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‎.github/workflows/main.yml‎

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# This workflow uses actions that are not certified by GitHub.
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# They are provided by a third-party and are governed by
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# separate terms of service, privacy policy, and support
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# documentation.
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#
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# See https://github.com/r-lib/actions/tree/master/examples#readme for
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# additional example workflows available for the R community.
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name: R
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on:
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push:
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branches: [ "master" ]
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pull_request:
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branches: [ "master" ]
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permissions:
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contents: write
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jobs:
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build:
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runs-on: macos-latest
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strategy:
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matrix:
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r-version: ['3.6.3', '4.1.1']
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steps:
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- uses: actions/checkout@v3
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- name: Set up R ${{ matrix.r-version }}
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uses: r-lib/actions/setup-r@f57f1301a053485946083d7a45022b278929a78a
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with:
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r-version: ${{ matrix.r-version }}
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- name: Install dependencies
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run: |
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install.packages(c("remotes", "rcmdcheck"))
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remotes::install_deps(dependencies = TRUE)
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shell: Rscript {0}
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- name: Check
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run: rcmdcheck::rcmdcheck(args = "--no-manual", error_on = "error")
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shell: Rscript {0}

‎.travis.yml‎

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‎Meta/vignette.rds‎

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‎R/curve_gen.R‎

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#' logistic regression and the 'glm' function. Similarly, the Glm function from the
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#' rms package can also be used for this option. The gls method allows objects from gls()
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#' or from Gls() from the rms package.
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#' @param log Determines whether the coefficients will be exponentiated or not. By default,
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#' @param log Determines whether the coefficients will be exponentiated or not. By default,
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#' it is off and set to FALSE or F, but changing this to TRUE or T, will exponentiate the results
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#' which may be useful if trying to view the results from a logistic regression on a scale that is not
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#' logarithmic.
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#' bob <- curve_gen(rob, "GroupB")
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#' }
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#'
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#'
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if ((Sys.info()["sysname"]) == "Windows") {
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curve_gen <- function(model, var, method = "lm", log = FALSE, penalty = NULL, m = NULL,
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steps = 1000, table = TRUE) {
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if (is.character(method) != TRUE) {
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stop("Error: 'method' must be a character vector")
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}
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if (is.numeric(steps) != TRUE) {
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stop("Error: 'steps' must be a numeric vector")
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}
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intrvls <- (1:(steps - 1)) / steps
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# No adjustment for multiple comparisons ----------------------------------
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if (is.null(penalty) & is.null(m)) {
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if (method == "lm") {
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results <- lapply(intrvls, FUN = function(i) confint.default(object = model, level = i)[var, ])
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} else if (method == "rlm") {
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results <- lapply(intrvls, FUN = function(i) confint(object = model, level = i)[var, ])
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} else if (method == "glm") {
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results <- lapply(intrvls, FUN = function(i) confint(object = model, level = i, trace = FALSE)[var, ])
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} else if (method == "aov") {
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results <- lapply(intrvls, FUN = function(i) confint(object = model, level = i)[var, ])
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} else if (method == "gls") {
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results <- lapply(intrvls, FUN = function(i) confint.default(object = model, level = i)[var, ])
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}
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# Bonferroni adjustment for multiple comparisons --------------------------
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} else if (penalty == "bonferroni" & m > 1) {
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bon.adj <- (1 - ((1 - intrvls) / m))
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if (method == "lm") {
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results <- lapply(bon.adj, FUN = function(i) confint.default(object = model, level = i)[var, ])
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} else if (method == "rlm") {
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results <- lapply(bon.adj, FUN = function(i) confint(object = model, level = i)[var, ])
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} else if (method == "glm") {
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results <- lapply(bon.adj, FUN = function(i) confint(object = model, level = i, trace = FALSE)[var, ])
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} else if (method == "aov") {
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results <- lapply(bon.adj, FUN = function(i) confint(object = model, level = i)[var, ])
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} else if (method == "gls") {
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results <- lapply(bon.adj, FUN = function(i) confint.default(object = model, level = i)[var, ])
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}
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# Sidak adjustment for multiple comparisons -------------------------------
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} else if (penalty == "sidak" & m > 1) {
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sidak.adj <- (((intrvls)^(1 / m)))
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if (method == "lm") {
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results <- lapply(sidak.adj, FUN = function(i) confint.default(object = model, level = i)[var, ])
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} else if (method == "rlm") {
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results <- lapply(sidak.adj, FUN = function(i) confint(object = model, level = i)[var, ])
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} else if (method == "glm") {
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results <- lapply(sidak.adj, FUN = function(i) confint(object = model, level = i, trace = FALSE)[var, ])
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} else if (method == "aov") {
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results <- lapply(sidak.adj, FUN = function(i) confint(object = model, level = i)[var, ])
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} else if (method == "gls") {
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results <- lapply(sidak.adj, FUN = function(i) confint.default(object = model, level = i)[var, ])
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}
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}
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df <- data.frame(do.call(rbind, results))
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if (log == FALSE) {
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df <- (df)
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} else if (log == TRUE) {
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df <- exp(df)
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}
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intrvl.limit <- c("lower.limit", "upper.limit")
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colnames(df) <- intrvl.limit
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df$intrvl.width <- (abs((df$upper.limit) - (df$lower.limit)))
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df$intrvl.level <- intrvls
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df$cdf <- (abs(df$intrvl.level / 2)) + 0.5
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df$pvalue <- 1 - intrvls
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df$svalue <- -log2(df$pvalue)
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df <- head(df, -1)
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class(df) <- c("data.frame", "concurve")
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densdf <- data.frame(c(df$lower.limit, df$upper.limit))
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colnames(densdf) <- "x"
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densdf <- head(densdf, -1)
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class(densdf) <- c("data.frame", "concurve")
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if (table == TRUE) {
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levels <- c(0.25, 0.50, 0.75, 0.80, 0.85, 0.90, 0.95, 0.975, 0.99)
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(df_subintervals <- (curve_table(df, levels, type = "c", format = "data.frame")))
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class(df_subintervals) <- c("data.frame", "concurve")
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dataframes <- list(df, densdf, df_subintervals)
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names(dataframes) <- c("Intervals Dataframe", "Intervals Density", "Intervals Table")
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class(dataframes) <- "concurve"
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return(dataframes)
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} else if (table == FALSE) {
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return(list(df, densdf))
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}
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}
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} else if ((Sys.info()["sysname"]) == "Darwin") {
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curve_gen <- function(model, var, method = "lm", log = FALSE, penalty = NULL, m = NULL,
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steps = 1000, cores = getOption("mc.cores", 1L), table = TRUE) {
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if (is.character(method) != TRUE) {
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results <- pbmclapply(sidak.adj, FUN = function(i) confint.default(object = model, level = i)[var, ], mc.cores = cores)
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}
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}
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df <- data.frame(do.call(rbind, results))
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if (log == FALSE) {
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df <- (df)
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} else if (log == TRUE) {
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df <- exp(df)
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}
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intrvl.limit <- c("lower.limit", "upper.limit")
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colnames(df) <- intrvl.limit
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df$intrvl.width <- (abs((df$upper.limit) - (df$lower.limit)))
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}
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}
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}
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# RMD Check
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utils::globalVariables(c("df", "lower.limit", "upper.limit", "intrvl.width", "intrvl.level", "cdf", "pvalue", "svalue"))

