diff --git a/R/check_autocorrelation.R b/R/check_autocorrelation.R
index 52ac9b53c..70930e1d6 100644
--- a/R/check_autocorrelation.R
+++ b/R/check_autocorrelation.R
@@ -11,6 +11,8 @@
 #' @return Invisibly returns the p-value of the test statistics. A p-value < 0.05
 #' indicates autocorrelated residuals.
 #'
+#' @family checking model assumptions and quality
+#'
 #' @details Performs a Durbin-Watson-Test to check for autocorrelated residuals.
 #' In case of autocorrelation, robust standard errors return more accurate
 #' results for the estimates, or maybe a mixed model with error term for the
diff --git a/R/check_collinearity.R b/R/check_collinearity.R
index 636ac4a98..8eb2a27de 100644
--- a/R/check_collinearity.R
+++ b/R/check_collinearity.R
@@ -110,6 +110,8 @@
 #' common statistical problems: Data exploration. Methods in Ecology and
 #' Evolution (2010) 1:3–14.
 #'
+#' @family checking model assumptions and quality
+#'
 #' @note The code to compute the confidence intervals for the VIF and tolerance
 #' values was adapted from the Appendix B from the Marcoulides et al. paper.
 #' Thus, credits go to these authors the original algorithm. There is also
diff --git a/R/check_convergence.R b/R/check_convergence.R
index 4580001e6..192f09377 100644
--- a/R/check_convergence.R
+++ b/R/check_convergence.R
@@ -12,38 +12,39 @@
 #' @return `TRUE` if convergence is fine and `FALSE` if convergence
 #'   is suspicious. Additionally, the convergence value is returned as attribute.
 #'
-#' @details \subsection{Convergence and log-likelihood}{
-#'   Convergence problems typically arise when the model hasn't converged
-#'   to a solution where the log-likelihood has a true maximum. This may result
-#'   in unreliable and overly complex (or non-estimable) estimates and standard
-#'   errors.
-#'   }
-#'   \subsection{Inspect model convergence}{
-#'   **lme4** performs a convergence-check (see `?lme4::convergence`),
-#'   however, as as discussed [here](https://github.com/lme4/lme4/issues/120)
-#'   and suggested by one of the lme4-authors in
-#'   [this comment](https://github.com/lme4/lme4/issues/120#issuecomment-39920269),
-#'   this check can be too strict. `check_convergence()` thus provides an
-#'   alternative convergence test for `merMod`-objects.
-#'   }
-#'   \subsection{Resolving convergence issues}{
-#'   Convergence issues are not easy to diagnose. The help page on
-#'   `?lme4::convergence` provides most of the current advice about
-#'   how to resolve convergence issues. Another clue might be large parameter
-#'   values, e.g. estimates (on the scale of the linear predictor) larger than
-#'   10 in (non-identity link) generalized linear model *might* indicate
-#'   [complete separation](https://stats.oarc.ucla.edu/other/mult-pkg/faq/general/faqwhat-is-complete-or-quasi-complete-separation-in-logisticprobit-regression-and-how-do-we-deal-with-them/).
-#'   Complete separation can be addressed by regularization, e.g. penalized
-#'   regression or Bayesian regression with appropriate priors on the fixed effects.
-#'   }
-#'   \subsection{Convergence versus Singularity}{
-#'   Note the different meaning between singularity and convergence: singularity
-#'   indicates an issue with the "true" best estimate, i.e. whether the maximum
-#'   likelihood estimation for the variance-covariance matrix of the random effects
-#'   is positive definite or only semi-definite. Convergence is a question of
-#'   whether we can assume that the numerical optimization has worked correctly
-#'   or not.
-#'   }
+#' @section Convergence and log-likelihood:
+#' Convergence problems typically arise when the model hasn't converged
+#' to a solution where the log-likelihood has a true maximum. This may result
+#' in unreliable and overly complex (or non-estimable) estimates and standard
+#' errors.
+#'
+#' @section Inspect model convergence:
+#' **lme4** performs a convergence-check (see `?lme4::convergence`),
+#' however, as as discussed [here](https://github.com/lme4/lme4/issues/120)
+#' and suggested by one of the lme4-authors in
+#' [this comment](https://github.com/lme4/lme4/issues/120#issuecomment-39920269),
+#' this check can be too strict. `check_convergence()` thus provides an
+#' alternative convergence test for `merMod`-objects.
