Updating run_mod and prep_priors as reviewed

Author: sschildhauer

R/Run_Mod.R

@@ -47,7 +47,7 @@
 #'   - `nIterations`: Number of iteration specified.
 #'   - `nBurnin`: Number of burn ins.
 #'   - `nThin`: Thinning number (niter/nmc)
-#'   - A [list] of `priors` that summarize the input priors, including:
+#'   - `priors`: A [list] that summarizes the input priors, including:
 #'     - `mu_hyp_param`
 #'     - `prec_hyp_param`
 #'     - `omega_param`

R/prep_priors.R

@@ -41,11 +41,12 @@
 #'
 #' @returns A "curve_params_priors" object 
 #' (a subclass of [list] with the inputs to `prep_priors()` attached 
-#' as [attributes]  named `"used_priors"`).
+#' as [attributes] entry named `"used_priors"`), containing the following
+#' elements:
 #' - "n_params": Corresponds to the 5 parameters being estimated.
 #' - "mu.hyp": A [matrix] of hyperpriors with dimensions
 #' `max_antigens` x 5 (# of parameters), representing the mean of the
-#' hyperprior distribution for each biomarker: y0, y1, t1, r, and alpha).
+#' hyperprior distribution for the five seroresponse parameters: y0, y1, t1, r, and alpha).
 #' - "prec.hyp": A three-dimensional [numeric] [array] 
 #' with dimensions `max_antigens` x 5 (# of parameters), 
 #' containing the precision matrices of the hyperprior distributions of
@@ -111,7 +112,6 @@ prep_priors <- function(max_antigens,
   }
 
   # Return results as a list
-
   prepped_priors <- list(
     "n_params" = n_params,
     "mu.hyp" = mu_hyp,
@@ -121,9 +121,7 @@ prep_priors <- function(max_antigens,
     "prec.logy.hyp" = prec_logy_hyp
   ) |>
     structure(
-      class = c("curve_params_priors", "list"),
-      "used_priors" = as.list(environment())
-    )
+      class = c("curve_params_priors", "list"))
   # Creating two objects in a list, one will be used in run_mod and the other
   # will be attached to run_mod output as an attribute. 
   prepped_priors <- prepped_priors |>