Initial CRAN release.

.Rbuildignore

@@ -1,14 +1,16 @@
 ^.*\.Rproj$
 RobinCar.Rproj
-*.tar.gz*
 docs/
 doc/
 ^\.Rproj\.user$
 ^LICENSE\.md$
 ^_pkgdown\.yml$
 ^docs$
 ^pkgdown$
-^doc$
 ^Meta$
 ^\.github$
 ^CITATION.cff
+^doc$
+^vignettes/articles$
+^CRAN-SUBMISSION$
+^cran-comments\.md$

.gitignore

@@ -8,3 +8,4 @@ RobinCar.Rproj
 inst/doc
 /Meta/
 ..Rcheck
+/doc/

DESCRIPTION

@@ -12,10 +12,10 @@ Authors@R: c(
     person("Faith", "Bian", role="aut")
     )
 Description: Performs robust estimation and inference when using covariate adjustment and/or covariate-adaptive randomization in randomized controlled trials.
-    Ting Ye, Jun Shao, Yanyao Yi, Qinyuan Zhao (2023) <doi:10.1080/01621459.2022.2049278>
-    Ting Ye, Marlena Bannick, Yanyao Yi, Jun Shao (2023) <doi:10.1080/24754269.2023.2205802>
-    Ting Ye, Jun Shao, Yanyao Yi (2023) <doi:10.1093/biomet/asad045>
-    Marlena Bannick, Jun Shao, Jingyi Liu, Yu Du, Yanyao Yi, Ting Ye (2024) <arXiv:2306.10213>
+    Ting Ye, Jun Shao, Yanyao Yi, Qinyuan Zhao (2023) <doi:10.1080/01621459.2022.2049278>.
+    Ting Ye, Marlena Bannick, Yanyao Yi, Jun Shao (2023) <doi:10.1080/24754269.2023.2205802>.
+    Ting Ye, Jun Shao, Yanyao Yi (2023) <doi:10.1093/biomet/asad045>.
+    Marlena Bannick, Jun Shao, Jingyi Liu, Yu Du, Yanyao Yi, Ting Ye (2024) <doi:10.48550/arXiv.2306.10213>.
 License: MIT + file LICENSE
 Encoding: UTF-8
 LazyData: true

R/robincar-SL.R

@@ -18,8 +18,8 @@
 #' @param covariate_to_include_strata Whether to include car_strata variables in covariate adjustment. Defaults to F for ANOVA and ANCOVA; defaults to T for ANHECOVA. User may override by passing in this argument.
 #' @param contrast_h An optional function to specify a desired contrast
 #' @param contrast_dh An optional jacobian function for the contrast (otherwise use numerical derivative)
-#' @param SL_libraries Vector of super-learner libraries to use for the covariate adjustment (see \link{SuperLearner::listWrappers})
-#' @param SL_learners Optional list of super-learner "learners" to use for the covariate adjustment (see \link{SuperLearner::create.Learner})
+#' @param SL_libraries Vector of super-learner libraries to use for the covariate adjustment (see \link[SuperLearner:listWrappers]{SuperLearner::listWrappers})
+#' @param SL_learners Optional list of super-learner "learners" to use for the covariate adjustment (see \link[SuperLearner:create.Learner]{SuperLearner::create.Learner())}
 #' @param k_split Number of splits to use in cross-fitting
 #' @param g_accuracy Level of accuracy to check prediction un-biasedness (in digits).
 #'
@@ -69,8 +69,8 @@
 #'
 #' sl.mod$result
 #'
-#' @returns See value of \link{RobinCar::robincar_glm}, but the working model for \eqn{\hat{\mu}(X_i)} is based on the \link{AIPW:AIPW} package that uses specified SuperLearner libraries and cross-fitting.
-#' Also, `mod` attribute is an object of class \link{AIPW::AIPW}.
+#' @returns See value of \link[RobinCar:robincar_glm]{RobinCar::robincar_glm}, but the working model for \eqn{\hat{\mu}(X_i)} is based on the \link{AIPW} package that uses specified SuperLearner libraries and cross-fitting.
+#' Also, `mod` attribute is an object of class \link[AIPW:AIPW]{AIPW::AIPW}.
 robincar_SL <- function(df,
                         treat_col, response_col, car_strata_cols=NULL, covariate_cols=NULL,
                         car_scheme="simple",
@@ -145,7 +145,7 @@ robincar_SL <- function(df,
 #' @inheritParams robincar_SL
 #' @export
 #'
-#' @returns See value of \link{RobinCar::robincar_SL}.
+#' @returns See value of \link[RobinCar:robincar_SL]{RobinCar::robincar_SL}.
 #' Attributes `mods` and `mu_as` are lists of `mod` and `mu_a` attributes, respectively, for each replicate of `robincar_SL` used in the median.
 robincar_SL_median <- function(n_times, seed, df,
                                treat_col, response_col,