+#'
+#' @section Resolving convergence issues:
+#' Convergence issues are not easy to diagnose. The help page on
+#' `?lme4::convergence` provides most of the current advice about
+#' how to resolve convergence issues. Another clue might be large parameter
+#' values, e.g. estimates (on the scale of the linear predictor) larger than
+#' 10 in (non-identity link) generalized linear model *might* indicate
+#' [complete separation](https://stats.oarc.ucla.edu/other/mult-pkg/faq/general/faqwhat-is-complete-or-quasi-complete-separation-in-logisticprobit-regression-and-how-do-we-deal-with-them/).
+#' Complete separation can be addressed by regularization, e.g. penalized
+#' regression or Bayesian regression with appropriate priors on the fixed effects.
+#'
+#' @section Convergence versus Singularity:
+#' Note the different meaning between singularity and convergence: singularity
+#' indicates an issue with the "true" best estimate, i.e. whether the maximum
+#' likelihood estimation for the variance-covariance matrix of the random effects
+#' is positive definite or only semi-definite. Convergence is a question of
+#' whether we can assume that the numerical optimization has worked correctly
+#' or not.
+#'
+#' @family checking model assumptions and quality
 #'
 #' @examples
 #' if (require("lme4")) {
diff --git a/R/check_heteroscedasticity.R b/R/check_heteroscedasticity.R
index 2d074737c..55bf00da8 100644
--- a/R/check_heteroscedasticity.R
+++ b/R/check_heteroscedasticity.R
@@ -18,6 +18,8 @@
 #'
 #' @references Breusch, T. S., and Pagan, A. R. (1979) A simple test for heteroscedasticity and random coefficient variation. Econometrica 47, 1287-1294.
 #'
+#' @family checking model assumptions and quality
+#'
 #' @examples
 #' m <<- lm(mpg ~ wt + cyl + gear + disp, data = mtcars)
 #' check_heteroscedasticity(m)
diff --git a/R/check_homogeneity.R b/R/check_homogeneity.R
index 1a8fc9a68..f746037cf 100644
--- a/R/check_homogeneity.R
+++ b/R/check_homogeneity.R
@@ -18,6 +18,8 @@
 #'
 #' @note There is also a [`plot()`-method](https://easystats.github.io/see/articles/performance.html) implemented in the \href{https://easystats.github.io/see/}{\pkg{see}-package}.
 #'
+#' @family checking model assumptions and quality
+#'
 #' @examples
 #' model <<- lm(len ~ supp + dose, data = ToothGrowth)
 #' check_homogeneity(model)
diff --git a/R/check_model.R b/R/check_model.R
index f057d4b49..c4f7a280d 100644
--- a/R/check_model.R
+++ b/R/check_model.R
@@ -131,6 +131,8 @@
 #' look at the `check` argument and see if some of the model checks could be
 #' skipped, which also increases performance.
 #'
+#' @family checking model assumptions and quality
+#'
 #' @examples
 #' \dontrun{
 #' m <- lm(mpg ~ wt + cyl + gear + disp, data = mtcars)
diff --git a/R/check_multimodal.R b/R/check_multimodal.R
index fb837c517..9223ee59a 100644
--- a/R/check_multimodal.R
+++ b/R/check_multimodal.R
@@ -2,7 +2,7 @@
 #'
 #' For univariate distributions (one-dimensional vectors), this functions
 #' performs a Ameijeiras-Alonso et al. (2018) excess mass test. For multivariate
-#' distributions (dataframes), it uses mixture modelling. However, it seems that
+#' distributions (data frames), it uses mixture modelling. However, it seems that
 #' it always returns a significant result (suggesting that the distribution is
 #' multimodal). A better method might be needed here.
 #'
diff --git a/R/check_outliers.R b/R/check_outliers.R
index ec25e3aa0..d02b1f145 100644
--- a/R/check_outliers.R
+++ b/R/check_outliers.R
@@ -34,6 +34,8 @@
 #'   function. Note that the function will (silently) return a vector of `FALSE`
 #'   for non-supported data types such as character strings.
