diff --git a/DESCRIPTION b/DESCRIPTION
index 954bc4f46..69a1226f4 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -76,4 +76,4 @@ UseLTO: true
 NeedsCompilation: yes
 URL: https://mc-stan.org/rstanarm/, https://discourse.mc-stan.org
 BugReports: https://github.com/stan-dev/rstanarm/issues
-RoxygenNote: 7.2.3
+RoxygenNote: 7.3.2
diff --git a/NAMESPACE b/NAMESPACE
index e32585cb3..491beb800 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -229,7 +229,6 @@ importFrom(ggplot2,scale_x_discrete)
 importFrom(ggplot2,theme)
 importFrom(ggplot2,theme_bw)
 importFrom(ggplot2,xlab)
-importFrom(reformulas,findbars)
 importFrom(lme4,fixef)
 importFrom(lme4,glFormula)
 importFrom(lme4,glmer)
@@ -262,6 +261,7 @@ importFrom(posterior,as_draws_df)
 importFrom(posterior,as_draws_list)
 importFrom(posterior,as_draws_matrix)
 importFrom(posterior,as_draws_rvars)
+importFrom(reformulas,findbars)
 importFrom(rstan,constrain_pars)
 importFrom(rstan,extract)
 importFrom(rstan,extract_sparse_parts)
diff --git a/R/doc-rstanarm-package.R b/R/doc-rstanarm-package.R
index a80773c9c..8d93e769d 100644
--- a/R/doc-rstanarm-package.R
+++ b/R/doc-rstanarm-package.R
@@ -17,9 +17,6 @@
 
 #' Applied Regression Modeling via RStan
 #'
-#' @docType package
-#' @name rstanarm-package
-#' @aliases rstanarm
 #' @useDynLib rstanarm, .registration = TRUE
 #'
 #' @import methods
@@ -104,4 +101,4 @@
 #' @template reference-bayesvis
 #' @template reference-muth
 #'
-NULL
+"_PACKAGE"
diff --git a/R/jm_make_assoc_terms.R b/R/jm_make_assoc_terms.R
index be823b2f6..91e6fab02 100644
--- a/R/jm_make_assoc_terms.R
+++ b/R/jm_make_assoc_terms.R
@@ -388,9 +388,11 @@ get_element <- function(parts, m = 1, which = "eta", ...) {
 # @param grp_assoc Character string, the function to use to collapse
 #   across the lower level units clustered within individuals.
 # @return A vector or matrix, depending on the method called.
+#' @noRd
 collapse_within_groups <- function(eta, grp_idx, grp_assoc = "sum") {
   UseMethod("collapse_within_groups")
 }
+#' @exportS3Method NULL
 collapse_within_groups.default <- function(eta, grp_idx, grp_assoc) {
   N <- nrow(grp_idx)
   val <- rep(NA, N)
@@ -400,6 +402,7 @@ collapse_within_groups.default <- function(eta, grp_idx, grp_assoc) {
   }
   val
 }
+#' @exportS3Method NULL
 collapse_within_groups.matrix <- function(eta, grp_idx, grp_assoc) {
   N <- nrow(grp_idx)
   val <- matrix(NA, nrow = nrow(eta), ncol = N)
diff --git a/R/launch_shinystan.R b/R/launch_shinystan.R
index f01592c6b..39d6acf05 100644
--- a/R/launch_shinystan.R
+++ b/R/launch_shinystan.R
@@ -43,7 +43,7 @@
 #'   used with ShinyStan. The only exception is that ShinyStan does not 
 #'   currently support \pkg{rstanarm} models fit using 
 #'   \code{algorithm='optimizing'}. See the 
-#'   \pkg{\link[=shinystan-package]{shinystan}} package documentation for more 
+#'   \pkg{\link[shinystan:shinystan-package]{shinystan}} package documentation for more 
 #'   information.
 #'   
 #' @section Faster launch times:
diff --git a/R/log_lik.R b/R/log_lik.R
index 7d9815d5f..61cff5423 100644
--- a/R/log_lik.R
+++ b/R/log_lik.R
@@ -200,7 +200,9 @@ ll_fun <- function(x, m = NULL) {
 #   just a single MCMC draw).
 # @return a named list with elements data, draws, S (posterior sample size) and
 #   N = number of observations
+#' @noRd
 ll_args <- function(object, ...) UseMethod("ll_args")
+#' @exportS3Method NULL
 ll_args.stanreg <- function(object, newdata = NULL, offset = NULL, m = NULL, 
                             reloo_or_kfold = FALSE, ...) {
   validate_stanreg_object(object)
@@ -511,6 +513,7 @@ ll_args.stanreg <- function(object, newdata = NULL, offset = NULL, m = NULL,
 # @param pars Output from extract_pars
 # @param m Integer specifying which submodel
 # @param reloo_or_kfold logical. TRUE if ll_args is for reloo or kfold
+#' @exportS3Method NULL
 ll_args.stanjm <- function(object, data, pars, m = 1, 
                            reloo_or_kfold = FALSE, ...) {
   validate_stanjm_object(object)
@@ -863,10 +866,12 @@ evaluate_log_basehaz <- function(times, basehaz, coefs) {
 #   at which the log hazard was evaluated for each individual.
 # @param qwts A vector of unstandardised GK quadrature weights.
 # @return A vector or matrix of log survival probabilities.
+#' @noRd
 evaluate_log_survival <- function(log_haz, qnodes, qwts) {
   UseMethod("evaluate_log_survival")
 }
 