R/robincar-calibrate.R

@@ -1,7 +1,7 @@
 #' Perform linear or joint calibration
 #'
 #' Uses linear or joint calibration to "calibrate" the estimates from a linear or GLM-type adjustment.
-#' Linear calibration fits a linear model with treatment and with the predicted \eqn{\hat{\mu}_a(X_i)} as constructed covariates;
+#' Linear calibration fits a linear model with treatment (and treatment-by-covariate interactions) and with the predicted \eqn{\hat{\bm \mu}(X_i) = (\hat{\mu}_1(X_i), \dots, \hat{\mu}_K(X_i))} as constructed covariates where \eqn{K} is the number of treatment groups;
 #' joint calibration also includes \eqn{Z_i} the strata variables as covariates.
 #'
 #' @param result A GLMModelResult
@@ -13,7 +13,7 @@
 #'              the original dataset that robincar_glm was called on.
 #' @export
 #'
-#' @returns A result object that has the same structure as \link{RobinCar::robincar_glm}, with the argument `result` included as "original" in the list.
+#' @returns A result object that has the same structure as \link[RobinCar:robincar_glm]{RobinCar::robincar_glm()}, with the argument `result` included as "original" in the list.
 robincar_calibrate <- function(result, joint=FALSE,
                                add_x=NULL){
 

R/robincar-contrast.R

@@ -97,7 +97,7 @@ contrast <- function(settings, treat, est, varcov){
 
 #' Estimate a treatment contrast
 #'
-#' Estimate a treatment contrast using the result of \link{RobinCar::robincar_linear}, \link{RobinCar::robincar_glm}, or \link{RobinCar::robincar_SL} using
+#' Estimate a treatment contrast using the result of \link[RobinCar:robincar_linear]{RobinCar::robincar_linear()}, \link[RobinCar:robincar_glm]{RobinCar::robincar_glm()}, or \link[RobinCar:robincar_SL]{RobinCar::robincar_SL()} using
 #' the delta method.
 #'
 #' @param result A LinModelResult or GLMModelResult
@@ -108,7 +108,7 @@ contrast <- function(settings, treat, est, varcov){
 #'
 #' @returns A contrast object which has the following attributes:
 #'
-#'  \item{result}{A \link{dplyr::tibble} with the label of the treatment contrast (e.g., 1 vs. 0), the estimate of the treatment contrast, estimated SE, and p-value based on a z-test with estimate and SE.}
+#'  \item{result}{A \link[dplyr:tibble]{dplyr::tibble()} with the label of the treatment contrast (e.g., 1 vs. 0), the estimate of the treatment contrast, estimated SE, and p-value based on a z-test with estimate and SE.}
 #'  \item{varcov}{The variance-covariance matrix for the treatment contrast estimates.}
 #'  \item{settings}{List of model settings used for the contrast.}
 #'

