}.}
+
+\item{r}{a positive number specifying the null rate per unit exposure,
+\eqn{\lambda_0}, for a one-sample test. Ignored for two-sample tests.}
+
+\item{alternative}{a character string specifying the alternative hypothesis,
+one of \code{"two.sided"}, \code{"less"}, or \code{"greater"}.}
+
+\item{conf.level}{confidence level for the confidence interval.}
+
+\item{correct}{logical; if \code{TRUE}, applies a continuity correction on
+the count scale. For one-sample tests, a \eqn{\pm 0.5} correction is applied
+to \eqn{x} in the direction determined by \code{alternative}. For
+\code{"two.sided"}, the correction uses the sign of \eqn{x-\mu_0}.}
+}
+\value{
+An object of class \code{"htest"} containing:
+\item{statistic}{the standardized normal statistic \code{z}.}
+\item{parameter}{degrees of freedom (\code{df = 1}).}
+\item{p.value}{the p-value.}
+\item{conf.int}{a confidence interval for the difference between the two
+ rates, \eqn{\lambda_1-\lambda_2}, for two-sample tests.}
+\item{estimate}{estimated rate (one-sample) or estimated rates (two-sample).}
+\item{null.value}{the null rate (one-sample) or the null rate difference
+ \eqn{\lambda_1-\lambda_2=0} (two-sample).}
+\item{alternative}{the alternative hypothesis.}
+\item{method}{a character string describing the test.}
+\item{data.name}{a character string describing the data.}
+}
+\description{
+Performs a large-sample (normal) test for one or two Poisson rates with known
+exposures. The test is carried out on the observed count scale and then
+translated to rates for reporting.
+}
+\details{
+One-sample test:
+Assume \eqn{X \sim \mathrm{Pois}(\mu)} with \eqn{\mu = \lambda T}. The null
+hypothesis is \eqn{H_0:\lambda=\lambda_0}, where \eqn{\lambda_0=r}. Let
+\eqn{\mu_0=\lambda_0 T}. The normal approximation gives
+\deqn{Z = \frac{(x - c) - \mu_0}{\sqrt{\mu_0}} \approx N(0,1),}
+where \eqn{c} is 0 if \code{correct=FALSE} and is a continuity correction on
+the count scale if \code{correct=TRUE}. Two-sided p-values use
+\eqn{2\{1-\Phi(|Z|)\}}.
+
+#' Two-sample test:
+Assume independent \eqn{X_1 \sim \mathrm{Pois}(\lambda_1 T_1)} and
+\eqn{X_2 \sim \mathrm{Pois}(\lambda_2 T_2)} with known exposures
+\eqn{T_1} and \eqn{T_2}. The null hypothesis is
+\eqn{H_0:\lambda_1=\lambda_2}.
+
+For hypothesis testing, the function uses the exact conditional
+representation under \eqn{H_0}:
+\deqn{
+X_1 \mid (X_1+X_2=n)
+\sim
+\mathrm{Binom}\!\left(n,\; \frac{T_1}{T_1+T_2}\right),
+}
+and applies the same large-sample normal approximation as
+\code{\link[stats]{prop.test}} (with optional Yates continuity correction)
+to obtain the test statistic and p-value.
+
+For estimation and confidence intervals, the inference target is the
+difference between the two rates,
+\eqn{\Delta = \lambda_1 - \lambda_2}.
+The point estimate is
+\eqn{\hat\Delta = X_1/T_1 - X_2/T_2}, and the confidence interval is
+constructed using a normal approximation with estimated standard error
+\deqn{
+\sqrt{X_1/T_1^2 + X_2/T_2^2}.
+}
+The continuity correction affects the hypothesis test but not the
+confidence interval for \eqn{\Delta}.
+
+Confidence intervals:
+For one-sample tests, the confidence interval is for \eqn{\lambda} and is
+obtained by inverting the same normal approximation on the count scale for
+\eqn{\mu=\lambda T}, then dividing by \eqn{T}.
+For two-sample tests, the confidence interval is for the difference in rates
+\eqn{\lambda_1-\lambda_2}, analogous to the difference in proportions in
+\code{\link[stats]{prop.test}}.
+}
+\examples{
+## One-sample test: compare observed rate to a reference unit rate
+rate.test(x = 411, T = 25800, r = 0.0119)
+rate.test(x = 411, T = 25800, r = 0.0119, correct = FALSE)
+
+## Two-sample test: compare two Poisson rates
+rate.test(x = c(12, 5), T = c(100, 80))
+rate.test(x = c(12, 5), T = c(100, 80), correct = FALSE)
+
+## One-sided alternative
+rate.test(x = 411, T = 25800, r = 0.0119, alternative = "greater")
+
+}
+\seealso{
+\code{\link[stats]{prop.test}}, \code{\link[stats]{poisson.test}}
+}
From 6d5771edef4f6a832e1ce75912c12d45c0172723 Mon Sep 17 00:00:00 2001
From: Jun Yan <1536656+jun-yan@users.noreply.github.com>
Date: Thu, 5 Feb 2026 09:28:08 -0500
Subject: [PATCH 16/17] added nrs data
---
NAMESPACE | 1 +
R/data-nrs.R | 43 ++++++++++++++++++++++++++++++++++++++++++
data-raw/nrs.R | 5 +++++
man/nrs.Rd | 51 ++++++++++++++++++++++++++++++++++++++++++++++++++
4 files changed, 100 insertions(+)
create mode 100644 R/data-nrs.R
create mode 100644 data-raw/nrs.R
create mode 100644 man/nrs.Rd
diff --git a/NAMESPACE b/NAMESPACE
index 5a4ec8d..f7d3888 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -2,6 +2,7 @@
export(demo_clt)
export(power.p1s.test)
+export(rate.test)
importFrom(ggplot2,aes)
importFrom(ggplot2,after_stat)
importFrom(ggplot2,facet_wrap)
diff --git a/R/data-nrs.R b/R/data-nrs.R
new file mode 100644
index 0000000..a17d783
--- /dev/null
+++ b/R/data-nrs.R
@@ -0,0 +1,43 @@
+#' Non-restorative sleep and physical activity (Japan cohort study)
+#'
+#' A large observational dataset from a cohort study conducted in Japan
+#' to examine the association between non-restorative sleep (NRS) and
+#' physical activity, gender, and age. The data are used to illustrate
+#' logistic regression modeling for a binary outcome in a large-sample
+#' setting.