 #'
+#' @family checking model assumptions and quality
+#'
 #' @note There is also a
 #'   [`plot()`-method](https://easystats.github.io/see/articles/performance.html)
 #'   implemented in the
diff --git a/R/check_overdispersion.R b/R/check_overdispersion.R
index 4661da515..a84ae9273 100644
--- a/R/check_overdispersion.R
+++ b/R/check_overdispersion.R
@@ -16,19 +16,17 @@
 #'   with the mean and, therefore, variance usually (roughly) equals the mean
 #'   value. If the variance is much higher, the data are "overdispersed".
 #'
-#' \subsection{Interpretation of the Dispersion Ratio}{
+#' @section Interpretation of the Dispersion Ratio:
 #' If the dispersion ratio is close to one, a Poisson model fits well to the
 #' data. Dispersion ratios larger than one indicate overdispersion, thus a
 #' negative binomial model or similar might fit better to the data. A p-value <
 #' .05 indicates overdispersion.
-#' }
 #'
-#' \subsection{Overdispersion in Poisson Models}{
+#' @section Overdispersion in Poisson Models:
 #' For Poisson models, the overdispersion test is based on the code from
-#' \cite{Gelman and Hill (2007), page 115}.
-#' }
+#' _Gelman and Hill (2007), page 115_.
 #'
-#' \subsection{Overdispersion in Mixed Models}{
+#' @section Overdispersion in Mixed Models:
 #' For `merMod`- and `glmmTMB`-objects, `check_overdispersion()`
 #' is based on the code in the
 #' [GLMM FAQ](http://bbolker.github.io/mixedmodels-misc/glmmFAQ.html),
@@ -36,16 +34,15 @@
 #' function only returns an *approximate* estimate of an overdispersion
 #' parameter, and is probably inaccurate for zero-inflated mixed models (fitted
 #' with `glmmTMB`).
-#' }
 #'
-#' \subsection{How to fix Overdispersion}{
+#' @section How to fix Overdispersion:
 #' Overdispersion can be fixed by either modeling the dispersion parameter, or
 #' by choosing a different distributional family (like Quasi-Poisson, or
-#' negative binomial, see \cite{Gelman and Hill (2007), pages 115-116}).
-#' }
+#' negative binomial, see _Gelman and Hill (2007), pages 115-116_).
 #'
-#' @references
+#' @family checking model assumptions and quality
 #'
+#' @references
 #' - Bolker B et al. (2017):
 #'  [GLMM FAQ.](http://bbolker.github.io/mixedmodels-misc/glmmFAQ.html)
 #'
diff --git a/R/check_singularity.R b/R/check_singularity.R
index b8cf5f52b..0891c0324 100644
--- a/R/check_singularity.R
+++ b/R/check_singularity.R
@@ -12,39 +12,39 @@
 #' @return `TRUE` if the model fit is singular.
 #'
 #' @details If a model is "singular", this means that some dimensions of the
-#'   variance-covariance matrix have been estimated as exactly zero. This
-#'   often occurs for mixed models with complex random effects structures.
-#'   \cr \cr
-#'   \dQuote{While singular models are statistically well defined (it is
-#'   theoretically sensible for the true maximum likelihood estimate to
-#'   correspond to a singular fit), there are real concerns that (1) singular
-#'   fits correspond to overfitted models that may have poor power; (2) chances
-#'   of numerical problems and mis-convergence are higher for singular models
-#'   (e.g. it may be computationally difficult to compute profile confidence
-#'   intervals for such models); (3) standard inferential procedures such as
-#'   Wald statistics and likelihood ratio tests may be inappropriate.}
-#'   (\cite{lme4 Reference Manual})
-#'   \cr \cr
-#'   There is no gold-standard about how to deal with singularity and which
-#'   random-effects specification to choose. Beside using fully Bayesian methods
-#'   (with informative priors), proposals in a frequentist framework are:
-#'  -ize{
-#'  - avoid fitting overly complex models, such that the
-#'   variance-covariance matrices can be estimated precisely enough
-#'   (\cite{Matuschek et al. 2017})
-#'  - use some form of model selection to choose a model that balances
-#'   predictive accuracy and overfitting/type I error (\cite{Bates et al. 2015},
-#'   \cite{Matuschek et al. 2017})
-#'  - \dQuote{keep it maximal}, i.e. fit the most complex model consistent
-#'   with the experimental design, removing only terms required to allow a
-#'   non-singular fit (\cite{Barr et al. 2013})
-#'   }
-#'   Note the different meaning between singularity and convergence: singularity
-#'   indicates an issue with the "true" best estimate, i.e. whether the maximum
-#'   likelihood estimation for the variance-covariance matrix of the random
-#'   effects is positive definite or only semi-definite. Convergence is a
-#'   question of whether we can assume that the numerical optimization has
-#'   worked correctly or not.
