From e8d10b0de2014ff5c9834f95d95cf43b84da361a Mon Sep 17 00:00:00 2001
From: fweber144 <fweber144@protonmail.com>
Date: Fri, 1 Jul 2022 11:47:57 +0200
Subject: [PATCH 1/2] Replace `\mbox{}` by `\mathrm{}` (because the former is
 not

displayed correctly in the HTML help from R 4.2.0 on). Note that `\text{}`
causes an error when running `devtools::build_manual()` (probably because it
requires the amsmath TeX package), so it can't be used.
---
 R/methods.R                 | 22 +++++++++++-----------
 R/misc.R                    | 12 ++++++------
 R/refmodel.R                | 18 +++++++++---------
 man/as.matrix.projection.Rd |  4 ++--
 man/pred-projection.Rd      |  8 ++++----
 man/refmodel-init-get.Rd    | 18 +++++++++---------
 man/suggest_size.Rd         | 10 +++++-----
 7 files changed, 46 insertions(+), 46 deletions(-)

diff --git a/R/methods.R b/R/methods.R
index 0d3fcdc43..624e518e8 100644
--- a/R/methods.R
+++ b/R/methods.R
@@ -42,16 +42,16 @@
 #' @param ... Arguments passed to [project()] if `object` is not already an
 #'   object returned by [project()].
 #'
-#' @return Let \eqn{S_{\mbox{prj}}}{S_prj} denote the number of (possibly
+#' @return Let \eqn{S_{\mathrm{prj}}}{S_prj} denote the number of (possibly
 #'   clustered) projected posterior draws (short: the number of projected draws)
 #'   and \eqn{N} the number of observations. Then, if the prediction is done for
 #'   one submodel only (i.e., `length(nterms) == 1 || !is.null(solution_terms)`
 #'   in the call to [project()]):
 #'   * [proj_linpred()] returns a `list` with elements `pred` (predictions) and
-#'   `lpd` (log predictive densities). Both elements are \eqn{S_{\mbox{prj}}
+#'   `lpd` (log predictive densities). Both elements are \eqn{S_{\mathrm{prj}}
 #'   \times N}{S_prj x N} matrices.
-#'   * [proj_predict()] returns an \eqn{S_{\mbox{prj}} \times N}{S_prj x N}
-#'   matrix of predictions where \eqn{S_{\mbox{prj}}}{S_prj} denotes
+#'   * [proj_predict()] returns an \eqn{S_{\mathrm{prj}} \times N}{S_prj x N}
+#'   matrix of predictions where \eqn{S_{\mathrm{prj}}}{S_prj} denotes
 #'   `nresample_clusters` in case of clustered projection.
 #'
 #'   If the prediction is done for more than one submodel, the output from above
@@ -689,18 +689,18 @@ print.vsel <- function(x, ...) {
 #'   the lower or upper bound (depending on argument `type`) of the
 #'   normal-approximation confidence interval (with nominal coverage `1 -
 #'   alpha`; see argument `alpha` of [summary.vsel()]) for \eqn{U_k -
-#'   U_{\mbox{base}}}{U_k - U_base} (with \eqn{U_k} denoting the \eqn{k}-th
-#'   submodel's true utility and \eqn{U_{\mbox{base}}}{U_base} denoting the
+#'   U_{\mathrm{base}}}{U_k - U_base} (with \eqn{U_k} denoting the \eqn{k}-th
+#'   submodel's true utility and \eqn{U_{\mathrm{base}}}{U_base} denoting the
 #'   baseline model's true utility) falls above (or is equal to)
-#'   \deqn{\texttt{pct} \cdot (u_0 - u_{\mbox{base}})}{pct * (u_0 - u_base)}
+#'   \deqn{\texttt{pct} \cdot (u_0 - u_{\mathrm{base}})}{pct * (u_0 - u_base)}
 #'   where \eqn{u_0} denotes the null model's estimated utility and
-#'   \eqn{u_{\mbox{base}}}{u_base} the baseline model's estimated utility. The
+#'   \eqn{u_{\mathrm{base}}}{u_base} the baseline model's estimated utility. The
 #'   baseline is either the reference model or the best submodel found (see
 #'   argument `baseline` of [summary.vsel()]).