+#'
+#' @format
+#' A data frame with 90,122 observations on the following variables:
+#' \describe{
+#' \item{id}{Subject identifier.}
+#' \item{Gender}{Gender of the subject (integer-coded).}
+#' \item{Age_2013}{Age in years in 2013.}
+#' \item{EX_2013}{Indicator of regular exercise in 2013
+#' (integer-coded).}
+#' \item{PA_2013}{Physical activity measure in 2013
+#' (integer-coded).}
+#' \item{NRS_2013}{Indicator of non-restorative sleep in 2013
+#' (1 = presence, 0 = absence).}
+#' \item{AgeGroup_2013}{Categorical age group in 2013
+#' (integer-coded).}
+#' \item{EXPA_classification}{Combined classification of exercise and
+#' physical activity status (integer-coded).}
+#' }
+#'
+#' @details
+#' Non-restorative sleep (NRS) is defined as a subjective feeling of lack
+#' of refreshment on awakening and reflects qualitative aspects of sleep.
+#' Hidaka et al. (2019) analyzed these data using logistic regression to
+#' assess whether the probability of NRS is associated with physical
+#' activity, gender, and age in a large cohort of adult subjects in
+#' Japan. Within this package, the dataset is provided for methodological
+#' illustration of binary regression models rather than for substantive
+#' epidemiological inference.
+#'
+#' All variables are stored as integer codes. Missing values are
+#' represented as \code{NA}.
+#'
+#' @source
+#' Hidaka et al. (2019).
+#'
+"nrs"
diff --git a/data-raw/nrs.R b/data-raw/nrs.R
new file mode 100644
index 0000000..efa0f00
--- /dev/null
+++ b/data-raw/nrs.R
@@ -0,0 +1,5 @@
+## data-raw/nrs.R
+
+nrs <- read.csv("data-raw/nrs.csv", header = TRUE)
+
+usethis::use_data(nrs, overwrite = TRUE)
diff --git a/man/nrs.Rd b/man/nrs.Rd
new file mode 100644
index 0000000..575a0d6
--- /dev/null
+++ b/man/nrs.Rd
@@ -0,0 +1,51 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/data-nrs.R
+\docType{data}
+\name{nrs}
+\alias{nrs}
+\title{Non-restorative sleep and physical activity (Japan cohort study)}
+\format{
+A data frame with 90,122 observations on the following variables:
+\describe{
+ \item{id}{Subject identifier.}
+ \item{Gender}{Gender of the subject (integer-coded).}
+ \item{Age_2013}{Age in years in 2013.}
+ \item{EX_2013}{Indicator of regular exercise in 2013
+ (integer-coded).}
+ \item{PA_2013}{Physical activity measure in 2013
+ (integer-coded).}
+ \item{NRS_2013}{Indicator of non-restorative sleep in 2013
+ (1 = presence, 0 = absence).}
+ \item{AgeGroup_2013}{Categorical age group in 2013
+ (integer-coded).}
+ \item{EXPA_classification}{Combined classification of exercise and
+ physical activity status (integer-coded).}
+}
+}
+\source{
+Hidaka et al. (2019).
+}
+\usage{
+nrs
+}
+\description{
+A large observational dataset from a cohort study conducted in Japan
+to examine the association between non-restorative sleep (NRS) and
+physical activity, gender, and age. The data are used to illustrate
+logistic regression modeling for a binary outcome in a large-sample
+setting.
+}
+\details{
+Non-restorative sleep (NRS) is defined as a subjective feeling of lack
+of refreshment on awakening and reflects qualitative aspects of sleep.
+Hidaka et al. (2019) analyzed these data using logistic regression to
+assess whether the probability of NRS is associated with physical
+activity, gender, and age in a large cohort of adult subjects in
+Japan. Within this package, the dataset is provided for methodological
+illustration of binary regression models rather than for substantive
+epidemiological inference.
+
+All variables are stored as integer codes. Missing values are
+represented as \code{NA}.
+}
+\keyword{datasets}
From d2874a9364aaadc09495f476241ae0b94d5bef7f Mon Sep 17 00:00:00 2001
From: Jun Yan <1536656+jun-yan@users.noreply.github.com>
Date: Thu, 5 Feb 2026 09:28:33 -0500
Subject: [PATCH 17/17] added nrs.rda
---
data/nrs.rda | Bin 0 -> 216779 bytes
1 file changed, 0 insertions(+), 0 deletions(-)
create mode 100644 data/nrs.rda
diff --git a/data/nrs.rda b/data/nrs.rda
new file mode 100644
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