+#' variance-covariance matrix have been estimated as exactly zero. This
+#' often occurs for mixed models with complex random effects structures.
+#'
+#' "While singular models are statistically well defined (it is theoretically
+#' sensible for the true maximum likelihood estimate to correspond to a singular
+#' fit), there are real concerns that (1) singular fits correspond to overfitted
+#' models that may have poor power; (2) chances of numerical problems and
+#' mis-convergence are higher for singular models (e.g. it may be computationally
+#' difficult to compute profile confidence intervals for such models); (3)
+#' standard inferential procedures such as Wald statistics and likelihood ratio
+#' tests may be inappropriate." (_lme4 Reference Manual_)
+#'
+#' There is no gold-standard about how to deal with singularity and which
+#' random-effects specification to choose. Beside using fully Bayesian methods
+#' (with informative priors), proposals in a frequentist framework are:
+#'
+#' - avoid fitting overly complex models, such that the variance-covariance
+#'   matrices can be estimated precisely enough (_Matuschek et al. 2017_)
+#' - use some form of model selection to choose a model that balances
+#'   predictive accuracy and overfitting/type I error (_Bates et al. 2015_,
+#'   _Matuschek et al. 2017_)
+#' - "keep it maximal", i.e. fit the most complex model consistent with the
+#'   experimental design, removing only terms required to allow a non-singular
+#'   fit (_Barr et al. 2013_)
+#'
+#' Note the different meaning between singularity and convergence: singularity
+#' indicates an issue with the "true" best estimate, i.e. whether the maximum
+#' likelihood estimation for the variance-covariance matrix of the random
+#' effects is positive definite or only semi-definite. Convergence is a
+#' question of whether we can assume that the numerical optimization has
+#' worked correctly or not.
+#'
+#' @family checking model assumptions and quality
 #'
 #' @references
 #' - Bates D, Kliegl R, Vasishth S, Baayen H. Parsimonious Mixed Models.
diff --git a/R/check_zeroinflation.R b/R/check_zeroinflation.R
index 24c9435b1..5cc1f75de 100644
--- a/R/check_zeroinflation.R
+++ b/R/check_zeroinflation.R
@@ -2,22 +2,24 @@
 #' @name check_zeroinflation
 #'
 #' @description `check_zeroinflation()` checks whether count models are
-#'   over- or underfitting zeros in the outcome.
+#' over- or underfitting zeros in the outcome.
 #'
-#' @param x Fitted model of class `merMod`, `glmmTMB`, `glm`,
-#'    or `glm.nb` (package \pkg{MASS}).
+#' @param x Fitted model of class `merMod`, `glmmTMB`, `glm`, or `glm.nb`
+#' (package **MASS**).
 #' @param tolerance The tolerance for the ratio of observed and predicted
-#'    zeros to considered as over- or underfitting zeros. A ratio
-#'    between 1 +/- `tolerance` is considered as OK, while a ratio
-#'    beyond or below this threshold would indicate over- or underfitting.
+#'  zeros to considered as over- or underfitting zeros. A ratio
+#'  between 1 +/- `tolerance` is considered as OK, while a ratio
+#'  beyond or below this threshold would indicate over- or underfitting.
 #'
 #' @return A list with information about the amount of predicted and observed
-#'    zeros in the outcome, as well as the ratio between these two values.
+#'  zeros in the outcome, as well as the ratio between these two values.
 #'
 #' @details If the amount of observed zeros is larger than the amount of
-#'   predicted zeros, the model is underfitting zeros, which indicates a
-#'   zero-inflation in the data. In such cases, it is recommended to use
-#'   negative binomial or zero-inflated models.
+#' predicted zeros, the model is underfitting zeros, which indicates a
+#' zero-inflation in the data. In such cases, it is recommended to use
+#' negative binomial or zero-inflated models.