 #'
 #'   For example, `alpha = 0.32`, `pct = 0`, and `type = "upper"` means that we
 #'   select the smallest model size for which the upper bound of the 68%
-#'   confidence interval for \eqn{U_k - U_{\mbox{base}}}{U_k - U_base} exceeds
+#'   confidence interval for \eqn{U_k - U_{\mathrm{base}}}{U_k - U_base} exceeds
 #'   (or is equal to) zero, that is, for which the submodel's utility estimate
 #'   is at most one standard error smaller than the baseline model's utility
 #'   estimate.
@@ -1056,8 +1056,8 @@ get_subparams.gamm4 <- function(x, ...) {
 #'   uses `"rstanarm"` if the reference model fit is of an unknown class).
 #' @param ... Currently ignored.
 #'
-#' @return An \eqn{S_{\mbox{prj}} \times Q}{S_prj x Q} matrix of projected
-#'   draws, with \eqn{S_{\mbox{prj}}}{S_prj} denoting the number of projected
+#' @return An \eqn{S_{\mathrm{prj}} \times Q}{S_prj x Q} matrix of projected
+#'   draws, with \eqn{S_{\mathrm{prj}}}{S_prj} denoting the number of projected
 #'   draws and \eqn{Q} the number of parameters.
 #'
 #' @examples
diff --git a/R/misc.R b/R/misc.R
index 8d528ed25..b657839d9 100644
--- a/R/misc.R
+++ b/R/misc.R
@@ -181,24 +181,24 @@ bootstrap <- function(x, fun = mean, B = 2000,
 #   subsampled (without replacement).
 #
 # @return Let \eqn{y} denote the response (vector), \eqn{N} the number of
-#   observations, and \eqn{S_{\mbox{prj}}}{S_prj} the number of projected draws
+#   observations, and \eqn{S_{\mathrm{prj}}}{S_prj} the number of projected draws
 #   (= either `nclusters` or `ndraws`, depending on which one is used). Then the
 #   return value is a list with elements:
 #
-#   * `mu`: An \eqn{N \times S_{\mbox{prj}}}{N x S_prj} matrix of expected
+#   * `mu`: An \eqn{N \times S_{\mathrm{prj}}}{N x S_prj} matrix of expected
 #   values for \eqn{y} for each draw/cluster.
-#   * `var`: An \eqn{N \times S_{\mbox{prj}}}{N x S_prj} matrix of predictive
+#   * `var`: An \eqn{N \times S_{\mathrm{prj}}}{N x S_prj} matrix of predictive
 #   variances for \eqn{y} for each draw/cluster which are needed for projecting
 #   the dispersion parameter (the predictive variances are NA for those families
 #   that do not have a dispersion parameter).
-#   * `dis`: A vector of length \eqn{S_{\mbox{prj}}}{S_prj} containing the
+#   * `dis`: A vector of length \eqn{S_{\mathrm{prj}}}{S_prj} containing the
 #   reference model's dispersion parameter value for each draw/cluster (NA for
 #   those families that do not have a dispersion parameter).
-#   * `weights`: A vector of length \eqn{S_{\mbox{prj}}}{S_prj} containing the
+#   * `weights`: A vector of length \eqn{S_{\mathrm{prj}}}{S_prj} containing the
 #   weights for the draws/clusters.
 #   * `cl`: Cluster assignment for each posterior draw, that is, a vector that
 #   has length equal to the number of posterior draws and each value is an
-#   integer between 1 and \eqn{S_{\mbox{prj}}}{S_prj}.
+#   integer between 1 and \eqn{S_{\mathrm{prj}}}{S_prj}.
