Updating DESCRIPTION to add links to GitHub site

.Rbuildignore

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+^.*\.Rproj$
+^\.Rproj\.user$
+^build-history$
+^README\.Rmd$
+^README-.*\.png$

.gitignore

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+.Rproj.user
+.Rhistory
+.RData

DESCRIPTION

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+Package: singlearm
+Type: Package
+Title: Design and analysis of single-arm clinical trials
+Version: 1.0
+Authors@R: c(person(given = "Michael", family = "Grayling",
+                    email = "[email protected]",
+                    role = c("aut", "cre")))
+Description: This package provides a suite of functions for designing,
+    analysing, and visualising single-arm clinical trials.
+License: GPL-3
+Encoding: UTF-8
+LazyData: true
+Depends: R (>= 3.3.2)
+Imports: Rcpp (>= 0.12.14),
+           tibble,
+           dplyr,
+           ggplot2 (>= 2.2.1),
+           ggthemes,
+           iterpc
+Enhances: clinfun, OneArmPhaseTwoStudy
+LinkingTo: Rcpp
+Date: 2018-03-24
+URL: http://github.com/mjg211/singlearm
+BugReports: http://github.com/mjg211/singlearm/issues
+RoxygenNote: 6.0.1

NAMESPACE

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+# Generated by roxygen2: do not edit by hand
+
+S3method(plot,sa_ci_fixed)
+S3method(plot,sa_ci_gs)
+S3method(plot,sa_des_adaptive)
+S3method(plot,sa_des_admissable)
+S3method(plot,sa_des_bayesfreq)
+S3method(plot,sa_des_curtailed)
+S3method(plot,sa_des_fixed)
+S3method(plot,sa_des_gs)
+S3method(plot,sa_est_fixed)
+S3method(plot,sa_est_gs)
+S3method(plot,sa_opchar_adaptive)
+S3method(plot,sa_opchar_admissable)
+S3method(plot,sa_opchar_bayesfreq)
+S3method(plot,sa_opchar_curtailed)
+S3method(plot,sa_opchar_fixed)
+S3method(plot,sa_opchar_gs)
+S3method(plot,sa_pval_fixed)
+S3method(plot,sa_pval_gs)
+export(ci_fixed)
+export(ci_gs)
+export(des_adaptive)
+export(des_admissable)
+export(des_bayesfreq)
+export(des_curtailed)
+export(des_fixed)
+export(des_gehan)
+export(des_gs)
+export(est_fixed)
+export(est_gs)
+export(opchar_adaptive)
+export(opchar_admissable)
+export(opchar_bayesfreq)
+export(opchar_curtailed)
+export(opchar_fixed)
+export(opchar_gs)
+export(pval_fixed)
+export(pval_gs)
+importFrom(Rcpp,sourceCpp)
+useDynLib(singlearm)

NEWS

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+News: singlearm
+
+Version 1.0
+
+- Package launched with support for the design, analysis, and visualisation of
+single-stage and group sequential single-arm clinical trials with a single
+binary endpoint (des_fixed(), opchar_fixed(), est_fixed(), ci_fixed(),
+pval_fixed(), des_gs(), opchar_gs(), est_gs(), ci_gs(), pval_gs(),
+des_admissable(), opchar_admissable(), and their associated S3 plotting
+functions). Support is also available for the design and visualisation of
+adaptive two-stage, bayesian-frequentist, two-stage Gehan, and curtailed
+single-arm trials with a single binary endpoint (des_adaptive(),
+opchar_adaptive(), des_bayesfreq(), opchar_bayesfreq(), des_gehan(),
+des_curtailed(), opchar_curtailed(), and their associated S3
+plotting functions).

NEWS.md

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+# News: singlearm
+
+## Version 1.0
+
+- Package launched with support for the design, analysis, and visualisation of
+single-stage and group sequential single-arm clinical trials with a single
+binary endpoint (`des_fixed()`, `opchar_fixed()`, `est_fixed()`, `ci_fixed()`,
+`pval_fixed()`, `des_gs()`, `opchar_gs()`, `est_gs()`, `ci_gs()`, `pval_gs()`,
+`des_admissable()`, `opchar_admissable()`, and their associated S3 plotting
+functions). Support is also available for the design and visualisation of
+adaptive two-stage, bayesian-frequentist, two-stage Gehan, and curtailed
+single-arm trials with a single binary endpoint (`des_adaptive()`,
+`opchar_adaptive()`, `des_bayesfreq()`, `opchar_bayesfreq()`, `des_gehan()`, `des_curtailed()`, `opchar_curtailed()`, and their associated
+S3 plotting functions).