+#'
+#' @family checking model assumptions and quality
 #'
 #' @examples
 #' if (require("glmmTMB")) {
diff --git a/man/check_autocorrelation.Rd b/man/check_autocorrelation.Rd
index e29ae8892..29b91de42 100644
--- a/man/check_autocorrelation.Rd
+++ b/man/check_autocorrelation.Rd
@@ -34,3 +34,16 @@ cluster groups should be used.
 m <- lm(mpg ~ wt + cyl + gear + disp, data = mtcars)
 check_autocorrelation(m)
 }
+\seealso{
+Other checking model assumptions and quality: 
+\code{\link{check_collinearity}()},
+\code{\link{check_convergence}()},
+\code{\link{check_heteroscedasticity}()},
+\code{\link{check_homogeneity}()},
+\code{\link{check_model}()},
+\code{\link{check_outliers}()},
+\code{\link{check_overdispersion}()},
+\code{\link{check_singularity}()},
+\code{\link{check_zeroinflation}()}
+}
+\concept{checking model assumptions and quality}
diff --git a/man/check_collinearity.Rd b/man/check_collinearity.Rd
index bbb3b7567..af8a79386 100644
--- a/man/check_collinearity.Rd
+++ b/man/check_collinearity.Rd
@@ -159,3 +159,16 @@ common statistical problems: Data exploration. Methods in Ecology and
 Evolution (2010) 1:3–14.
 }
 }
+\seealso{
+Other checking model assumptions and quality: 
+\code{\link{check_autocorrelation}()},
+\code{\link{check_convergence}()},
+\code{\link{check_heteroscedasticity}()},
+\code{\link{check_homogeneity}()},
+\code{\link{check_model}()},
+\code{\link{check_outliers}()},
+\code{\link{check_overdispersion}()},
+\code{\link{check_singularity}()},
+\code{\link{check_zeroinflation}()}
+}
+\concept{checking model assumptions and quality}
diff --git a/man/check_convergence.Rd b/man/check_convergence.Rd
index 5f8886e45..ce2370847 100644
--- a/man/check_convergence.Rd
+++ b/man/check_convergence.Rd
@@ -22,14 +22,16 @@ is suspicious. Additionally, the convergence value is returned as attribute.
 \code{check_convergence()} provides an alternative convergence
 test for \code{merMod}-objects.
 }
-\details{
-\subsection{Convergence and log-likelihood}{
+\section{Convergence and log-likelihood}{
+
 Convergence problems typically arise when the model hasn't converged
 to a solution where the log-likelihood has a true maximum. This may result
 in unreliable and overly complex (or non-estimable) estimates and standard
 errors.
 }
-\subsection{Inspect model convergence}{
+
+\section{Inspect model convergence}{
+
 \strong{lme4} performs a convergence-check (see \code{?lme4::convergence}),
 however, as as discussed \href{https://github.com/lme4/lme4/issues/120}{here}
 and suggested by one of the lme4-authors in
@@ -37,7 +39,9 @@ and suggested by one of the lme4-authors in
 this check can be too strict. \code{check_convergence()} thus provides an
 alternative convergence test for \code{merMod}-objects.
 }
-\subsection{Resolving convergence issues}{
+
+\section{Resolving convergence issues}{
+
 Convergence issues are not easy to diagnose. The help page on
 \code{?lme4::convergence} provides most of the current advice about
 how to resolve convergence issues. Another clue might be large parameter
@@ -47,7 +51,9 @@ values, e.g. estimates (on the scale of the linear predictor) larger than
 Complete separation can be addressed by regularization, e.g. penalized
 regression or Bayesian regression with appropriate priors on the fixed effects.
 }
-\subsection{Convergence versus Singularity}{
+
+\section{Convergence versus Singularity}{
+
 Note the different meaning between singularity and convergence: singularity
 indicates an issue with the "true" best estimate, i.e. whether the maximum
 likelihood estimation for the variance-covariance matrix of the random effects
@@ -55,7 +61,7 @@ is positive definite or only semi-definite. Convergence is a question of
 whether we can assume that the numerical optimization has worked correctly
 or not.
