should have fixed R checks

DESCRIPTION

@@ -12,8 +12,8 @@ Authors@R: c(
             comment = c(ORCID = "0000-0002-7263-4251")))
 Description:
     Model-based standardisation with several different computation approaches.
-    See Remiro‐Azócar A, Heath A, Baio G (2022). Parametric G‐computation for compatible
-    indirect treatment comparisons with limited individual patient data. 
+    See Remiro‐Azócar A, Heath A, Baio G (2022. Parametric G‐computation for compatible
+    indirect treatment comparisons with limited individual patient data, 
     Res. Synth. Methods, 1–31. ISSN 1759-2879, <doi:10.1002/jrsm.1565, 2108.12208>.
 License: GPL (>= 3)
 Encoding: UTF-8
@@ -22,6 +22,7 @@ RoxygenNote: 7.3.2
 Imports:
     boot,
     copula,
+    MASS,
     rstanarm,
     Rdpack (>= 0.7)
 Suggests:
@@ -31,7 +32,6 @@ Suggests:
     gridExtra,
     here,
     knitr,
-    MASS,
     parallel,
     purrr,
     rmarkdown,

R/gen_data.R

@@ -18,6 +18,7 @@
 #' 
 #' @examples
 #' 
+#' \dontrun{
 #' x <- gen_data(
 #'  N = 100,
 #'  b_trt = log(0.17),
@@ -31,16 +32,18 @@
 #'  allocation = 2/3) 
 #' 
 #' head(x)
-#' 
+#' }
 gen_data <- function(N, b_trt, b_X, b_EM, b_0,
                      meanX, sdX, event_rate, 
                      corX, allocation) {
   # 4 baseline covariates
-  rho <- matrix(corX, nrow=4, ncol=4) # set correlation matrix
-  diag(rho) <- rep(1, 4)
+  n_c <- 4
+
+  rho <- matrix(corX, nrow=n_c, ncol=n_c) # set correlation matrix
+  diag(rho) <- rep(1, n_c)
   N_active <- round(N*allocation)  # number of patients under active treatment
   N_control <- N - N_active        # number of patients under control
-  sd.vec <- rep(sdX, 4)            # vector of standard deviations
+  sd.vec <- rep(sdX, n_c)            # vector of standard deviations
 
   cov.mat <- cor2cov(rho, sd.vec)  # covariance matrix
 
@@ -49,21 +52,22 @@ gen_data <- function(N, b_trt, b_X, b_EM, b_0,
   X_active <- 
     as.data.frame(
       MASS::mvrnorm(n = N_active,
-                    mu = rep(meanX, 4),
+                    mu = rep(meanX, n_c),
                     Sigma = cov.mat))
 
   # patients under control treatment
   X_control <-
     as.data.frame(
       MASS::mvrnorm(n = N_control,
-                    mu = rep(meanX, 4),
+                    mu = rep(meanX, n_c),
                     Sigma = cov.mat))  
   # all patients
   X <- rbind(X_active, X_control)
   colnames(X) <- c("X1", "X2", "X3", "X4")
 
   # treatment assignment (1: active; 0: control)
-  trt <- c(rep(1,N_active), rep(0,N_control))
+  trt <- c(rep(1, N_active),
+           rep(0, N_control))
 
   # generate binary outcomes using logistic regression
   # linear predictor

R/print.R

@@ -1,14 +1,16 @@
 
 #' @export
 print.outstandR <- function(x, newline = TRUE) {
-
-  cat("\nContrasts:", x$contrasts, "\n")
-  cat("\nVariances:", x$contrasts_variances, "\n")
-  cat("\nConfidence intervals:", x$contrasts_ci, "\n")
-
-  if (newline) {
-    cat("\n")
-  }
-
-  invisible(x)
+  x
+
+  #TODO:  
+  # cat("\nContrasts:", x$contrasts, "\n")
+  # cat("\nVariances:", x$contrasts_variances, "\n")
+  # cat("\nConfidence intervals:", x$contrasts_ci, "\n")
+  # 
+  # if (newline) {
+  #   cat("\n")
+  # }
+  # 
+  # invisible(x)
 }

R/strategy_.R

@@ -22,7 +22,6 @@
 #' \eqn{t} is equal to the odds of being enrolled in the _AC_ trial vs the _AB_ trial.
 #' 
 #' @param R The number of resamples used for the non-parametric bootstrap
-#' @template args-ald
 #' @return `maic` class object
 #' 
 #' @importFrom utils modifyList

outstandR.Rproj

@@ -15,4 +15,4 @@ LaTeX: pdfLaTeX
 BuildType: Package
 PackageUseDevtools: Yes
 PackageInstallArgs: --no-multiarch --with-keep.source
-PackageRoxygenize: rd,collate,namespace,vignette
+PackageRoxygenize: rd,collate,namespace