‎README.md‎

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### <span style="color:#000; font-weight: 400;">`concurve`</span> \| Graph Frequentist Distributions of Parameters </strong> <img src="man/figures/logo.svg" align="right" width="70"/>
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# `concurve` \| Graph Frequentist Distributions of Parameters <img src="https://raw.githubusercontent.com/zadrafi/concurve/master/man/figures/logo.svg" align="right" width="70"/>
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------------------------------------------------------------------------
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@@ -16,8 +16,7 @@ Downloads](https://cranlogs.r-pkg.org/badges/grand-total/concurve)](https://cran
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[![Rdoc](http://www.rdocumentation.org/badges/version/concurve)](http://www.rdocumentation.org/packages/concurve)
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[![License: GPL
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v3](https://img.shields.io/badge/License-GPL%20v3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0)
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[![Travis build
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status](https://travis-ci.com/zadrafi/concurve.svg?branch=master)](https://travis-ci.com/zadrafi/concurve)
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[![R-CMD-check](https://github.com/zadrafi/concurve/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/zadrafi/concurve/actions/workflows/R-CMD-check.yaml)
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[![Codecov test
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coverage](https://codecov.io/gh/zadrafi/concurve/branch/master/graph/badge.svg)](https://codecov.io/gh/zadrafi/concurve?branch=master)
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<!-- badges: end -->
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### Citation
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To properly cite the package, please see the [following
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page](file:///Users/zad/Desktop/GitHub/concurve/docs/authors.html) or
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page](https://data.lesslikely.com/concurve/authors.html) or
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run the R script below.
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citation("concurve")
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Please note that the concurve project is released with a [Contributor
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Code of
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Conduct](https://data.lesslikely.com/concurve//CODE_OF_CONDUCT.html). By
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Conduct](https://data.lesslikely.com/concurve/CODE_OF_CONDUCT.html). By
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contributing to this project, you agree to abide by its terms.
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##### Environment
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The package was currently run on:
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## R version 4.0.2 (2020-06-22)
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## Platform: x86_64-apple-darwin17.0 (64-bit)
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## Running under: macOS Catalina 10.15.6
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##
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## Matrix products: default
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## BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
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## LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
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##
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## locale:
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## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
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##
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## attached base packages:
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## [1] stats graphics grDevices utils datasets methods base
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##
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## loaded via a namespace (and not attached):
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## [1] compiler_4.0.2 magrittr_1.5 credentials_1.3.0 htmltools_0.5.0 tools_4.0.2 yaml_2.2.1 stringi_1.5.3
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## [8] rmarkdown_2.4 knitr_1.30 stringr_1.4.0 xfun_0.18 digest_0.6.25 rlang_0.4.7 openssl_1.4.3
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## [15] sys_3.4 evaluate_0.14 askpass_1.1
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------------------------------------------------------------------------

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