update docs formatting

Author: kkbrum

R/best_split.R

@@ -14,7 +14,6 @@
 #' from `nc_list` and `nt_list` that violate this are skipped
 #'
 #' @return A list containing the following elements:
-#' \itemize{
 #'     \item{valuesIP, valuesLP: }{matrices containing integer and linear programming
 #' scaled objective values for each sample size tried, with rows corresponding to the
 #' elements of `nc_list` and columns corresponding to the elements of `nt_list`}
@@ -28,7 +27,6 @@
 #'     with rows corresponding to the strata and columns to the units}
 #'     \item{selection: }{vector of selected strata for each unit
 #'     in the initial stratum to be split for the best split}
-#' }
 #'
 #' @export
 #' @examples

R/rand_pvals.R

@@ -28,7 +28,7 @@
 #' and the proportion of randomized values more imbalanced than the observational value (the P-value).
 #'
 #' The `options` list argument can contain any of the following elements:
-#' \itemize{
+#' \describe{
 #'     \item{nrand: }{how many times to randomize the treatment assignment when forming the null distribution.
 #'     Default is 10000}
 #'     \item{criterion: }{which optimization criterion to use when calculating the objective value.
@@ -41,7 +41,6 @@
 #'     Default is `TRUE` if a base stratification is provided}
 #' }
 #' @return List with three components:
-#' \itemize{
 #'    \item{pvals: }{list containing `base` and `refined` elements, each of which is a list with randomization P-values
 #'    for the objective value (`NULL` for the base stratification),
 #'    the maximum standardized mean difference (SMD),
@@ -58,7 +57,6 @@
 #'    the maximum standardized mean difference (SMD),
 #'    and for the average SMD across strata for each covariate
 #'    (this element is a matrix with `nrand` rows and a column for each covariate)}
-#' }
 #' @export
 #' @import stats
 #'
@@ -202,7 +200,7 @@ rand_pvals <- function(object = NULL, z = NULL, X = NULL,
 #'
 #'
 #' @details The `options` list argument can contain any of the following elements:
-#' \itemize{
+#' \describe{
 #'     \item{nrand: }{how many times to randomize the treatment assignment when forming the null distribution.
 #'     Default is 10000}
 #'     \item{criterion: }{which optimization criterion to use when calculating the objective value.
@@ -213,7 +211,7 @@ rand_pvals <- function(object = NULL, z = NULL, X = NULL,
 #' Only used if criterion is set to "combo". Default is 5}
 #'     \item{incl_base: }{whether to include columns for the initial stratification in the table.
 #'     Default is `TRUE` if a base stratification is provided}
-#'     \item{rand_pvals: if already calculated, the returned list of information from \code{\link{rand_pvals}()}.
+#'     \item{rand_pvals: }{if already calculated, the returned list of information from \code{\link{rand_pvals}()}.
 #' If `NULL`, this will be calculated}
 #' }
 #' @return Matrix with 4 or 8 columns, depending whether one or both of base and

R/refine.R

@@ -20,7 +20,7 @@
 #'
 #' @details
 #' The `options` argument can contain any of the following elements:
-#' \itemize{
+#' \describe{
 #'     \item{solver: }{character specifying the optimization software to use.
 #'     Options are "Rglpk" or "gurobi". The default is "Rglpk" unless a gurobi installation is detected,
 #'     in which case it is set to "gurobi". It is recommended to use "gurobi" if available.}
@@ -47,13 +47,12 @@
 #'
 #'
 #' @return Object of class "strat", which is a list object with the following components:
-#'  \itemize{
 #'     \item{z: }{treatment vector}
 #'     \item{X: }{covariate matrix}
 #'     \item{base_strata: }{initial stratification}
 #'     \item{refined_strata: }{refined_stratification}
 #'     \item{details: }{various details about the optimization that can be ignored in practice, but may be interesting:
-#'     \itemize{
+#'     \describe{
 #'         \item{valueIP, valueLP: }{integer (determined via randomized rounding, unless `integer` option set to true)
 #'         and linear programming scaled objective values}
 #'         \item{n_fracs: }{number of units with fractional LP solutions}
@@ -67,7 +66,6 @@
 #'         \item{X_std: }{standardized version of `X`}
 #'         }
 #'     }
-#' }
 #'
 #'
 #' @export

R/split_stratum.R

@@ -20,7 +20,6 @@
 #' @param threads how many threads to use in the optimization if using "gurobi" as the solver. Default will use all available threads
 #'
 #' @return A list containing the following elements:
-#' \itemize{
 #'     \item{valueIP, valueLP: }{integer and linear programming scaled objective values}
 #'     \item{n_smds: }{number of standardized mean differences contributing to the objective values
 #' (multiply the scaled objective values by this number to get the true objective values)}
@@ -29,7 +28,6 @@
 #'     that were selected with randomness}
 #'     \item{pr: }{linear programming solution, with rows corresponding to the strata and columns to the units}
 #'     \item{selection: }{vector of selected strata for each unit in the initial stratum to be split}
-#' }
 #'
 #' @examples
 #' # Generate a small data set