renamed the package (again!) from ModStanR to outstandR

DESCRIPTION

@@ -1,4 +1,4 @@
-Package: ModStanR
+Package: outstandR
 Title: Model-based Standardisation
 Version: 0.0.0.9000
 Authors@R: c(
@@ -39,5 +39,5 @@ Suggests:
 VignetteBuilder: knitr
 Config/testthat/edition: 3
 RdMacros: Rdpack
-URL: https://github.com/StatisticsHealthEconomics/ModStanR/
-BugReports: https://github.com/StatisticsHealthEconomics/ModStanR/issues/
+URL: https://github.com/StatisticsHealthEconomics/outstandR/
+BugReports: https://github.com/StatisticsHealthEconomics/outstandR/issues/

NAMESPACE

@@ -7,10 +7,10 @@ S3method(IPD_stats,maic)
 S3method(IPD_stats,stc)
 export(ALD_stats)
 export(IPD_stats)
-export(ModStanR)
 export(marginal_treatment_effect)
 export(marginal_variance)
 export(new_strategy)
+export(outstandR)
 export(strategy_gcomp_ml)
 export(strategy_gcomp_stan)
 export(strategy_maic)

NEWS.md

@@ -1,3 +1,3 @@
-# ModStanR (development version)
+# outstandR (development version)
 
 * Initial CRAN submission.

R/ModStanR.R

@@ -2,24 +2,24 @@
 #' @title Calculate the difference between treatments using all evidence
 #' 
 #' @description
-#' This is the main, top-level wrapper for `{ModStanR}`.
+#' This is the main, top-level wrapper for `{outstandR}`.
 #' Methods taken from
-#' \insertCite{RemiroAzocar2022}{ModStanR}.
+#' \insertCite{RemiroAzocar2022}{outstandR}.
 #' 
 #' @param AC.IPD Individual-level patient data. Suppose between studies _A_ and _C_.
 #' @param BC.ALD Aggregate-level data. Suppose between studies _B_ and _C_. 
 #' @param strategy Computation strategy function. These can be
 #'    `strategy_maic()`, `strategy_stc()`, `strategy_gcomp_ml()` and  `strategy_gcomp_stan()`
 #' @param CI Confidence interval; between 0,1
 #' @param ... Additional arguments
-#' @return List of length 3 of statistics as a `ModStanR` class object.
+#' @return List of length 3 of statistics as a `outstandR` class object.
 #'   Containing statistics between each pair of treatments.
 #'   These are the mean contrasts, variances and confidence intervals,
 #'   respectively.
 #' @importFrom Rdpack reprompt
 #' 
 #' @references
-#' \insertRef{RemiroAzocar2022}{ModStanR}
+#' \insertRef{RemiroAzocar2022}{outstandR}
 #' 
 #' @export
 #' @examples
@@ -29,18 +29,18 @@
 #' lin_form <- as.formula("y ~ X3 + X4 + trt*X1 + trt*X2")
 #' 
 #' # matching-adjusted indirect comparison
-#' ModStanR_maic <- ModStanR(AC_IPD, BC_ALD, strategy = strategy_maic(formula = lin_form))
+#' outstandR_maic <- outstandR(AC_IPD, BC_ALD, strategy = strategy_maic(formula = lin_form))
 #' 
 #' # simulated treatment comparison
-#' ModStanR_stc <- ModStanR(AC_IPD, BC_ALD, strategy = strategy_stc(lin_form))
+#' outstandR_stc <- outstandR(AC_IPD, BC_ALD, strategy = strategy_stc(lin_form))
 #' 
 #' # G-computation with maximum likelihood
-#' # ModStanR_gcomp_ml <- ModStanR(AC_IPD, BC_ALD, strategy = strategy_gcomp_ml(lin_form))
+#' # outstandR_gcomp_ml <- outstandR(AC_IPD, BC_ALD, strategy = strategy_gcomp_ml(lin_form))
 #' 
 #' # G-computation with Bayesian inference
-#' ModStanR_gcomp_stan <- ModStanR(AC_IPD, BC_ALD, strategy = strategy_gcomp_stan(lin_form))
+#' outstandR_gcomp_stan <- outstandR(AC_IPD, BC_ALD, strategy = strategy_gcomp_stan(lin_form))
 #' 
-ModStanR <- function(AC.IPD, BC.ALD, strategy, CI = 0.95, ...) {
+outstandR <- function(AC.IPD, BC.ALD, strategy, CI = 0.95, ...) {
 
   if (CI <= 0 || CI >= 1) stop("CI argument must be between 0 and 1.")
   ##TODO: as method instead?
@@ -67,21 +67,21 @@ ModStanR <- function(AC.IPD, BC.ALD, strategy, CI = 0.95, ...) {
 
   ald <- BC.ALD[keep_names]
 