R/robincar-glm.R

@@ -25,7 +25,7 @@
 #' @param covariate_to_include_strata Whether to include car_strata variables in covariate adjustment. Defaults to F for homogeneous; defaults to T for heterogeneous. User may override by passing in this argument.
 #' @param contrast_h An optional function to specify a desired contrast
 #' @param contrast_dh An optional jacobian function for the contrast (otherwise use numerical derivative)
-#' @param g_family Family that would be supplied to \link{stats::glm}, e.g., binomial. If no link specified, will use default link, like behavior in glm.
+#' @param g_family Family that would be supplied to \link[stats:glm]{glm()}, e.g., binomial. If no link specified, will use default link, like behavior in glm.
 #'                 If you wish to use a negative binomial working model with an unknown dispersion parameter, then use `g_family="nb"`.
 #' @param g_accuracy Level of accuracy to check prediction un-biasedness.
 #' @param formula An optional formula to use for adjustment specified using as.formula("..."). This overrides car_strata_cols and covariate_cols.
@@ -34,16 +34,16 @@
 #'
 #' @returns If `contrast_h` argument is used, outputs a `main` and a `contrast` object. The `main` object has the following structure:
 #'
-#'  \item{result}{A \link{dplyr::tibble} with the treatment label, treatment mean estimate using AIPW, estimated SE, and p-value based on a z-test with estimate and SE.}
+#'  \item{result}{A \link[dplyr:tibble]{dplyr::tibble()} with the treatment label, treatment mean estimate using AIPW, estimated SE, and p-value based on a z-test with estimate and SE.}
 #'  \item{varcov}{The variance-covariance matrix for the treatment mean estimates.}
 #'  \item{settings}{List of model settings used in covariate adjustment.}
 #'  \item{original_df}{The original dataset provided by the user.}
-#'  \item{mod}{The fit from the \link{stats::glm} working model used for covariate adjustment.}
+#'  \item{mod}{The fit from the \link[stats:glm]{glm()} working model used for covariate adjustment.}
 #'  \item{mu_a}{Predicted potential outcomes for each treatment category (columns) and individual (rows). These are the \eqn{\hat{\mu}_a}}.
 #'  \item{g.estimate}{The G-computation estimate based only on \eqn{\frac{1}{n} \sum_{i=1}^{n} \hat{\mu}_a(X_i)}. This is equivalent to the AIPW estimate when a canonical link function is used.}
 #'  \item{data}{Attributes about the dataset.}
 #'
-#'  The `contrast` object has a structure that is documented in \link{RobinCar::robincar_contrast}.
+#'  The `contrast` object has a structure that is documented in \link[RobinCar:robincar_contrast]{RobinCar::robincar_contrast()}.
 robincar_glm <- function(df,
                          treat_col, response_col, car_strata_cols=NULL, covariate_cols=NULL,
                          car_scheme="simple", adj_method="heterogeneous", # vcovHC="HC0",
@@ -112,7 +112,7 @@ robincar_glm <- function(df,
 #' @param g_accuracy Level of accuracy to check prediction un-biasedness.
 #' @export
 #'
-#' @returns See value of \link{RobinCar::robincar_glm}. This function is a wrapper for \link{RobinCar::robincar_glm}.
+#' @returns See value of \link[RobinCar:robincar_glm]{RobinCar::robincar_glm()}. This function is a wrapper for \link[RobinCar:robincar_glm]{RobinCar::robincar_glm()}.
 robincar_glm2 <- function(df,
                           treat_col, response_col,
                           formula=NULL, car_strata_cols=NULL,

R/robincar-linear.R

@@ -15,7 +15,7 @@
 #' @param contrast_dh An optional jacobian function for the contrast (otherwise use numerical derivative)
 #' @export
 #'
-#' @returns See value of \link{RobinCar::robincar_glm}. This function is a wrapper for \link{RobinCar::robincar_glm}, using an identity link for the glm.
+#' @returns See value of \link[RobinCar:robincar_glm]{RobinCar::robincar_glm()}, this function is a wrapper using an identity link for the glm.
 robincar_linear <- function(df,
                             treat_col, response_col, car_strata_cols=NULL, covariate_cols=NULL,
                             car_scheme="simple", adj_method="ANOVA",
@@ -56,7 +56,7 @@ robincar_linear <- function(df,
 #' Estimate treatment-group-specific response means and (optionally)
 #' treatment group contrasts using a linear working model for continuous outcomes.
 #'
-#' Note the difference between \link{RobinCar::robincar_linear} and \link{RobinCar::robincar_linear2} with regards to their `covariate_cols` argument.
+#' Note the difference between \link[RobinCar:robincar_linear]{RobinCar::robincar_linear()} and \link[RobinCar:robincar_linear2]{RobinCar::robincar_linear2()} with regards to their `covariate_cols` argument.
 #'
 #' @param df A data.frame with the required columns
 #' @param treat_col Name of column in df with treatment variable
@@ -69,7 +69,7 @@ robincar_linear <- function(df,
 #' @param contrast_dh An optional jacobian function for the contrast (otherwise use numerical derivative)
 #' @export
 #'
-#' @returns See value of \link{RobinCar::robincar_linear}. This function is a wrapper for \link{RobinCar::robincar_linear}.
+#' @returns See value of \link[RobinCar:robincar_linear]{RobinCar::robincar_linear()}, this function is a wrapper.
 robincar_linear2 <- function(df,
                              treat_col, response_col, car_strata_cols=NULL, covariate_cols=NULL,
                              car_scheme="simple", adj_method="ANOVA",