R/RcppExports.R

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+# Generated by using Rcpp::compileAttributes() -> do not edit by hand
+# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
+
+saBivariateBD <- function(J, alphaR, alphaT, beta, Nmin, Nmax, equal_n, dbivar_00_list, dbivar_01_list, dbivar_10_list, dbivar_11_min_list, dbivar_11_max_list, summary) {
+    .Call('_singlearm_saBivariateBD', PACKAGE = 'singlearm', J, alphaR, alphaT, beta, Nmin, Nmax, equal_n, dbivar_00_list, dbivar_01_list, dbivar_10_list, dbivar_11_min_list, dbivar_11_max_list, summary)
+}
+
+saBivariateCP <- function(J, alphaL, alphaG, beta, Nmin, Nmax, equal_n, dbivar_00_list, dbivar_01_list, dbivar_10_list, dbivar_11_list, dbivar_max1_list, dbivar_max2_list, summary) {
+    .Call('_singlearm_saBivariateCP', PACKAGE = 'singlearm', J, alphaL, alphaG, beta, Nmin, Nmax, equal_n, dbivar_00_list, dbivar_01_list, dbivar_10_list, dbivar_11_list, dbivar_max1_list, dbivar_max2_list, summary)
+}
+
+saGS <- function(J, pi0, pi1, alpha, beta, Nmin, Nmax, futility, efficacy, equal_n, ensign, summary) {
+    .Call('_singlearm_saGS', PACKAGE = 'singlearm', J, pi0, pi1, alpha, beta, Nmin, Nmax, futility, efficacy, equal_n, ensign, summary)
+}
+
+saVariable <- function(J, pi0, pi1, alpha, beta, v, Nmin, Nmax, equal_n) {
+    .Call('_singlearm_saVariable', PACKAGE = 'singlearm', J, pi0, pi1, alpha, beta, v, Nmin, Nmax, equal_n)
+}
+

