diff --git a/man/details_boost_tree_lightgbm.Rd b/man/details_boost_tree_lightgbm.Rd
index 1853eed15..b5ee3dd0a 100644
--- a/man/details_boost_tree_lightgbm.Rd
+++ b/man/details_boost_tree_lightgbm.Rd
@@ -175,7 +175,7 @@ that the booster will perform bagging at every \code{k}th boosting iteration.
 Thus, by default, the \code{sample_size} argument would be ignored without
 setting this argument manually. Other boosting libraries, like xgboost,
 do not have an analogous argument to \code{bagging_freq} and use \code{k = 1} when
-the analogue to \code{bagging_fraction} is in $(0, 1)$. \emph{bonsai will thus
+the analogue to \code{bagging_fraction} is in $\verb{(0, 1)}$. \emph{bonsai will thus
 automatically set} \code{bagging_freq = 1} \emph{in} \code{set_engine("lightgbm", ...)}
 if \code{sample_size} (i.e. \code{bagging_fraction}) is not equal to 1 and no
 \code{bagging_freq} value is supplied. This default can be overridden by
diff --git a/man/details_linear_reg_glmer.Rd b/man/details_linear_reg_glmer.Rd
index 67d8745bb..ae354daa5 100644
--- a/man/details_linear_reg_glmer.Rd
+++ b/man/details_linear_reg_glmer.Rd
@@ -52,7 +52,7 @@ linear predictor (\verb{\eta}) for a random intercept:
 \if{html}{\out{<div class="sourceCode">}}\preformatted{\eta_\{i\} = (\beta_0 + b_\{0i\}) + \beta_1x_\{i1\}
 }\if{html}{\out{</div>}}
 
-where $i$ denotes the \code{i}th independent experimental unit
+where $\code{i}$ denotes the \code{i}th independent experimental unit
 (e.g. subject). When the model has seen subject \code{i}, it can use that
 subject’s data to adjust the \emph{population} intercept to be more specific
 to that subjects results.
diff --git a/man/details_linear_reg_lme.Rd b/man/details_linear_reg_lme.Rd
index 3c9c70096..fd9362d3d 100644
--- a/man/details_linear_reg_lme.Rd
+++ b/man/details_linear_reg_lme.Rd
@@ -44,7 +44,7 @@ linear predictor (\verb{\eta}) for a random intercept:
 \if{html}{\out{<div class="sourceCode">}}\preformatted{\eta_\{i\} = (\beta_0 + b_\{0i\}) + \beta_1x_\{i1\}
 }\if{html}{\out{</div>}}
 
-where $i$ denotes the \code{i}th independent experimental unit
+where $\code{i}$ denotes the \code{i}th independent experimental unit
 (e.g. subject). When the model has seen subject \code{i}, it can use that
 subject’s data to adjust the \emph{population} intercept to be more specific
 to that subjects results.
diff --git a/man/details_linear_reg_lmer.Rd b/man/details_linear_reg_lmer.Rd
index 0441f464a..26a917fc4 100644
--- a/man/details_linear_reg_lmer.Rd
+++ b/man/details_linear_reg_lmer.Rd
@@ -44,7 +44,7 @@ linear predictor (\verb{\eta}) for a random intercept:
 \if{html}{\out{<div class="sourceCode">}}\preformatted{\eta_\{i\} = (\beta_0 + b_\{0i\}) + \beta_1x_\{i1\}
 }\if{html}{\out{</div>}}
 
-where $i$ denotes the \code{i}th independent experimental unit
+where $\code{i}$ denotes the \code{i}th independent experimental unit
 (e.g. subject). When the model has seen subject \code{i}, it can use that
 subject’s data to adjust the \emph{population} intercept to be more specific
 to that subjects results.
diff --git a/man/details_linear_reg_stan_glmer.Rd b/man/details_linear_reg_stan_glmer.Rd
index 3bcb67ddf..6c6ae4f2a 100644
--- a/man/details_linear_reg_stan_glmer.Rd
+++ b/man/details_linear_reg_stan_glmer.Rd
@@ -64,7 +64,7 @@ linear predictor (\verb{\eta}) for a random intercept:
 \if{html}{\out{<div class="sourceCode">}}\preformatted{\eta_\{i\} = (\beta_0 + b_\{0i\}) + \beta_1x_\{i1\}
 }\if{html}{\out{</div>}}
 
