documentation fixes

R/Estimate.R

@@ -34,12 +34,12 @@
 #' data as ignorable missing data that will be removed (list-wise deletion) before estimating a
 #' model.
 #' 
-#' @section Time-Varying Models
+#' @section Time-Varying Models:
 #' 
 #' To run a time-varying model, you need to include the name of a column with dates (or integers) that is passed 
 #' to the \code{time_id} option.
 #' 
-#' @section Continuous Outcomes
+#' @section Continuous Outcomes:
 #' 
 #' If the outcome is unbounded i.e. a continuous or an unbounded 
 #' discrete variable like Poisson, simply set \code{unbounded} to \code{TRUE}. You can ignore the
@@ -48,7 +48,7 @@
 #' missing values (\code{NA}) will be recoded to a specific value out of the range of the outcome to use
 #' for modeling the missingness.
 #' 
-#' @section Hierarchical Covariates
+#' @section Hierarchical Covariates:
 #' 
 #' Covariates can be fit on the person-level ideal point parameters as well as
 #' item discrimination parameters for either the inflated (missing) or non-inflated (observed) 
@@ -533,7 +533,7 @@ id_make <- function(score_data=NULL,
 #'   \item Ordered Beta (proportion/percentage) with missing-data inflation
 #' }
 #' 
-#' @section Time-Varying Inferece
+#' @section Time-Varying Inference:
 #' 
 #' In addition, each of these models can have time-varying ideal point (person) parameters if
 #' a column of dates is fed to the \code{\link{id_make}} function. If the option \code{vary_ideal_pts} is 
@@ -546,7 +546,7 @@ id_make <- function(score_data=NULL,
 #' Please see the package vignette and associated paper for more detail
 #' about these time-varying models.
 #' 
-#' @section Missing Data
+#' @section Missing Data:
 #' 
 #' The inflation model used to account for missing data assumes that missingness is a 
 #' function of the persons' (legislators')
@@ -566,7 +566,7 @@ id_make <- function(score_data=NULL,
 #' Models can be either fit on the person/legislator IDs or on group-level IDs (as specified to the 
 #' \code{id_make} function). If group-level parameters should be fit, set \code{use_groups} to \code{TRUE}.
 #' 
-#' @section Covariates
+#' @section Covariates:
 #' 
 #' Covariates are included in the model if they were specified as options to the 
 #' \code{\link{id_make}} function. The covariate plots can be accessed with 

R/Generics.R

@@ -764,7 +764,7 @@ setMethod(id_plot, signature(object='idealstan'),
 
 #' Generic Method for Extracting Posterior Samples
 #' 
-#' This generic will extract the full \code{\link[rstan]{stan}}} posterior samples from \code{idealstan} objects.
+#' This generic will extract the full \code{\link[rstan]{stan}} posterior samples from \code{idealstan} objects.
 #' 
 #' See the corresponding method definition for more information about what you can acccess with this generic.
 #' 

R/Helpers.R

@@ -3752,8 +3752,8 @@ return(as.vector(idx))
     return(all_time)
 }
 
-#' @noRd
 #' Function to add in time-varying covariates to person time-varying ideal points
+#' @noRd
 .add_person_cov <- function(all_time, 
                             obj,
                             legis_x,

R/idealstan.R

@@ -5,16 +5,15 @@
 #' See the README on
 #' \href{https://github.com/saudiwin/idealstan/blob/master/README.md}{GitHub}
 #'
-#' @docType package
-#' @name idealstan
+#' @keywords internal
 #' @aliases idealstan-package
 #' @import methods
 #' @references \enumerate{
-#'    \item Kubinec, Robert. Generalized Ideal Point Models for Time-Varying and Missing-Data Inference. Working Paper.
+#'    \item Kubinec, Robert. (2024). Generalized Ideal Point Models for Robust Measurement with Dirty Data in the Social Sciences. SocArchiv Preprint. doi.org:10.31219/osf.io/8j2bt
 #'    \item Clinton, J., Jackman, S., & Rivers, D. (2004). The Statistical Analysis of Roll Call Data. \emph{The American Political Science Review}, 98(2), 355-370. doi:10.1017/S0003055404001194
 #'    \item Bafumi, J., Gelman, A., Park, D., & Kaplan, N. (2005). Practical Issues in Implementing and Understanding Bayesian Ideal Point Estimation. \emph{Political Analysis}, 13(2), 171-187. doi:10.1093/pan/mpi010
 #' }
-NULL
+"_PACKAGE"
 
 ## quiets concerns of R CMD check with dplyr and NSE
 ## temporary fix, will use standard evaluation in future versions

R/rstan_generics.R

@@ -565,7 +565,7 @@ setMethod('id_post_pred',signature(object='idealstan'),function(object,draws=100
 #' 
 #' This function is a wrapper around \code{\link[bayesplot]{ppc_bars}},
 #' \code{\link[bayesplot]{ppc_dens_overlay}} and 
-#' \code{\link[bayesplot]{ppc_violin_grouped} that plots the posterior predictive distribution
+#' \code{\link[bayesplot]{ppc_violin_grouped}} that plots the posterior predictive distribution
 #' derived from \code{\link{id_post_pred}} against the original data. You can also subset the 
 #' posterior predictions over
 #' legislators/persons or
@@ -592,7 +592,7 @@ setGeneric('id_plot_ppc',signature='object',
 #' 
 #' This function is a wrapper around \code{\link[bayesplot]{ppc_bars}},
 #' \code{\link[bayesplot]{ppc_dens_overlay}} and 
-#' \code{\link[bayesplot]{ppc_violin_grouped} that plots the posterior predictive distribution
+#' \code{\link[bayesplot]{ppc_violin_grouped}} that plots the posterior predictive distribution
 #' derived from \code{\link{id_post_pred}} against the original data. 
 #' Because \code{idealstan} allows for different distributions for each item,
 #' this function can either produce one predictive distribution for all items