+#' @exportS3Method NULL
 evaluate_log_survival.default <- function(log_haz, qnodes, qwts) {
   # convert log hazard to hazard
   haz <- exp(log_haz)
@@ -879,6 +884,7 @@ evaluate_log_survival.default <- function(log_haz, qnodes, qwts) {
   -cum_haz
 }
 
+#' @exportS3Method NULL
 evaluate_log_survival.matrix <- function(log_haz, qnodes, qwts) {
   # convert log hazard to hazard
   haz <- exp(log_haz)
diff --git a/R/loo-prediction.R b/R/loo-prediction.R
index e2c19d38c..41dd59238 100644
--- a/R/loo-prediction.R
+++ b/R/loo-prediction.R
@@ -155,6 +155,7 @@ loo_predictive_interval.stanreg <-
 
 # internal ----------------------------------------------------------------
 
+#' @exportS3Method NULL
 psis.stanreg <- function(log_ratios, ...) {
   object <- log_ratios
   message("Running PSIS to compute weights...")
diff --git a/R/loo.R b/R/loo.R
index ff1a88c18..8d1819d6b 100644
--- a/R/loo.R
+++ b/R/loo.R
@@ -19,7 +19,7 @@
 #'
 #' @description For models fit using MCMC, compute approximate leave-one-out
 #'   cross-validation (LOO, LOOIC) or, less preferably, the Widely Applicable
-#'   Information Criterion (WAIC) using the \pkg{\link[=loo-package]{loo}}
+#'   Information Criterion (WAIC) using the \pkg{\link[loo:loo-package]{loo}}
 #'   package. (For \eqn{K}-fold cross-validation see \code{\link{kfold.stanreg}}.)
 #'   Functions for  model comparison, and model weighting/averaging are also
 #'   provided. 
@@ -74,7 +74,7 @@
 #'   package).
 #'
 #' @section Approximate LOO CV: The \code{loo} method for stanreg objects
-#'   provides an interface to the \pkg{\link[=loo-package]{loo}} package for
+#'   provides an interface to the \pkg{\link[loo:loo-package]{loo}} package for
 #'   approximate leave-one-out cross-validation (LOO). The LOO Information
 #'   Criterion (LOOIC) has the same purpose as the Akaike Information Criterion
 #'   (AIC) that is used by frequentists. Both are intended to estimate the
@@ -96,7 +96,7 @@
 #'   observations. Warnings about Pareto \eqn{k} estimates indicate observations
 #'   for which the approximation to LOO is problematic (this is described in
 #'   detail in Vehtari, Gelman, and Gabry (2017) and the
-#'   \pkg{\link[=loo-package]{loo}} package documentation). The
+#'   \pkg{\link[loo:loo-package]{loo}} package documentation). The
 #'   \code{k_threshold} argument can be used to set the \eqn{k} value above
 #'   which an observation is flagged. If \code{k_threshold} is not \code{NULL}
 #'   and there are \eqn{J} observations with \eqn{k} estimates above
diff --git a/R/misc.R b/R/misc.R
index 9d59d3fb3..39e7e616a 100644
--- a/R/misc.R
+++ b/R/misc.R
@@ -545,9 +545,11 @@ set_prior_scale <- function(scale, default, link) {
 # @param x Predictor matrix.
 # @param offset Optional offset vector.
 # @return A vector or matrix.
+#' @noRd
 linear_predictor <- function(beta, x, offset = NULL) {
   UseMethod("linear_predictor")
 }
+#' @exportS3Method NULL
 linear_predictor.default <- function(beta, x, offset = NULL) {
   eta <- as.vector(if (NCOL(x) == 1L) x * beta else x %*% beta)
   if (length(offset))
@@ -555,6 +557,7 @@ linear_predictor.default <- function(beta, x, offset = NULL) {
   