 .get_refdist <- function(refmodel, ndraws = NULL, nclusters = NULL,
                          thinning = TRUE) {
   # Number of draws in the reference model:
diff --git a/R/refmodel.R b/R/refmodel.R
index f7a85978d..e80a5d757 100644
--- a/R/refmodel.R
+++ b/R/refmodel.R
@@ -101,8 +101,8 @@
 #' Arguments `ref_predfun`, `proj_predfun`, and `div_minimizer` may be `NULL`
 #' for using an internal default. Otherwise, let \eqn{N} denote the number of
 #' observations (in case of CV, these may be reduced to each fold),
-#' \eqn{S_{\mbox{ref}}}{S_ref} the number of posterior draws for the reference
-#' model's parameters, and \eqn{S_{\mbox{prj}}}{S_prj} the number of (possibly
+#' \eqn{S_{\mathrm{ref}}}{S_ref} the number of posterior draws for the reference
+#' model's parameters, and \eqn{S_{\mathrm{prj}}}{S_prj} the number of (possibly
 #' clustered) parameter draws for projection (short: the number of projected
 #' draws). Then the functions supplied to these arguments need to have the
 #' following prototypes:
@@ -114,7 +114,7 @@
 #'     typically stored in `fit`) or data for new observations (at least in the
 #'     form of a `data.frame`).
 #' * `proj_predfun`: `proj_predfun(fits, newdata)` where:
-#'     + `fits` accepts a `list` of length \eqn{S_{\mbox{prj}}}{S_prj}
+#'     + `fits` accepts a `list` of length \eqn{S_{\mathrm{prj}}}{S_prj}
 #'     containing this number of submodel fits. This `list` is the same as that
 #'     returned by [project()] in its output element `submodl` (which in turn is
 #'     the same as the return value of `div_minimizer`, except if [project()]
@@ -125,15 +125,15 @@
 #' * `div_minimizer` does not need to have a specific prototype, but it needs to
 #' be able to be called with the following arguments:
 #'     + `formula` accepts either a standard [`formula`] with a single response
-#'     (if \eqn{S_{\mbox{prj}} = 1}{S_prj = 1}) or a [`formula`] with
-#'     \eqn{S_{\mbox{prj}} > 1}{S_prj > 1} response variables [cbind()]-ed on
+#'     (if \eqn{S_{\mathrm{prj}} = 1}{S_prj = 1}) or a [`formula`] with
+#'     \eqn{S_{\mathrm{prj}} > 1}{S_prj > 1} response variables [cbind()]-ed on
 #'     the left-hand side in which case the projection has to be performed for
 #'     each of the response variables separately.
 #'     + `data` accepts a `data.frame` to be used for the projection.
 #'     + `family` accepts a [`family`] object.
 #'     + `weights` accepts either observation weights (at least in the form of a
 #'     numeric vector) or `NULL` (for using a vector of ones as weights).
-#'     + `projpred_var` accepts an \eqn{N \times S_{\mbox{prj}}}{N x S_prj}
+#'     + `projpred_var` accepts an \eqn{N \times S_{\mathrm{prj}}}{N x S_prj}
 #'     matrix of predictive variances (necessary for \pkg{projpred}'s internal
 #'     GLM fitter).
 #'     + `projpred_regul` accepts a single numeric value as supplied to argument
@@ -141,9 +141,9 @@
 #'     + `...` accepts further arguments specified by the user.
 #'
 #' The return value of these functions needs to be:
-#' * `ref_predfun`: an \eqn{N \times S_{\mbox{ref}}}{N x S_ref} matrix.
-#' * `proj_predfun`: an \eqn{N \times S_{\mbox{prj}}}{N x S_prj} matrix.
-#' * `div_minimizer`: a `list` of length \eqn{S_{\mbox{prj}}}{S_prj} containing
+#' * `ref_predfun`: an \eqn{N \times S_{\mathrm{ref}}}{N x S_ref} matrix.
+#' * `proj_predfun`: an \eqn{N \times S_{\mathrm{prj}}}{N x S_prj} matrix.
+#' * `div_minimizer`: a `list` of length \eqn{S_{\mathrm{prj}}}{S_prj} containing
 #' this number of submodel fits.
