Fix dplyr::as_tibble and dplyr::tbl_df references to use tibble package

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: serodynamics
 Title: What the Package Does (One Line, Title Case)
-Version: 0.0.0.9047
+Version: 0.0.0.9048
 Authors@R: c(
     person("Peter", "Teunis", , "p.teunis@emory.edu", role = c("aut", "cph"),
            comment = "Author of the method and original code."),

NEWS.md

@@ -1,5 +1,6 @@
 # serodynamics (development version)
 
+* Fixed `dplyr::as_tibble()` references to `tibble::as_tibble()` in `post_summ()` and `run_mod()`, since `as_tibble()` is exported from the `tibble` package, not `dplyr`.
 * Added dev container configuration for persistent, cached development environment
   that includes R, JAGS, and all dependencies preinstalled, making Copilot
   Workspace sessions much faster.

R/Run_Mod.R

@@ -26,7 +26,7 @@
 #' should be included as an element of the [list] object returned by `run_mod()`
 #' (see `Value` section below for details).
 #' Note: These objects can be large.
-#' @returns An `sr_model` class object: a subclass of [dplyr::tbl_df] that
+#' @returns An `sr_model` class object: a subclass of [tibble::tbl_df] that
 #' contains MCMC samples from the joint posterior distribution of the model
 #' parameters, conditional on the provided input `data`, 
 #' including the following:
@@ -181,7 +181,7 @@ run_mod <- function(data,
   # jags output results for each stratification rbinded.
 
   # Making output a tibble and restructing.
-  jags_out <- dplyr::as_tibble(jags_out)  |>
+  jags_out <- tibble::as_tibble(jags_out)  |>
     select(!c("Parameter")) |>
     rename("Parameter" = "Parameter_sub")
   jags_out <- jags_out[, c("Iteration", "Chain", "Parameter", "Iso_type",

R/nepal_sees_jags_output.R

@@ -8,12 +8,12 @@
 #' which is the diagnosis type (typhoid or
 #' paratyphoid). Keeping only IDs `"newperson"`, `"sees_npl_1"`, `"sees_npl_2"`.
 #'
-#' @format An S3 object of class `sr_model`: A [dplyr::tbl_df] that contains the
-#'   posterior predictive distribution of the person-specific parameters for a
-#'   "new person" with no observed data (`Subject = "newperson"`) and posterior
-#'   distributions of the person-specific parameters for two arbitrarily-chosen
-#'   subjects (`"sees_npl_1"` and `"sees_npl_2"`).
-#'   Contains 40,000 `rows`, 7 `columns`, and model `attributes`.
+#' @format An S3 object of class `sr_model`: A [tibble::tbl_df] that contains 
+#' the posterior predictive distribution of the person-specific parameters for 
+#' a "new person" with no observed data (`Subject = "newperson"`) and posterior
+#' distributions of the person-specific parameters for two arbitrarily-chosen
+#' subjects (`"sees_npl_1"` and `"sees_npl_2"`).
+#' Contains 40,000 `rows`, 7 `columns`, and model `attributes`.
 #' \describe{
 #'  \item{Iteration}{Number of sampling iterations: 500 iterations}
 #'  \item{Chain}{Number of MCMC chains run: 2 chains run}

R/plot_predicted_curve.R

@@ -12,7 +12,7 @@
 #' @param ids The participant IDs to plot; for example, `"sees_npl_128"`.
 #' @param antigen_iso  The antigen isotype to plot; for example, "HlyE_IgA" or 
 #' "HlyE_IgG".
-#' @param dataset (Optional) A [dplyr::tbl_df] with observed antibody response 
+#' @param dataset (Optional) A [tibble::tbl_df] with observed antibody response 
 #' data. 
 #' Must contain:
 #'   - `timeindays`

R/post_summ.R

@@ -55,5 +55,5 @@ post_summ <- function(data,
                      `50.0%` = quantile(.data$value, 0.50), 
                      `75.0%` = quantile(.data$value, 0.75), 
                      `97.5%` = quantile(.data$value, 0.975))
-  dplyr::as_tibble(summarize_jags)
+  tibble::as_tibble(summarize_jags)
 }

R/use_att_names.R

@@ -2,7 +2,7 @@
 #' @description
 #'  `use_att_names` takes prepared longitudinal data for antibody kinetic
 #'  modeling and names columns using attribute values to allow merging
-#'  with a modeled [run_mod()] output [dplyr::tbl_df]. The column names include
+#'  with a modeled [run_mod()] output [tibble::tbl_df]. The column names include
 #'  `Subject`, `Iso_type`, `t`, and `result`. 
 #' @param data A [data.frame] raw longitudinal data that has been
 #' prepared for antibody kinetic modeling using [as_case_data()].