   return(eta)
 }
+#' @exportS3Method NULL
 linear_predictor.matrix <- function(beta, x, offset = NULL) {
   if (NCOL(beta) == 1L) 
     beta <- as.matrix(beta)
@@ -630,19 +633,24 @@ get_z.stanmvreg <- function(object, m = NULL, ...) {
 # @param x A stanreg object, family object, or string. 
 # @param ... Other arguments passed to methods. For a \code{stanmvreg} object
 #   this can be an integer \code{m} specifying the submodel.
-# @return The inverse link function associated with x.
+# @return The inverse link function associated with x
+#' @noRd
 linkinv <- function(x, ...) UseMethod("linkinv")
+#' @exportS3Method NULL
 linkinv.stanreg <- function(x, ...) {
   if (is(x, "polr")) polr_linkinv(x) else family(x)$linkinv
 }
+#' @exportS3Method NULL
 linkinv.stanmvreg <- function(x, m = NULL, ...) {
   ret <- lapply(family(x), `[[`, "linkinv")
   stub <- get_stub(x)
   if (!is.null(m)) ret[[m]] else list_nms(ret, stub = stub)
 }
+#' @exportS3Method NULL
 linkinv.family <- function(x, ...) {
   x$linkinv
 }
+#' @exportS3Method NULL
 linkinv.character <- function(x, ...) {
   stopifnot(length(x) == 1)
   polr_linkinv(x)
diff --git a/R/plots.R b/R/plots.R
index a1f8551a8..83be13801 100644
--- a/R/plots.R
+++ b/R/plots.R
@@ -19,7 +19,7 @@
 #'
 #' The \code{plot} method for \link{stanreg-objects} provides a convenient 
 #' interface to the \link[bayesplot:MCMC-overview]{MCMC} module in the 
-#' \pkg{\link{bayesplot}} package for plotting MCMC draws and diagnostics. It is also 
+#' \pkg{\link[bayesplot:bayesplot-package]{bayesplot}} package for plotting MCMC draws and diagnostics. It is also 
 #' straightforward to use the functions from the \pkg{bayesplot} package directly rather than
 #' via the \code{plot} method. Examples of both methods of plotting are given
 #' below.
diff --git a/R/pp_check.R b/R/pp_check.R
index 6e74d0b35..5b3a9fa5f 100644
--- a/R/pp_check.R
+++ b/R/pp_check.R
@@ -18,7 +18,7 @@
 #' Graphical posterior predictive checks
 #'
 #' Interface to the \link[bayesplot:PPC-overview]{PPC} (posterior predictive checking) module
-#' in the \pkg{\link{bayesplot}} package, providing various plots comparing the
+#' in the \pkg{\link[bayesplot:bayesplot-package]{bayesplot}} package, providing various plots comparing the
 #' observed outcome variable \eqn{y} to simulated datasets \eqn{y^{rep}}{yrep}
 #' from the posterior predictive distribution. The \code{pp_check} method for
 #' \link{stanreg-objects} prepares the arguments required for the specified
@@ -51,7 +51,7 @@
 #'   overlay many \code{yrep} datasets (e.g., \code{ppc_dens_overlay}) a larger
 #'   number is used by default, and for other functions (e.g. \code{ppc_stat})
 #'   the default is to set \code{nreps} equal to the posterior sample size.
-#' @param ... Additonal arguments passed to the \pkg{\link{bayesplot}} function
+#' @param ... Additonal arguments passed to the \pkg{\link[bayesplot:bayesplot-package]{bayesplot}} function
 #'   called. For many plotting functions \code{...} is optional, however for
 #'   functions that require a \code{group} or \code{x} argument, these arguments
 #'   should be specified in \code{...}. If specifying \code{group} and/or
diff --git a/R/predictive_error.R b/R/predictive_error.R
index ba9c34364..0567a9940 100644
--- a/R/predictive_error.R
+++ b/R/predictive_error.R
@@ -141,6 +141,7 @@ predictive_error.ppd <- function(object, y, ...) {
 #   is available. The argument \code{u} specifies the time at which the 
 #   prediction error should be calculated (i.e. the time horizon).
 #   
+#' @exportS3Method NULL
 predictive_error.stanmvreg <-
   function(object,
            newdataLong = NULL,
diff --git a/R/stan_biglm.R b/R/stan_biglm.R
index 48f38897e..b9602fccf 100644
--- a/R/stan_biglm.R
+++ b/R/stan_biglm.R
@@ -52,7 +52,7 @@
 #'   outcome, such observations do not contribute to these statistics.
 #'   
 #' @return The output of both \code{stan_biglm} and \code{stan_biglm.fit} is an
-#'   object of \code{\link[rstan]{stanfit-class}} rather than
+#'   object of \code{\link[rstan:stanfit-class]{stanfit-class}} rather than
 #'   \code{\link{stanreg-objects}}, which is more limited and less convenient
 #'   but necessitated by the fact that \code{stan_biglm} does not bring the full
 #'   design matrix into memory. Without the full design matrix,some of the
diff --git a/R/stan_glm.fit.R b/R/stan_glm.fit.R
index 4747a2e3b..2ea9273ea 100644
--- a/R/stan_glm.fit.R
+++ b/R/stan_glm.fit.R
@@ -902,7 +902,10 @@ pad_reTrms <- function(Ztlist, cnms, flist) {
 #   stats)
 # @param columns Do the columns (TRUE) or rows (FALSE) correspond to the 
 #   variables?
+
+#' @noRd
 unpad_reTrms <- function(x, ...) UseMethod("unpad_reTrms")
+#' @exportS3Method NULL
 unpad_reTrms.default <- function(x, ...) {
   if (is.matrix(x) || is.array(x))
     return(unpad_reTrms.array(x, ...))
@@ -910,6 +913,7 @@ unpad_reTrms.default <- function(x, ...) {
   x[keep]
 }
 