 }
-}
+
 \examples{
 if (require("lme4")) {
   data(cbpp)
@@ -83,3 +89,16 @@ if (require("glmmTMB")) {
 }
 }
 }
+\seealso{
+Other checking model assumptions and quality: 
+\code{\link{check_autocorrelation}()},
+\code{\link{check_collinearity}()},
+\code{\link{check_heteroscedasticity}()},
+\code{\link{check_homogeneity}()},
+\code{\link{check_model}()},
+\code{\link{check_outliers}()},
+\code{\link{check_overdispersion}()},
+\code{\link{check_singularity}()},
+\code{\link{check_zeroinflation}()}
+}
+\concept{checking model assumptions and quality}
diff --git a/man/check_heteroscedasticity.Rd b/man/check_heteroscedasticity.Rd
index 77906cfa0..470706201 100644
--- a/man/check_heteroscedasticity.Rd
+++ b/man/check_heteroscedasticity.Rd
@@ -43,3 +43,16 @@ if (require("see")) {
 \references{
 Breusch, T. S., and Pagan, A. R. (1979) A simple test for heteroscedasticity and random coefficient variation. Econometrica 47, 1287-1294.
 }
+\seealso{
+Other checking model assumptions and quality: 
+\code{\link{check_autocorrelation}()},
+\code{\link{check_collinearity}()},
+\code{\link{check_convergence}()},
+\code{\link{check_homogeneity}()},
+\code{\link{check_model}()},
+\code{\link{check_outliers}()},
+\code{\link{check_overdispersion}()},
+\code{\link{check_singularity}()},
+\code{\link{check_zeroinflation}()}
+}
+\concept{checking model assumptions and quality}
diff --git a/man/check_homogeneity.Rd b/man/check_homogeneity.Rd
index fa775f693..68d8654dc 100644
--- a/man/check_homogeneity.Rd
+++ b/man/check_homogeneity.Rd
@@ -43,3 +43,16 @@ if (require("see")) {
   plot(result)
 }
 }
+\seealso{
+Other checking model assumptions and quality: 
+\code{\link{check_autocorrelation}()},
+\code{\link{check_collinearity}()},
+\code{\link{check_convergence}()},
+\code{\link{check_heteroscedasticity}()},
+\code{\link{check_model}()},
+\code{\link{check_outliers}()},
+\code{\link{check_overdispersion}()},
+\code{\link{check_singularity}()},
+\code{\link{check_zeroinflation}()}
+}
+\concept{checking model assumptions and quality}
diff --git a/man/check_model.Rd b/man/check_model.Rd
index 7c2c09c99..d682d8098 100644
--- a/man/check_model.Rd
+++ b/man/check_model.Rd
@@ -202,3 +202,16 @@ if (require("rstanarm")) {
 }
 }
 }
+\seealso{
+Other checking model assumptions and quality: 
+\code{\link{check_autocorrelation}()},
+\code{\link{check_collinearity}()},
+\code{\link{check_convergence}()},
+\code{\link{check_heteroscedasticity}()},
+\code{\link{check_homogeneity}()},
+\code{\link{check_outliers}()},
+\code{\link{check_overdispersion}()},
+\code{\link{check_singularity}()},
+\code{\link{check_zeroinflation}()}
+}
+\concept{checking model assumptions and quality}
diff --git a/man/check_multimodal.Rd b/man/check_multimodal.Rd
index 6e670ad85..43153734a 100644
--- a/man/check_multimodal.Rd
+++ b/man/check_multimodal.Rd
@@ -14,7 +14,7 @@ check_multimodal(x, ...)
 \description{
 For univariate distributions (one-dimensional vectors), this functions
 performs a Ameijeiras-Alonso et al. (2018) excess mass test. For multivariate
-distributions (dataframes), it uses mixture modelling. However, it seems that
+distributions (data frames), it uses mixture modelling. However, it seems that
 it always returns a significant result (suggesting that the distribution is
 multimodal). A better method might be needed here.
 }
diff --git a/man/check_outliers.Rd b/man/check_outliers.Rd
index 2e1212b2c..1552a2e7d 100644
--- a/man/check_outliers.Rd
+++ b/man/check_outliers.Rd
@@ -347,3 +347,16 @@ outliers and leverage points. Journal of the American Statistical
 association, 85(411), 633-639.
