passes local CHECKS

.Rbuildignore

@@ -5,3 +5,4 @@
 ^BSCweb$
 ^docs$
 ^scripts$
+^DESCRIPTION-dev$

DESCRIPTION

@@ -5,7 +5,7 @@ Authors@R:
     c(person("Nathan", "Green", , "[email protected]", role = c("aut"),
            comment = c(ORCID = "0000-0003-2745-1736")),
       person("Che", "Zhaojing", , "[email protected]", role = c("aut", "cre"),
-           comment = c(ORCID = "")))
+           comment = c(ORCID = "0000-0003-2245-1606")))
 Description: Create blended survival curves, see Che et al (2022) <doi:10.1177/0272989X221134545>.
 License: GPL (>= 3)
 Encoding: UTF-8
@@ -19,7 +19,6 @@ Imports:
     manipulate,
     sn,
     survHE,
-    survHEhmc,
     tibble
 Depends:
     R (>= 3.5.0)
@@ -29,8 +28,6 @@ Suggests:
     remotes,
     survival,
     testthat (>= 3.0.0)
-Remotes:
-    github::giabaio/survHEhmc
 VignetteBuilder: knitr
 Additional_repositories: https://inla.r-inla-download.org/R/stable/
 URL: https://github.com/StatisticsHealthEconomics/blendR/

DESCRIPTION-dev

@@ -0,0 +1,39 @@
+Package: blendR
+Title: Blended Survival Curves
+Version: 0.0.0.9000
+Authors@R: 
+    c(person("Nathan", "Green", , "[email protected]", role = c("aut"),
+           comment = c(ORCID = "0000-0003-2745-1736")),
+      person("Che", "Zhaojing", , "[email protected]", role = c("aut", "cre"),
+           comment = c(ORCID = "")))
+Description: Create blended survival curves, see Che et al (2022) <doi:10.1177/0272989X221134545>.
+License: GPL (>= 3)
+Encoding: UTF-8
+Roxygen: list(markdown = TRUE)
+RoxygenNote: 7.2.3.9000
+Imports:
+    dplyr,
+    flexsurv,
+    ggplot2,
+    INLA,
+    manipulate,
+    Matrix (>= 1.6.0),
+    sn,
+    survHE,
+    survHEhmc,
+    tibble
+Depends:
+    R (>= 3.5.0)
+Suggests: 
+    knitr,
+    rmarkdown,
+    remotes,
+    survival,
+    testthat (>= 3.0.0)
+Remotes:
+    github::giabaio/survHEhmc
+VignetteBuilder: knitr
+Additional_repositories: https://inla.r-inla-download.org/R/stable/
+URL: https://github.com/StatisticsHealthEconomics/blendR/
+BugReports: https://github.com/StatisticsHealthEconomics/blendR/issues/
+Config/testthat/edition: 3

NAMESPACE

@@ -10,9 +10,11 @@ export(ext_surv_sim)
 export(fit_inla_pw)
 export(make_surv)
 export(weightplot)
+import(dplyr)
 import(ggplot2)
 import(manipulate)
 import(sn)
+import(tibble)
 importFrom(INLA,inla.posterior.sample)
 importFrom(dplyr,contains)
 importFrom(dplyr,select)

R/ext_surv_sim.R

@@ -12,7 +12,7 @@
 #'
 #' @param t_info  A vector of times for which expert opinion is elicited
 #' @param S_info  A vector of mean survival probabilities estimated by experts
-#'                corresponding to time points in \code{t_info}
+#'                corresponding to time points in `t_info`
 #' @param T_max   The maximum survival time to be used
 #' @param n       The number of patients to construct the artificial external data set; default 70
 #' @importFrom stats runif rmultinom

R/fit_inla_pw.R

@@ -1,9 +1,9 @@
 
 #' Generate survival estimates with a piecewise exponential Cox model (using INLA)
 #'
-#' @param inla.formula The formula for PEM which must be an \code{inla.surv} object
-#' @param data A dataframe for survival data with time (\code{death_t}) and
-#'    event (\code{death})
+#' @param inla.formula The formula for PEM which must be an `inla.surv` object
+#' @param data A dataframe for survival data with time (`death_t`) and
+#'    event (`death`)
 #' @param cutpoints A sequence of cut points for intervals in the baseline hazard
 #' @param nsim The number of simulations from posteriors; default 100
 #' @param ... Additional arguments

R/make_surv.R

@@ -2,7 +2,7 @@
 #' @title Create survival probabilities
 #' @name make_surv_methods
 #'
-#' @description These function are version of the \code{make.surv} function
+#' @description These function are version of the [make.surv()] function
 #'    from \pkg{survHE}. These are needed prior to blending.
 #'
 #' @param Surv  survival analysis object

R/manip_plot.R

@@ -3,14 +3,15 @@
 #'
 #' RStudio bug
 #' need to run base R first
-#' manipulate(plot(1:x), x = slider(5, 10))
+#' `manipulate(plot(1:x), x = slider(5, 10))`
 #'
 #' @param obs_Surv Observed survival
 #' @param ext_Surv External survival
 #' @param blend_interv Blending interval
 #' @import manipulate
 #'
 manip_plot <- function(obs_Surv, ext_Surv, blend_interv) {
+  a <- b <- NULL
   manipulate::manipulate(
     {params <- list(obs_Surv = obs_Surv,
                     ext_Surv = ext_Surv,

R/plot.R

@@ -1,14 +1,14 @@
 
 #' Blended survival curve based on short-term data and external information
 #'
-#' @param x A blended survival curve object obtain from \code{blendsurv}
+#' @param x A blended survival curve object obtain from [blendsurv()]
 #' @param alpha A vector specifying the opacity of ribbon for the blended curve and other curves
 #' @param ... Additional arguments
 #' @import ggplot2
 #' @importFrom stats quantile
 #'
 #' @return \pkg{ggplot2} object
-#' @seealso \code{\link{blendsurv}}
+#' @seealso [blendsurv()]
 #' @method plot blended
 #' @export
 plot.blended <- function(x, alpha = c(0.1,0.05), ...) {
@@ -67,25 +67,25 @@ plot.blended <- function(x, alpha = c(0.1,0.05), ...) {
 
 #' Plots the weights for the blending procedure
 #'
-#' @param x A blended survival curve object obtain from \code{blendsurv}
+#' @param x A blended survival curve object obtained from [blendsurv()]
 #' @param ... Additional arguments
-#' @import ggplot2
+#' @import ggplot2 tibble dplyr
 #'
 #' @return \pkg{ggplot2} object
-#' @seealso \code{\link{blendsurv}}
+#' @seealso [blendsurv()]
 #' @importFrom stats pbeta
 #' @export
-
+#'
 weightplot <- function(x, ...) {
   tibble(
     t = x$times,
     t_scaled = (t - x$blend_interv$min)/(x$blend_interv$max - x$blend_interv$min),
-    y = stats::pbeta(t_scaled, x$beta_params$alpha, x$beta_params$beta)) |>
+    y = stats::pbeta(.data$t_scaled, x$beta_params$alpha, x$beta_params$beta)) |>
     mutate(
       y = case_when(t_scaled < 0 ~ 0,
                     t_scaled > 1 ~ 1,
                     TRUE ~ y)) |>
-    ggplot(aes(t,y)) +
+    ggplot(aes(.data$t, .data$y)) +
     geom_line() +
     theme_bw() +
     xlab("Time") + ylab("Weight function") +