fix missing package anchors in roxygen links

DESCRIPTION

@@ -56,4 +56,4 @@ Suggests:
     truncnorm
 VignetteBuilder: knitr
 Roxygen: list(markdown = TRUE)
-RoxygenNote: 7.2.3
+RoxygenNote: 7.3.1

R/ctstm.R

@@ -383,7 +383,7 @@ IndivCtstmTrans <- R6::R6Class(
 #' `transition_types` in [`IndivCtstmTrans`].
 #' @inheritParams create_CohortDtstmTrans
 #' @param ... Further arguments passed to `IndivCtstmTrans$new()` in [`IndivCtstmTrans`].
-#' @return Returns an [`R6Class`] object of class [`IndivCtstmTrans`].
+#' @return Returns an [`R6::R6Class`] object of class [`IndivCtstmTrans`].
 #' @template details-create_disease_model
 #' @seealso See [`IndivCtstmTrans`] and [`IndivCtstm`] for examples.
 #' @name create_IndivCtstmTrans

R/hesim_data.R

@@ -44,7 +44,7 @@ create_lines_dt <- function(strategy_list, strategy_ids = NULL){
 #' the states and transitions in a multi-state model suitable for use with [`hesim_data`]. 
 #' @param trans_mat A transition matrix in the format from the [`mstate`][mstate::mstate] package. 
 #' See [`IndivCtstmTrans`].
-#' @return Returns a [`data.table`] in tidy format with three columns:
+#' @return Returns a [`data.table::data.table`] in tidy format with three columns:
 #' \describe{
 #' \item{transition_id}{Health state transition ID.}
 #' \item{from}{The starting health state.}

R/input_mats.R

@@ -7,7 +7,7 @@
 #' (ii) [metadata][id_attributes()] used to index each matrix in `X`. 
 #' 
 #' Once created, an `input_mats` object can be converted 
-#' to a [`data.table`] with `as.data.table()`, which is a helpful way to check that 
+#' to a [`data.table::data.table`] with `as.data.table()`, which is a helpful way to check that 
 #' the object is as expected. The `print()` method summarizes the object and 
 #' prints it to the console. 
 #' 
@@ -18,7 +18,7 @@
 #' in a statistical model. May also be a list of lists of input matrices when a 
 #' list of separate models is fit (e.g., with [flexsurvreg_list()]).
 #' @param ... For `input_mats()`, arguments to pass to [id_attributes()]. For `print()`,
-#' arguments to pass to [print.data.table()].
+#' arguments to pass to [`data.table::print.data.table()`].
 #' 
 #' @details 
 #' `input_mats` objects are used with [`params`] objects to simulate

R/model-fits.R

@@ -82,15 +82,15 @@ multinom_list <- function(...){
 #' Partitioned survival regression object
 #'
 #' Create a partitioned survival regression object of class `partsurvfit`. The object contains a list
-#' of fitted survival models fit using either \code{\link{flexsurvreg}} or \code{\link{flexsurvspline}} (i.e.,
+#' of fitted survival models fit using either [`flexsurv::flexsurvreg`]
+#' or [`flexsurv::flexsurvspline`] (i.e.,
 #' an object of class \code{\link{flexsurvreg_list}}) and the data frame used to perform the fit of each model. 
 #' The same data frame must have been used for each fit.  
-#' @param object An object of class \code{\link{flexsurvreg_list}}.
-#' @param data The data frame used to fit each survival model in \code{object}.
-#' \code{\link{flexsurvreg}}.  
+#' @param object An object of class [`flexsurv::flexsurvreg_list`].
+#' @param data The data frame used to fit each survival model in `object`.
 #' @return Returns an object of class `partsurvfit`, which is a list containing two elements. 
-#' The first element, "models", contains the survival models passed to \code{object}, and the second 
-#' element, "data" contains the data frame passed to \code{data}.
+#' The first element, "models", contains the survival models passed to `object`, and the second 
+#' element, "data" contains the data frame passed to `data`.
 #' @examples 
 #' library("flexsurv")
 #' fit1 <- flexsurv::flexsurvreg(formula = Surv(endpoint1_time, endpoint1_status) ~ age, 