R/robincar-tte.R

@@ -23,7 +23,7 @@
 #'
 #' @export
 #'
-#' @returns For adjustment method "CL" or "CSL", see value of \link{RobinCar::robincar_logrank}; for adjustment method "coxscore" see value of \link{RobinCar::robincar_coxscore}.
+#' @returns For adjustment method "CL" or "CSL", see value of \link[RobinCar:robincar_linear]{RobinCar::robincar_logrank()}; for adjustment method "coxscore" see value of \link[RobinCar:robincar_coxscore]{RobinCar::robincar_coxscore()}.
 robincar_tte <- function(df,
                          treat_col, response_col, event_col,
                          adj_method,

R/summary.R

@@ -5,7 +5,7 @@
 #' @export
 #'
 #' @returns Prints the treatment mean estimates (and variances) based on a linear working model,
-#' along with the settings used. See \link{RobinCar::robincar_linear}.
+#' along with the settings used. See \link[RobinCar:robincar_linear]{RobinCar::robincar_linear()}.
 print.LinModelResult <- function(x, ...){
   descript(x)
   cat("\n\n")
@@ -22,7 +22,7 @@ print.LinModelResult <- function(x, ...){
 #' @export
 #'
 #' @returns Prints the treatment mean estimates (and variances) based on a GLM working model,
-#' along with the settings used. See \link{RobinCar::robincar_glm}.
+#' along with the settings used. See \link[RobinCar:robincar_glm]{RobinCar::robincar_glm()}.
 print.GLMModelResult <- function(x, ...){
   descript(x)
   cat("\n\n")
@@ -39,7 +39,7 @@ print.GLMModelResult <- function(x, ...){
 #' @export
 #'
 #' @returns Prints estimates (and variances) of treatment contrasts based on a linear or GLM working model,
-#' along with the settings used. See \link{RobinCar::robincar_contrast}
+#' along with the settings used. See \link[RobinCar:robincar_contrast]{RobinCar::robincar_contrast()}
 print.ContrastResult <- function(x, ...){
   if("DIFF" %in% class(x$settings)){
     c_type <- "linear contrast"
@@ -68,7 +68,7 @@ print.ContrastResult <- function(x, ...){
 #'
 #' @returns Prints results of time-to-event covariate adjusted analyses including covariate-adjusted (stratified) logrank,
 #' robust Cox score, and covariate-adjusted hazard ratio. Prints summary statistics about number of observations and events, possibly by strata,
-#' and the test statistics and/or estimates, and p-values. See \link{RobinCar::robincar_tte} and \link{RobinCar::robincar_covhr}.
+#' and the test statistics and/or estimates, and p-values. See \link[RobinCar:robincar_tte]{RobinCar::robincar_tte()} and \link[RobinCar:robincar_covhr]{RobinCar::robincar_covhr()}.
 print.TTEResult <- function(x, ...){
 
   if(!is.null(x$data$covariate)){
@@ -193,7 +193,7 @@ print.TTEResult <- function(x, ...){
 #' @export
 #'
 #' @returns Prints the treatment mean estimates (and variances) based on a calibration on top of a
-#' GLM working model, along with the settings used. See \link{RobinCar::robincar_calibrate}.
+#' GLM working model, along with the settings used. See \link[RobinCar:robincar_calibrate]{RobinCar::robincar_calibrate()}.
 print.CalibrationResult <- function(x, ...){
   if(x$joint){
     type <- "Joint calibration"