 #'
 #' # Argument `extract_model_data`
diff --git a/man/as.matrix.projection.Rd b/man/as.matrix.projection.Rd
index d835af0e1..567d4e63c 100644
--- a/man/as.matrix.projection.Rd
+++ b/man/as.matrix.projection.Rd
@@ -18,8 +18,8 @@ uses \code{"rstanarm"} if the reference model fit is of an unknown class).}
 \item{...}{Currently ignored.}
 }
 \value{
-An \eqn{S_{\mbox{prj}} \times Q}{S_prj x Q} matrix of projected
-draws, with \eqn{S_{\mbox{prj}}}{S_prj} denoting the number of projected
+An \eqn{S_{\mathrm{prj}} \times Q}{S_prj x Q} matrix of projected
+draws, with \eqn{S_{\mathrm{prj}}}{S_prj} denoting the number of projected
 draws and \eqn{Q} the number of parameters.
 }
 \description{
diff --git a/man/pred-projection.Rd b/man/pred-projection.Rd
index f55151ffd..9f91f4c8d 100644
--- a/man/pred-projection.Rd
+++ b/man/pred-projection.Rd
@@ -82,17 +82,17 @@ the set of clustered posterior draws after projection (with this set being
 determined by argument \code{nclusters} of \code{\link[=project]{project()}}).}
 }
 \value{
-Let \eqn{S_{\mbox{prj}}}{S_prj} denote the number of (possibly
+Let \eqn{S_{\mathrm{prj}}}{S_prj} denote the number of (possibly
 clustered) projected posterior draws (short: the number of projected draws)
 and \eqn{N} the number of observations. Then, if the prediction is done for
 one submodel only (i.e., \code{length(nterms) == 1 || !is.null(solution_terms)}
 in the call to \code{\link[=project]{project()}}):
 \itemize{
 \item \code{\link[=proj_linpred]{proj_linpred()}} returns a \code{list} with elements \code{pred} (predictions) and
-\code{lpd} (log predictive densities). Both elements are \eqn{S_{\mbox{prj}}
+\code{lpd} (log predictive densities). Both elements are \eqn{S_{\mathrm{prj}}
   \times N}{S_prj x N} matrices.
-\item \code{\link[=proj_predict]{proj_predict()}} returns an \eqn{S_{\mbox{prj}} \times N}{S_prj x N}
-matrix of predictions where \eqn{S_{\mbox{prj}}}{S_prj} denotes
+\item \code{\link[=proj_predict]{proj_predict()}} returns an \eqn{S_{\mathrm{prj}} \times N}{S_prj x N}
+matrix of predictions where \eqn{S_{\mathrm{prj}}}{S_prj} denotes
 \code{nresample_clusters} in case of clustered projection.
 }
 
diff --git a/man/refmodel-init-get.Rd b/man/refmodel-init-get.Rd
index 223fc1b8a..954cd6404 100644
--- a/man/refmodel-init-get.Rd
+++ b/man/refmodel-init-get.Rd
@@ -155,8 +155,8 @@ analogously for higher-order joint effects, e.g., of three predictors).
 Arguments \code{ref_predfun}, \code{proj_predfun}, and \code{div_minimizer} may be \code{NULL}
 for using an internal default. Otherwise, let \eqn{N} denote the number of
 observations (in case of CV, these may be reduced to each fold),
-\eqn{S_{\mbox{ref}}}{S_ref} the number of posterior draws for the reference
-model's parameters, and \eqn{S_{\mbox{prj}}}{S_prj} the number of (possibly
+\eqn{S_{\mathrm{ref}}}{S_ref} the number of posterior draws for the reference
+model's parameters, and \eqn{S_{\mathrm{prj}}}{S_prj} the number of (possibly
 clustered) parameter draws for projection (short: the number of projected
 draws). Then the functions supplied to these arguments need to have the
 following prototypes:
@@ -172,7 +172,7 @@ form of a \code{data.frame}).