-  AC_ModStanR <- IPD_stats(strategy, ipd = ipd, ald = ald, ...) 
-  BC_ModStanR <- ALD_stats(ald = ald) 
+  AC_outstandR <- IPD_stats(strategy, ipd = ipd, ald = ald, ...) 
+  BC_outstandR <- ALD_stats(ald = ald) 
 
   upper <- 0.5 + CI/2
   ci_range <- c(1-upper, upper)
 
   contrasts <- list(
-    AB = AC_ModStanR$mean - BC_ModStanR$mean,
-    AC = AC_ModStanR$mean,
-    BC = BC_ModStanR$mean)
+    AB = AC_outstandR$mean - BC_outstandR$mean,
+    AC = AC_outstandR$mean,
+    BC = BC_outstandR$mean)
 
   contrast_variances <- list(
-    AB = AC_ModStanR$var + BC_ModStanR$var,
-    AC = AC_ModStanR$var,
-    BC = BC_ModStanR$var)
+    AB = AC_outstandR$var + BC_outstandR$var,
+    AC = AC_outstandR$var,
+    BC = BC_outstandR$var)
 
   contrast_ci <- list(
     AB = contrasts$AB + qnorm(ci_range)*as.vector(sqrt(contrast_variances$AB)),
@@ -94,5 +94,5 @@ ModStanR <- function(AC.IPD, BC.ALD, strategy, CI = 0.95, ...) {
 
   structure(stats,
             CI = CI,
-            class = c("ModStanR", class(stats)))
+            class = c("outstandR", class(stats)))
 }

R/data.R

@@ -5,7 +5,7 @@
 #' 
 #' @name AC_IPD
 #' @docType data
-#' @format A data list including the variables needed for the ModStanR basic example. The variables are as follows:
+#' @format A data list including the variables needed for the outstandR basic example. The variables are as follows:
 #' \describe{
 #' \item{list("X1")}{Numeric covariate}
 #' \item{list("X2")}{Numeric covariate}
@@ -25,7 +25,7 @@ NULL
 #' 
 #' @name BC_ALD
 #' @docType data
-#' @format A data list including the variables needed for the ModStanR basic example. The variables are as follows:
+#' @format A data list including the variables needed for the outstandR basic example. The variables are as follows:
 #' \describe{
 #' \item{list("mean.X1")}{Numeric value}
 #' \item{list("mean.X2")}{Numeric value}

R/maic.R

@@ -3,13 +3,13 @@
 #' 
 #' Matching-adjusted indirect comparison weights.
 #' Method is taken from
-#' \insertCite{Signorovitch2010}{ModStanR}.
+#' \insertCite{Signorovitch2010}{outstandR}.
 #' 
 #' @param X_EM Centred \eqn{S=1} effect modifiers; matrix or data frame
 #' @return Estimated weights for each individual; vector
 #' 
 #' @references
-#' \insertRef{Signorovitch2010}{ModStanR}
+#' \insertRef{Signorovitch2010}{outstandR}
 #' @keywords internal
 #' 
 maic_weights <- function(X_EM) {

R/performance_measures.R

@@ -149,15 +149,15 @@ var.ratio <- function(theta.hat, std.err) {
 #' Variability ratio MCSE
 #' 
 #' Approximation of ratio variance based on independence of avg. se and emp.se
-#' see \insertCite{wolter2007}{ModStanR}
+#' see \insertCite{wolter2007}{outstandR}
 #'
 #' @param avg.se Average SE
 #' @param emp.se Emp SE
 #' @param var.avg.se Variance of average SE
 #' @param var.emp.se Variance of Emp SE
 #' 
 #' @references
-#' \insertRef{wolter2007}{ModStanR}
+#' \insertRef{wolter2007}{outstandR}
 #' 
 #' @keywords internal
 #' 

R/print.R

@@ -1,6 +1,6 @@
 
 #'
-print.ModStanR <- function(x, newline = TRUE) {
+print.outstandR <- function(x, newline = TRUE) {
 
   cat("\nContrasts:", x$contrasts, "\n")
   cat("\nVariances:", x$contrasts_variances, "\n")

R/summary.R

@@ -1,8 +1,8 @@
 
 #
-summary.ModStanR <- function(object, ...) {
+summary.outstandR <- function(object, ...) {
 
   ans <- NULL
-  class(ans) <- "summary.ModStanR"
+  class(ans) <- "summary.outstandR"
   ans
 } 

README.md

@@ -3,7 +3,7 @@
 
 <!-- badges: start -->
 
-[![R-CMD-check](https://github.com/StatisticsHealthEconomics/ModStanR/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/StatisticsHealthEconomics/ModStanR/actions/workflows/R-CMD-check.yaml)
+[![R-CMD-check](https://github.com/StatisticsHealthEconomics/outstandR/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/StatisticsHealthEconomics/outstandR/actions/workflows/R-CMD-check.yaml)
 [![Lifecycle: experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://lifecycle.r-lib.org/articles/stages.html#experimental)
 <!-- badges: end -->
 