+#' @exportS3Method NULL
 unpad_reTrms.array <- function(x, columns = TRUE, ...) {
   ndim <- length(dim(x))
   if (ndim > 3)
@@ -943,7 +947,7 @@ make_b_nms <- function(group, m = NULL, stub = "Long") {
                                    levels(group$flist[[nm]])))))
     }
   }
-  return(b_nms)  
+  return(b_nms)
 }
 
 
diff --git a/R/stanreg-objects.R b/R/stanreg-objects.R
index df78b4cf3..f8fbfbc28 100644
--- a/R/stanreg-objects.R
+++ b/R/stanreg-objects.R
@@ -84,7 +84,7 @@
 #'   A list with information about the prior distributions used.
 #'   }
 #'   \item{\code{stanfit,stan_summary}}{
-#'   The object of \code{\link[rstan]{stanfit-class}} returned by RStan and a
+#'   The object of \code{\link[rstan:stanfit-class]{stanfit-class}} returned by RStan and a
 #'   matrix of various summary statistics from the stanfit object.
 #'   }
 #'   \item{\code{rstan_version}}{
diff --git a/R/stanreg_list.R b/R/stanreg_list.R
index b4044c36a..fcc59f73b 100644
--- a/R/stanreg_list.R
+++ b/R/stanreg_list.R
@@ -277,9 +277,11 @@ stanreg_list_families <- function(mods) {
 # when a stanreg_list is created those attributes should be changed to match 
 # the names of the models used for the stanreg_list in case user has specified 
 # the model_names argument
+#' @noRd
 rename_loos <- function(x,...) UseMethod("rename_loos")
 
 # Change model_name attributes of a loo/waic/kfold object stored in a stanreg object,
+#' @exportS3Method NULL
 rename_loos.stanreg <- function(x, new_model_name,...) {
   for (criterion in c("loo", "waic", "kfold")) {
      if (!is.null(x[[criterion]])) {
@@ -291,6 +293,7 @@ rename_loos.stanreg <- function(x, new_model_name,...) {
 
 # Change model_name attributes of loo/waic/kfold objects to correspond to 
 # model names used for stanreg_list
+#' @exportS3Method NULL
 rename_loos.stanreg_list <- function(x, ...) {
   for (j in seq_along(x)) {
     x[[j]] <- rename_loos.stanreg(x[[j]], new_model_name = names(x)[j])
diff --git a/man-roxygen/return-stanfit-object.R b/man-roxygen/return-stanfit-object.R
index d607b4b8c..60444ede5 100644
--- a/man-roxygen/return-stanfit-object.R
+++ b/man-roxygen/return-stanfit-object.R
@@ -1,2 +1,2 @@
-#' @return A \link[=stanfit-class]{stanfit} object (or a slightly modified 
+#' @return A \link[rstan:stanfit-class]{stanfit} object (or a slightly modified 
 #'   stanfit object) is returned if \code{<%= fitfun %>} is called directly.