 }
 \item \code{proj_predfun}: \code{proj_predfun(fits, newdata)} where:
 \itemize{
-\item \code{fits} accepts a \code{list} of length \eqn{S_{\mbox{prj}}}{S_prj}
+\item \code{fits} accepts a \code{list} of length \eqn{S_{\mathrm{prj}}}{S_prj}
 containing this number of submodel fits. This \code{list} is the same as that
 returned by \code{\link[=project]{project()}} in its output element \code{submodl} (which in turn is
 the same as the return value of \code{div_minimizer}, except if \code{\link[=project]{project()}}
@@ -185,15 +185,15 @@ as with \code{refit_prj = FALSE}).
 be able to be called with the following arguments:
 \itemize{
 \item \code{formula} accepts either a standard \code{\link{formula}} with a single response
-(if \eqn{S_{\mbox{prj}} = 1}{S_prj = 1}) or a \code{\link{formula}} with
-\eqn{S_{\mbox{prj}} > 1}{S_prj > 1} response variables \code{\link[=cbind]{cbind()}}-ed on
+(if \eqn{S_{\mathrm{prj}} = 1}{S_prj = 1}) or a \code{\link{formula}} with
+\eqn{S_{\mathrm{prj}} > 1}{S_prj > 1} response variables \code{\link[=cbind]{cbind()}}-ed on
 the left-hand side in which case the projection has to be performed for
 each of the response variables separately.
 \item \code{data} accepts a \code{data.frame} to be used for the projection.
 \item \code{family} accepts a \code{\link{family}} object.
 \item \code{weights} accepts either observation weights (at least in the form of a
 numeric vector) or \code{NULL} (for using a vector of ones as weights).
-\item \code{projpred_var} accepts an \eqn{N \times S_{\mbox{prj}}}{N x S_prj}
+\item \code{projpred_var} accepts an \eqn{N \times S_{\mathrm{prj}}}{N x S_prj}
 matrix of predictive variances (necessary for \pkg{projpred}'s internal
 GLM fitter).
 \item \code{projpred_regul} accepts a single numeric value as supplied to argument
@@ -204,9 +204,9 @@ GLM fitter).
 
 The return value of these functions needs to be:
 \itemize{
-\item \code{ref_predfun}: an \eqn{N \times S_{\mbox{ref}}}{N x S_ref} matrix.
-\item \code{proj_predfun}: an \eqn{N \times S_{\mbox{prj}}}{N x S_prj} matrix.
-\item \code{div_minimizer}: a \code{list} of length \eqn{S_{\mbox{prj}}}{S_prj} containing
+\item \code{ref_predfun}: an \eqn{N \times S_{\mathrm{ref}}}{N x S_ref} matrix.
+\item \code{proj_predfun}: an \eqn{N \times S_{\mathrm{prj}}}{N x S_prj} matrix.
+\item \code{div_minimizer}: a \code{list} of length \eqn{S_{\mathrm{prj}}}{S_prj} containing
 this number of submodel fits.
 }
 }
diff --git a/man/suggest_size.Rd b/man/suggest_size.Rd
index 0aa1a18a0..3549a58dc 100644
--- a/man/suggest_size.Rd
+++ b/man/suggest_size.Rd
@@ -52,18 +52,18 @@ The suggested model size is the smallest model size \eqn{k \in \{0,
   1, ..., \texttt{nterms\_max\}}}{k = 0, 1, ..., nterms_max} for which either
 the lower or upper bound (depending on argument \code{type}) of the
 normal-approximation confidence interval (with nominal coverage \code{1 - alpha}; see argument \code{alpha} of \code{\link[=summary.vsel]{summary.vsel()}}) for \eqn{U_k -
-  U_{\mbox{base}}}{U_k - U_base} (with \eqn{U_k} denoting the \eqn{k}-th
-submodel's true utility and \eqn{U_{\mbox{base}}}{U_base} denoting the
+  U_{\mathrm{base}}}{U_k - U_base} (with \eqn{U_k} denoting the \eqn{k}-th
+submodel's true utility and \eqn{U_{\mathrm{base}}}{U_base} denoting the
 baseline model's true utility) falls above (or is equal to)
-\deqn{\texttt{pct} \cdot (u_0 - u_{\mbox{base}})}{pct * (u_0 - u_base)}
+\deqn{\texttt{pct} \cdot (u_0 - u_{\mathrm{base}})}{pct * (u_0 - u_base)}
 where \eqn{u_0} denotes the null model's estimated utility and
-\eqn{u_{\mbox{base}}}{u_base} the baseline model's estimated utility. The
+\eqn{u_{\mathrm{base}}}{u_base} the baseline model's estimated utility. The
 baseline is either the reference model or the best submodel found (see
 argument \code{baseline} of \code{\link[=summary.vsel]{summary.vsel()}}).