 }
 }
+\seealso{
+Other checking model assumptions and quality: 
+\code{\link{check_autocorrelation}()},
+\code{\link{check_collinearity}()},
+\code{\link{check_convergence}()},
+\code{\link{check_heteroscedasticity}()},
+\code{\link{check_homogeneity}()},
+\code{\link{check_model}()},
+\code{\link{check_overdispersion}()},
+\code{\link{check_singularity}()},
+\code{\link{check_zeroinflation}()}
+}
+\concept{checking model assumptions and quality}
diff --git a/man/check_overdispersion.Rd b/man/check_overdispersion.Rd
index 3feb5c32e..b61ecffa5 100644
--- a/man/check_overdispersion.Rd
+++ b/man/check_overdispersion.Rd
@@ -25,20 +25,23 @@ Overdispersion occurs when the observed variance is higher than the
 variance of a theoretical model. For Poisson models, variance increases
 with the mean and, therefore, variance usually (roughly) equals the mean
 value. If the variance is much higher, the data are "overdispersed".
+}
+\section{Interpretation of the Dispersion Ratio}{
 
-\subsection{Interpretation of the Dispersion Ratio}{
 If the dispersion ratio is close to one, a Poisson model fits well to the
 data. Dispersion ratios larger than one indicate overdispersion, thus a
 negative binomial model or similar might fit better to the data. A p-value <
 .05 indicates overdispersion.
 }
 
-\subsection{Overdispersion in Poisson Models}{
+\section{Overdispersion in Poisson Models}{
+
 For Poisson models, the overdispersion test is based on the code from
-\cite{Gelman and Hill (2007), page 115}.
+\emph{Gelman and Hill (2007), page 115}.
 }
 
-\subsection{Overdispersion in Mixed Models}{
+\section{Overdispersion in Mixed Models}{
+
 For \code{merMod}- and \code{glmmTMB}-objects, \code{check_overdispersion()}
 is based on the code in the
 \href{http://bbolker.github.io/mixedmodels-misc/glmmFAQ.html}{GLMM FAQ},
@@ -48,12 +51,13 @@ parameter, and is probably inaccurate for zero-inflated mixed models (fitted
 with \code{glmmTMB}).
 }
 
-\subsection{How to fix Overdispersion}{
+\section{How to fix Overdispersion}{
+
 Overdispersion can be fixed by either modeling the dispersion parameter, or
 by choosing a different distributional family (like Quasi-Poisson, or
-negative binomial, see \cite{Gelman and Hill (2007), pages 115-116}).
-}
+negative binomial, see \emph{Gelman and Hill (2007), pages 115-116}).
 }
+
 \examples{
 \dontshow{if (getRversion() >= "4.0.0" && require("glmmTMB", quietly = TRUE)) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf}
 
@@ -79,3 +83,16 @@ multilevel/hierarchical models. Cambridge; New York: Cambridge University
 Press.
 }
 }
+\seealso{
+Other checking model assumptions and quality: 
+\code{\link{check_autocorrelation}()},
+\code{\link{check_collinearity}()},
+\code{\link{check_convergence}()},
+\code{\link{check_heteroscedasticity}()},
+\code{\link{check_homogeneity}()},
+\code{\link{check_model}()},
+\code{\link{check_outliers}()},
+\code{\link{check_singularity}()},
+\code{\link{check_zeroinflation}()}
+}
+\concept{checking model assumptions and quality}
diff --git a/man/check_singularity.Rd b/man/check_singularity.Rd
index e5f897cc0..1fe0aec0d 100644
--- a/man/check_singularity.Rd
+++ b/man/check_singularity.Rd
@@ -25,32 +25,30 @@ Check mixed models for boundary fits.
 If a model is "singular", this means that some dimensions of the
 variance-covariance matrix have been estimated as exactly zero. This
 often occurs for mixed models with complex random effects structures.