R/ci_fixed.R

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+#' Determine confidence intervals in a single-stage single-arm trial design for
+#' a single binary endpoint
+#'
+#' Determines all possible confidence intervals at the end of a single-stage
+#' single-arm trial for a single binary endpoint, as determined
+#' using \code{des_fixed()}. Support is available to compute confidence
+#' intervals using the Agresti-Coull, Clopper-Pearson, Jeffreys, Mid-p, Wald,
+#' and Wilson Score approaches.
+#'
+#' In addition, the performance of the chosen confidence interval determination
+#' procedures (including their coverage and expected length) for each value of
+#' \ifelse{html}{\out{<i>pi</i>}}{\eqn{\pi}} in the supplied vector
+#' \ifelse{html}{\out{<b><i>pi</i></b>}}{\eqn{\bold{\pi}}}, will also be
+#' evaluated.
+#'
+#' @param des An object of class \code{"sa_des_fixed"}, as returned by
+#' \code{des_fixed()}.
+#' @param pi A vector of response probabilities to evaluate the expected
+#' performance of the confidence interval calculation procedure at. This will
+#' internally default to be the
+#' \ifelse{html}{\out{<i>&pi;</i><sub>0</sub>}}{\deqn{\pi_0}} and
+#' \ifelse{html}{\out{<i>&pi;</i><sub>1</sub>}}{\deqn{\pi_1}} from \code{des} if
+#' it is left unspecified.
+#' @param alpha \ifelse{html}{\out{<i>&alpha;</i>}}{\deqn{\alpha}}-level to use
+#' in confidence interval construction. Defaults to the value of
+#' \ifelse{html}{\out{<i>&alpha;</i>}}{\deqn{\alpha}} used in the construction
+#' of \code{des} (i.e., \code{des$alpha}).
+#' @param method A vector of methods to use to construct p-values. Currently,
+#' support is available to use the Agresti-Coull (\code{"agresti_coull"}),
+#' Clopper-Pearson (\code{"clopper_pearson"}), Jeffreys (\code{"jeffreys"}),
+#' Mid-p (\code{"mid_p"}), Wald (\code{"wald"}), and Wilson Score
+#' (\code{"wilson"}) approaches to confidence interval determination.
+#' @param summary A logical variable indicating whether a summary of the
+#' function's progress should be printed to the console.
+#' @return A list of class \code{"sa_ci_fixed"} containing the following
+#' elements
+#' \itemize{
+#' \item A tibble in the slot \code{$ci} summarising the possible confidence
+#' intervals at the end of the trial for the supplied design, according to the
+#' chosen methods.
+#' \item A tibble in the slot \code{$perf} summarising the performance of the
+#' chosen confidence interval determination procedures for each specified value
+#' of \ifelse{html}{\out{<i>&pi;</i>}}{\deqn{\pi}}.
+#' \item Each of the input variables as specified, subject to internal
+#' modification.
+#' }
+#' @examples
+#' # Find the optimal single-stage design for the default parameters
+#' des <- des_fixed()
+#' # Determine the performance of all supported confidence interval
+#' # determination procedures for a range of possible response probabilities
+#' ci  <- ci_fixed(des, pi = seq(0, 1, 0.01))
+#' @seealso \code{\link{des_fixed}}, \code{\link{opchar_fixed}},
+#' \code{\link{est_fixed}}, \code{\link{pval_fixed}}, and their associated
+#' \code{plot} family of functions.
+#' @export
+ci_fixed <- function(des, pi, alpha = des$alpha,
+                     method = c("agresti_coull", "clopper_pearson", "jeffreys",
+                                "mid_p", "wald", "wilson"), summary = F) {
+
+  ##### Input Checking #########################################################
+
+  check_sa_des_fixed(des, "des")
+  check_real_range_strict(alpha, "alpha", c(0, 1), 1)
+  if (!missing(pi)) {
+    check_pi(pi, "any")
+  } else {
+    pi <- c(des$des$pi0, des$des$pi1)
+  }
+  check_belong(method, "method", c("agresti_coull", "clopper_pearson",
+                                   "jeffreys", "mid_p", "wald", "wilson"),
+               "any")
+  check_logical(summary, "summary")
+
+  ##### Print Summary ##########################################################
+
+  if (summary){
+    message(rep("-", 10))
+    message("Confidence interval determination for single-stage single-arm trials with a single binary endpoint")
+    message(rep("-", 10))
+    Sys.sleep(2)
+    message("You have chosen to use the following methods to construct confidence intervals\n")
+    if ("aggresti_coull" %in% method) {
+      message("  • Aggresti-Coull.")
+    }
+    if ("clopper_pearson" %in% method) {
+      message("  • Clopper-Pearson.")
+    }
+    if ("jeffreys" %in% method) {
+      message("  • Jeffreys.")
+    }
+    if ("mid_p" %in% method) {
+      message("  • Mid-p.")
+    }
+    if ("wald" %in% method) {
+      message("  • Wald.")
+    }
+    if ("wilson" %in% method) {
+      message("  • Wilson.")
+    }
+    Sys.sleep(2)
+    message("\nNow beginning the required calculations...")
+  }
+
+  ##### Main Computations ######################################################
+
+  ci <- tibble::tibble(s = rep(0:des$des$n, each = length(method)),
+                       m = rep(des$des$n, (des$des$n + 1)*length(method)),
+                       method = factor(rep(method, des$des$n + 1),
+                                       levels = method), `clow(s,m)` = NA,
+                       `cupp(s,m)` = NA)
+  for (i in 1:nrow(ci)) {
+    ci[i, 4:5] <- switch(as.character(ci$method[i]),
+                         agresti_coull   = ci_fixed_agresti_coull(ci$s[i],
+                                                                  ci$m[i],
+                                                                  alpha),
+                         clopper_pearson = ci_fixed_clopper_pearson(ci$s[i],
+                                                                    ci$m[i],
+                                                                    alpha),
+                         jeffreys        = ci_fixed_jeffreys(ci$s[i], ci$m[i],
+                                                             alpha),
+                         mid_p           = ci_fixed_mid_p(ci$s[i], ci$m[i],
+                                                          alpha),
+                         wald            = ci_fixed_wald(ci$s[i], ci$m[i],
+                                                         alpha),
+                         wilson          = ci_fixed_wilson(ci$s[i], ci$m[i],
+                                                           alpha))
+    if (all(summary, i%%100 == 0)) {
+      message("...", i, " confidence intervals determined...")
+    }
+  }
+  ci     <- dplyr::mutate(ci, `l(s,m)` = `cupp(s,m)` - `clow(s,m)`)
+  len_pi <- length(pi)
+  pmf    <- pmf_fixed(pi, des$des$n)
+  perf   <- tibble::tibble(pi = rep(pi, length(method)),
+                           method = factor(rep(method, each = len_pi), method),
+                           `bar(L)` = NA, `max(L)` = NA, `E(L|pi)` = NA,
+                           `Var(L|pi)` = NA, `Cover(C|pi)` = 0)
+  for (i in 1:nrow(perf)) {
+    ci_i                    <- dplyr::filter(ci, method == perf$method[i])
+    pmf_i                   <- dplyr::filter(pmf, pi == perf$pi[i])
+    perf$`E(L|pi)`[i]       <- sum(pmf_i$`f(s,m|pi)`*ci_i$`l(s,m)`)
+    perf$`Var(L|pi)`[i]     <- sum(pmf_i$`f(s,m|pi)`*ci_i$`l(s,m)`^2) -
+                                 perf$`E(L|pi)`[i]^2
+    perf$`bar(L)`[i]        <- mean(ci_i$`l(s,m)`)
+    perf$`max(L)`[i]        <- max(ci_i$`l(s,m)`)
+    coverage                 <- dplyr::filter(ci_i, `clow(s,m)` <= perf$pi[i] &
+                                               `cupp(s,m)` >= perf$pi[i])
+    if (nrow(coverage) > 0) {
+      perf$`Cover(C|pi)`[i] <- sum(dplyr::filter(pmf_i,
+                                                 s %in% coverage$s)$`f(s,m|pi)`)
+    }
+    if (all(summary, i%%100 == 0)) {
+      message("...performance for ", i, " pi-method combinations evaluated...")
+    }
+  }
+
+  ##### Outputting #############################################################
+
+  if (summary) {
+    message("...outputting.")
+  }
+  output        <- list(ci = ci, perf = perf, des = des, pi = pi, alpha = alpha,
+                        summary = summary)
+  class(output) <- "sa_ci_fixed"
+  return(output)
+}