@@ -24,7 +24,7 @@ and
 Install the [development version from GitHub](https://github.com/StatisticsHealthEconomics/) using `remotes`:
 
 ```r
-remotes::install_github("StatisticsHealthEconomics/ModStanR")
+remotes::install_github("StatisticsHealthEconomics/outstandR")
 ```
 
 ## License
@@ -33,6 +33,6 @@ remotes::install_github("StatisticsHealthEconomics/ModStanR")
 ## Contributing
 Please submit contributions through `Pull Requests`, following the [contributing guidelines](https://github.com/n8thangreen/BCEA/blob/dev/CONTRIBUTING.md).
 To report issues and/or seek support, please file a new ticket in the
-[issue](https://github.com/StatisticsHealthEconomics/ModStanR/issues) tracker.
+[issue](https://github.com/StatisticsHealthEconomics/outstandR/issues) tracker.
 
 Please note that this project is released with a [Contributor Code of Conduct](https://github.com/n8thangreen/BCEA/blob/dev/CONDUCT.md). By participating in this project you agree to abide by its terms.

_pkgdown.yml

@@ -1,4 +1,4 @@
-url: https://StatisticsHealthEconomics.github.io/ModStanR/
+url: https://StatisticsHealthEconomics.github.io/outstandR/
 
 news:
   cran_dates: false
@@ -18,8 +18,8 @@ reference:
 - title: "Main"
   desc: "Main analysis functions."
   contents:
-  # - ModStanR-package
-  - starts_with("ModStanR")
+  # - outstandR-package
+  - starts_with("outstandR")
   - strategy
 - title: "Constituent statistics"
   desc: "Helper functions used by the main functions."
@@ -34,4 +34,4 @@ reference:
   contents:
   - AC_IPD
   - BC_ALD
-
+

doc/Basic_example.R

@@ -8,7 +8,7 @@ knitr::opts_chunk$set(
 library(boot)      # non-parametric bootstrap in MAIC and ML G-computation
 library(copula)    # simulating BC covariates from Gaussian copula
 library(rstanarm)  # fit outcome regression, draw outcomes in Bayesian G-computation
-library(ModStanR)
+library(outstandR)
 
 ## ----load-data----------------------------------------------------------------
 set.seed(555)
@@ -25,20 +25,20 @@ BC.ALD
 ## -----------------------------------------------------------------------------
 lin_form <- as.formula("y ~ X3 + X4 + trt*X1 + trt*X2")
 
-## ----ModStanR_maic------------------------------------------------------------
-ModStanR_maic <- ModStanR(AC.IPD, BC.ALD, strategy = strategy_maic(formula = lin_form))
+## ----outstandR_maic------------------------------------------------------------
+outstandR_maic <- outstandR(AC.IPD, BC.ALD, strategy = strategy_maic(formula = lin_form))
 
-## ----ModStanR_maic-print------------------------------------------------------
-ModStanR_maic
+## ----outstandR_maic-print------------------------------------------------------
+outstandR_maic
 
-## ----ModStanR_stc-------------------------------------------------------------
-ModStanR_stc <- ModStanR(AC.IPD, BC.ALD, strategy = strategy_stc(formula = lin_form))
-ModStanR_stc
+## ----outstandR_stc-------------------------------------------------------------
+outstandR_stc <- outstandR(AC.IPD, BC.ALD, strategy = strategy_stc(formula = lin_form))
+outstandR_stc
 
-## ----ModStanR_gcomp_ml--------------------------------------------------------
-# ModStanR_gcomp_ml <- ModStanR(AC.IPD, BC.ALD, strategy = strategy_gcomp_ml(formula = lin_form))
+## ----outstandR_gcomp_ml--------------------------------------------------------
+# outstandR_gcomp_ml <- outstandR(AC.IPD, BC.ALD, strategy = strategy_gcomp_ml(formula = lin_form))
 