-\cr \cr
-\dQuote{While singular models are statistically well defined (it is
-theoretically sensible for the true maximum likelihood estimate to
-correspond to a singular fit), there are real concerns that (1) singular
-fits correspond to overfitted models that may have poor power; (2) chances
-of numerical problems and mis-convergence are higher for singular models
-(e.g. it may be computationally difficult to compute profile confidence
-intervals for such models); (3) standard inferential procedures such as
-Wald statistics and likelihood ratio tests may be inappropriate.}
-(\cite{lme4 Reference Manual})
-\cr \cr
+
+"While singular models are statistically well defined (it is theoretically
+sensible for the true maximum likelihood estimate to correspond to a singular
+fit), there are real concerns that (1) singular fits correspond to overfitted
+models that may have poor power; (2) chances of numerical problems and
+mis-convergence are higher for singular models (e.g. it may be computationally
+difficult to compute profile confidence intervals for such models); (3)
+standard inferential procedures such as Wald statistics and likelihood ratio
+tests may be inappropriate." (\emph{lme4 Reference Manual})
+
 There is no gold-standard about how to deal with singularity and which
 random-effects specification to choose. Beside using fully Bayesian methods
 (with informative priors), proposals in a frequentist framework are:
--ize{
 \itemize{
-\item avoid fitting overly complex models, such that the
-variance-covariance matrices can be estimated precisely enough
-(\cite{Matuschek et al. 2017})
+\item avoid fitting overly complex models, such that the variance-covariance
+matrices can be estimated precisely enough (\emph{Matuschek et al. 2017})
 \item use some form of model selection to choose a model that balances
-predictive accuracy and overfitting/type I error (\cite{Bates et al. 2015},
-\cite{Matuschek et al. 2017})
-\item \dQuote{keep it maximal}, i.e. fit the most complex model consistent
-with the experimental design, removing only terms required to allow a
-non-singular fit (\cite{Barr et al. 2013})
+predictive accuracy and overfitting/type I error (\emph{Bates et al. 2015},
+\emph{Matuschek et al. 2017})
+\item "keep it maximal", i.e. fit the most complex model consistent with the
+experimental design, removing only terms required to allow a non-singular
+fit (\emph{Barr et al. 2013})
 }
+
 Note the different meaning between singularity and convergence: singularity
 indicates an issue with the "true" best estimate, i.e. whether the maximum
 likelihood estimation for the variance-covariance matrix of the random
@@ -58,7 +56,6 @@ effects is positive definite or only semi-definite. Convergence is a
 question of whether we can assume that the numerical optimization has
 worked correctly or not.
 }
-}
 \examples{
 \dontshow{if (require("lme4")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf}
 library(lme4)
@@ -93,3 +90,16 @@ I error and power in linear mixed models. Journal of Memory and Language,
 \item lme4 Reference Manual, \url{https://cran.r-project.org/package=lme4}
 }
 }
+\seealso{
+Other checking model assumptions and quality: 
+\code{\link{check_autocorrelation}()},
+\code{\link{check_collinearity}()},
+\code{\link{check_convergence}()},
+\code{\link{check_heteroscedasticity}()},
+\code{\link{check_homogeneity}()},
+\code{\link{check_model}()},
+\code{\link{check_outliers}()},
+\code{\link{check_overdispersion}()},
+\code{\link{check_zeroinflation}()}
+}
+\concept{checking model assumptions and quality}
diff --git a/man/check_zeroinflation.Rd b/man/check_zeroinflation.Rd
index 90744e1f1..2460bf2a7 100644
--- a/man/check_zeroinflation.Rd
+++ b/man/check_zeroinflation.Rd
@@ -7,8 +7,8 @@
 check_zeroinflation(x, tolerance = 0.05)
 }
 \arguments{
-\item{x}{Fitted model of class \code{merMod}, \code{glmmTMB}, \code{glm},
-or \code{glm.nb} (package \pkg{MASS}).}
+\item{x}{Fitted model of class \code{merMod}, \code{glmmTMB}, \code{glm}, or \code{glm.nb}
+(package \strong{MASS}).}
 
 \item{tolerance}{The tolerance for the ratio of observed and predicted
 zeros to considered as over- or underfitting zeros. A ratio
@@ -36,3 +36,16 @@ if (require("glmmTMB")) {
   check_zeroinflation(m)
 }
 }
+\seealso{
+Other checking model assumptions and quality: 
+\code{\link{check_autocorrelation}()},
+\code{\link{check_collinearity}()},
+\code{\link{check_convergence}()},
+\code{\link{check_heteroscedasticity}()},
+\code{\link{check_homogeneity}()},
+\code{\link{check_model}()},
+\code{\link{check_outliers}()},
+\code{\link{check_overdispersion}()},
+\code{\link{check_singularity}()}
+}
+\concept{checking model assumptions and quality}