R/model_def.R

@@ -123,7 +123,7 @@ c.eval_rng <- function(...) {
 #' character string used to separate the terms.
 #' @param ... For the print method, arguments to pass to `summary.eval_rng()`. 
 #' 
-#' @return `summary.eval_rng()` returns a [`data.table`] with columns for
+#' @return `summary.eval_rng()` returns a [`data.table::data,table`] with columns for
 #' (i) the name of the parameter (`param`), (ii) the mean of the parameter
 #' samples (`mean`), (iii) the standard deviation of the parameter samples (`sd`),
 #' and (iv) quantiles of the parameter samples corresponding

R/params.R

@@ -22,7 +22,7 @@ NULL
 #' computed.
 #' @param ... Additional arguments affecting the summary. Currently unused. 
 #' 
-#' @return A [`data.table`] that always contains the following columns:
+#' @return A [`data.table::data.table`] that always contains the following columns:
 #' \describe{
 #' \item{term}{The regression term.}
 #' \item{mean}{The mean value of the regression term.}

R/plot.R

@@ -6,7 +6,7 @@ format_dollar <- function(x) {
 # Cost-effectiveness plane -----------------------------------------------------
 #' Plot cost-effectiveness plane
 #' 
-#' Plot a cost-effectiveness plane from the output of [`cea_pw()`] using [`ggplot2`]. 
+#' Plot a cost-effectiveness plane from the output of [`cea_pw()`] using [`ggplot2::ggplot`]. 
 #' Each point is a random draw of incremental costs (y-axis) and incremental QALYs (x-axis)
 #' from a probabilistic sensitivity analysis.
 #' @inheritParams set_labels
@@ -69,7 +69,7 @@ plot_ceplane <- function(x, k = 50000, labels = NULL) {
 #' Plot cost-effectiveness acceptability curve
 #' 
 #' Plot a cost-effectiveness curve from either the output of [`cea()`] or
-#' [`cea_pw()`] using [`ggplot2`]. The former compares all treatment strategies
+#' [`cea_pw()`] using [`ggplot2::ggplot`]. The former compares all treatment strategies
 #' simultaneously and uses the probabilistic sensitivity analysis (PSA) to compute
 #' the probability that each strategy is the most cost-effective at a given 
 #' willingness to pay value, while the latter uses the PSA to compute the probability
@@ -154,7 +154,7 @@ plot_ceac.cea <- function(x, labels = NULL, ...) {
 #' Plot cost-effectiveness acceptability frontier
 #' 
 #' Plot a cost-effectiveness acceptability frontier (CEAF) from the output of 
-#' [`cea`] using [`ggplot2`]. The CEAF plots the probability
+#' [`cea`] using [`ggplot2::ggplot`]. The CEAF plots the probability
 #' that the optimal treatment strategy (i.e., the strategy with the highest 
 #' expected net monetary benefit) is cost-effective. 
 #' @inheritParams set_labels
@@ -172,7 +172,7 @@ plot_ceaf <- function(x, labels = NULL) {
 #' Plot expected value of perfect information
 #' 
 #' Plot the expected value of perfect information (EVPI) from the output of 
-#' [`cea()`] using [`ggplot2`]. Intuitively, the EVPI provides an estimate of the 
+#' [`cea()`] using [`ggplot2::ggplot`]. Intuitively, the EVPI provides an estimate of the 
 #' amount that a decision maker would be willing to pay to collect additional data 
 #' and completely eliminate uncertainty.
 #' @inheritParams set_labels

R/tparams_transprobs.R

@@ -347,7 +347,7 @@ summarize_transprobs_dt <- function(x, probs, unflatten,
 #' @inheritParams summary.tpmatrix
 #' @param object A [`tparams_transprobs`] object.
 #' 
-#' @return If `unflatten = "FALSE"` (the default), then a [`data.table`]
+#' @return If `unflatten = "FALSE"` (the default), then a [`data.table::data.table`]
 #' is returned with columns for (i) the health state that is being transitioned
 #' from (`from`), (ii) the health state that is being transitioned to (`to`)
 #' (iii) the mean of each parameter across parameter samples (`mean`),

R/tpmatrix.R

@@ -339,7 +339,7 @@ tpmatrix <- function(..., complement = NULL, states = NULL,
 #' vectors. See "Value" below for additional details.
 #' @param ... Additional arguments affecting the summary. Currently unused.
 #' 
-#' @return If `unflatten = "FALSE"` (the default), then a [`data.table`]
+#' @return If `unflatten = "FALSE"` (the default), then a [`data.table::data.table`]
 #' is returned with columns for (i) the health state that is being transitioned
 #' from (`from`), (ii) the health state that is being transitioned to (`to`)
 #' (iii) the mean of each parameter across parameter samples (`mean`),