-where $i$ denotes the \code{i}th independent experimental unit
+where $\code{i}$ denotes the \code{i}th independent experimental unit
 (e.g. subject). When the model has seen subject \code{i}, it can use that
 subject’s data to adjust the \emph{population} intercept to be more specific
 to that subjects results.
diff --git a/man/details_logistic_reg_glmer.Rd b/man/details_logistic_reg_glmer.Rd
index b848df19c..6d7882bed 100644
--- a/man/details_logistic_reg_glmer.Rd
+++ b/man/details_logistic_reg_glmer.Rd
@@ -44,7 +44,7 @@ linear predictor (\verb{\eta}) for a random intercept:
 \if{html}{\out{<div class="sourceCode">}}\preformatted{\eta_\{i\} = (\beta_0 + b_\{0i\}) + \beta_1x_\{i1\}
 }\if{html}{\out{</div>}}
 
-where $i$ denotes the \code{i}th independent experimental unit
+where $\code{i}$ denotes the \code{i}th independent experimental unit
 (e.g. subject). When the model has seen subject \code{i}, it can use that
 subject’s data to adjust the \emph{population} intercept to be more specific
 to that subjects results.
diff --git a/man/details_logistic_reg_stan_glmer.Rd b/man/details_logistic_reg_stan_glmer.Rd
index ce1281501..6dc9d78d3 100644
--- a/man/details_logistic_reg_stan_glmer.Rd
+++ b/man/details_logistic_reg_stan_glmer.Rd
@@ -63,7 +63,7 @@ linear predictor (\verb{\eta}) for a random intercept:
 \if{html}{\out{<div class="sourceCode">}}\preformatted{\eta_\{i\} = (\beta_0 + b_\{0i\}) + \beta_1x_\{i1\}
 }\if{html}{\out{</div>}}
 
-where $i$ denotes the \code{i}th independent experimental unit
+where $\code{i}$ denotes the \code{i}th independent experimental unit
 (e.g. subject). When the model has seen subject \code{i}, it can use that
 subject’s data to adjust the \emph{population} intercept to be more specific
 to that subjects results.
diff --git a/man/details_poisson_reg_glmer.Rd b/man/details_poisson_reg_glmer.Rd
index 5a32c17bd..277d40325 100644
--- a/man/details_poisson_reg_glmer.Rd
+++ b/man/details_poisson_reg_glmer.Rd
@@ -44,7 +44,7 @@ linear predictor (\verb{\eta}) for a random intercept:
 \if{html}{\out{<div class="sourceCode">}}\preformatted{\eta_\{i\} = (\beta_0 + b_\{0i\}) + \beta_1x_\{i1\}
 }\if{html}{\out{</div>}}
 
-where $i$ denotes the \code{i}th independent experimental unit
+where $\code{i}$ denotes the \code{i}th independent experimental unit
 (e.g. subject). When the model has seen subject \code{i}, it can use that
 subject’s data to adjust the \emph{population} intercept to be more specific
 to that subjects results.
diff --git a/man/details_poisson_reg_stan_glmer.Rd b/man/details_poisson_reg_stan_glmer.Rd
index ef1065ada..a15f0f787 100644
--- a/man/details_poisson_reg_stan_glmer.Rd
+++ b/man/details_poisson_reg_stan_glmer.Rd
@@ -63,7 +63,7 @@ linear predictor (\verb{\eta}) for a random intercept:
 \if{html}{\out{<div class="sourceCode">}}\preformatted{\eta_\{i\} = (\beta_0 + b_\{0i\}) + \beta_1x_\{i1\}
 }\if{html}{\out{</div>}}
 
-where $i$ denotes the \code{i}th independent experimental unit
+where $\code{i}$ denotes the \code{i}th independent experimental unit
 (e.g. subject). When the model has seen subject \code{i}, it can use that
 subject’s data to adjust the \emph{population} intercept to be more specific
 to that subjects results.