 
 For example, \code{alpha = 0.32}, \code{pct = 0}, and \code{type = "upper"} means that we
 select the smallest model size for which the upper bound of the 68\%
-confidence interval for \eqn{U_k - U_{\mbox{base}}}{U_k - U_base} exceeds
+confidence interval for \eqn{U_k - U_{\mathrm{base}}}{U_k - U_base} exceeds
 (or is equal to) zero, that is, for which the submodel's utility estimate
 is at most one standard error smaller than the baseline model's utility
 estimate.

From a677b12b97c52e7a925644f83cebacd88810579f Mon Sep 17 00:00:00 2001
From: fweber144 <fweber144@protonmail.com>
Date: Fri, 1 Jul 2022 11:55:12 +0200
Subject: [PATCH 2/2] Reflow to margin width 80.

---
 R/misc.R                 | 6 +++---
 R/refmodel.R             | 4 ++--
 man/refmodel-init-get.Rd | 4 ++--
 3 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/R/misc.R b/R/misc.R
index b657839d9..57f00cefc 100644
--- a/R/misc.R
+++ b/R/misc.R
@@ -181,9 +181,9 @@ bootstrap <- function(x, fun = mean, B = 2000,
 #   subsampled (without replacement).
 #
 # @return Let \eqn{y} denote the response (vector), \eqn{N} the number of
-#   observations, and \eqn{S_{\mathrm{prj}}}{S_prj} the number of projected draws
-#   (= either `nclusters` or `ndraws`, depending on which one is used). Then the
-#   return value is a list with elements:
+#   observations, and \eqn{S_{\mathrm{prj}}}{S_prj} the number of projected
+#   draws (= either `nclusters` or `ndraws`, depending on which one is used).
+#   Then the return value is a list with elements:
 #
 #   * `mu`: An \eqn{N \times S_{\mathrm{prj}}}{N x S_prj} matrix of expected
 #   values for \eqn{y} for each draw/cluster.
diff --git a/R/refmodel.R b/R/refmodel.R
index e80a5d757..66f228106 100644
--- a/R/refmodel.R
+++ b/R/refmodel.R
@@ -143,8 +143,8 @@
 #' The return value of these functions needs to be:
 #' * `ref_predfun`: an \eqn{N \times S_{\mathrm{ref}}}{N x S_ref} matrix.
 #' * `proj_predfun`: an \eqn{N \times S_{\mathrm{prj}}}{N x S_prj} matrix.
-#' * `div_minimizer`: a `list` of length \eqn{S_{\mathrm{prj}}}{S_prj} containing
-#' this number of submodel fits.
+#' * `div_minimizer`: a `list` of length \eqn{S_{\mathrm{prj}}}{S_prj}
+#' containing this number of submodel fits.
 #'
 #' # Argument `extract_model_data`
 #'
diff --git a/man/refmodel-init-get.Rd b/man/refmodel-init-get.Rd
index 954cd6404..8bcafa28b 100644
--- a/man/refmodel-init-get.Rd
+++ b/man/refmodel-init-get.Rd
@@ -206,8 +206,8 @@ The return value of these functions needs to be:
 \itemize{
 \item \code{ref_predfun}: an \eqn{N \times S_{\mathrm{ref}}}{N x S_ref} matrix.
 \item \code{proj_predfun}: an \eqn{N \times S_{\mathrm{prj}}}{N x S_prj} matrix.
-\item \code{div_minimizer}: a \code{list} of length \eqn{S_{\mathrm{prj}}}{S_prj} containing
-this number of submodel fits.
+\item \code{div_minimizer}: a \code{list} of length \eqn{S_{\mathrm{prj}}}{S_prj}
+containing this number of submodel fits.
 }
 }