-## ----ModStanR_gcomp_stan------------------------------------------------------
-ModStanR_gcomp_stan <- ModStanR(AC.IPD, BC.ALD, strategy = strategy_gcomp_stan(formula = lin_form))
-ModStanR_gcomp_stan
+## ----outstandR_gcomp_stan------------------------------------------------------
+outstandR_gcomp_stan <- outstandR(AC.IPD, BC.ALD, strategy = strategy_gcomp_stan(formula = lin_form))
+outstandR_gcomp_stan
 

doc/Basic_example.Rmd

@@ -41,7 +41,7 @@ First, let us load necessary packages.
 library(boot)      # non-parametric bootstrap in MAIC and ML G-computation
 library(copula)    # simulating BC covariates from Gaussian copula
 library(rstanarm)  # fit outcome regression, draw outcomes in Bayesian G-computation
-library(ModStanR)
+library(outstandR)
 ```
 
 ### Data
@@ -110,10 +110,10 @@ where $\bar{C}$ is the compliment of $C$ so e.g. $n_{\bar{C}} = N_C - n_c$.
 
 ## Model fitting in R
 
-The `{ModStanR}` package has been written to be easy to use and essential consists of a single function, `ModStanR()`.
-This can be used to run all of the different types of model, which we will call _strategies_. The first two arguments of `ModStanR()` are the individual and aggregate-level data, respectively.
+The `{outstandR}` package has been written to be easy to use and essential consists of a single function, `outstandR()`.
+This can be used to run all of the different types of model, which we will call _strategies_. The first two arguments of `outstandR()` are the individual and aggregate-level data, respectively.
 
-A `strategy` argument of `ModStanR` takes functions called `strategy_*()`, where the wildcard `*` is replaced by the name of the particular method required, e.g. `strategy_maic()` for MAIC. Each specific example is provided below.
+A `strategy` argument of `outstandR` takes functions called `strategy_*()`, where the wildcard `*` is replaced by the name of the particular method required, e.g. `strategy_maic()` for MAIC. Each specific example is provided below.
 
 
 ### MAIC
@@ -133,20 +133,20 @@ lin_form <- as.formula("y ~ X3 + X4 + trt*X1 + trt*X2")
 ```
 
 
-```{r ModStanR_maic}
-ModStanR_maic <- ModStanR(AC.IPD, BC.ALD, strategy = strategy_maic(formula = lin_form))
+```{r outstandR_maic}
+outstandR_maic <- outstandR(AC.IPD, BC.ALD, strategy = strategy_maic(formula = lin_form))
 ```
 
-The returned object is of class `ModStanR`.
+The returned object is of class `outstandR`.
 
 <!-- Wald-type normal distribution-based 95\% confidence interval are also returned. -->
 
-```{r ModStanR_maic-print}
-ModStanR_maic
+```{r outstandR_maic-print}
+outstandR_maic
 ```
 
 We see that this is a list object with 3 parts, each containing statistics between each pair of treatments. These are the mean contrasts, variances and confidence intervals (CI), respectively.
-The default CI is for 95% but can be altered in `ModStanR` with the `CI` argument.
+The default CI is for 95% but can be altered in `outstandR` with the `CI` argument.
 
 
 ### STC
@@ -166,11 +166,11 @@ As already mentioned, running the STC analysis is almost identical to the previo
 $$
 y = X_3 + X_4 + \beta_t(X_1 - \bar{X_1}) + \beta_t(X_2 - \bar{X_2})
 $$
-However, `ModStanR()` knows how to handle this so we can simply pass the same (uncentred) formula as before.
+However, `outstandR()` knows how to handle this so we can simply pass the same (uncentred) formula as before.
 
-```{r ModStanR_stc}
-ModStanR_stc <- ModStanR(AC.IPD, BC.ALD, strategy = strategy_stc(formula = lin_form))
-ModStanR_stc
+```{r outstandR_stc}
+outstandR_stc <- outstandR(AC.IPD, BC.ALD, strategy = strategy_stc(formula = lin_form))
+outstandR_stc
 ```
 
 For the last two approaches, we perform G-computation firstly with a frequentist MLE approach and then a Bayesian approach.
@@ -199,8 +199,8 @@ $$
 $$
 
 
-```{r ModStanR_gcomp_ml}
-# ModStanR_gcomp_ml <- ModStanR(AC.IPD, BC.ALD, strategy = strategy_gcomp_ml(formula = lin_form))
+```{r outstandR_gcomp_ml}
+# outstandR_gcomp_ml <- outstandR(AC.IPD, BC.ALD, strategy = strategy_gcomp_ml(formula = lin_form))
 ```
 
 
@@ -223,8 +223,8 @@ The average, variance and interval estimates of the marginal treatment effect ca
 
 We can draw a vector of size $N^*$ of predicted outcomes $y^*_z$ under each set intervention $z^*$ from its posterior predictive distribution under the specific treatment.
 
-```{r ModStanR_gcomp_stan}
-ModStanR_gcomp_stan <- ModStanR(AC.IPD, BC.ALD, strategy = strategy_gcomp_stan(formula = lin_form))
-ModStanR_gcomp_stan
+```{r outstandR_gcomp_stan}
+outstandR_gcomp_stan <- outstandR(AC.IPD, BC.ALD, strategy = strategy_gcomp_stan(formula = lin_form))
+outstandR_gcomp_stan
 ```