docs

DESCRIPTION

@@ -154,4 +154,4 @@ Config/Needs/website:
     r-lib/pkgdown,
     easystats/easystatstemplate
 Config/rcmdcheck/ignore-inconsequential-notes: true
-Remotes: easystats/insight
+Remotes: easystats/insight

R/icc.R

@@ -3,7 +3,7 @@
 #' This function calculates the intraclass-correlation coefficient (ICC) -
 #' sometimes also called *variance partition coefficient* (VPC) or
 #' *repeatability* - for mixed effects models. The ICC can be calculated for all
-#' models supported by `insight::get_variance()`. For models fitted with the
+#' models supported by [`insight::get_variance()`]. For models fitted with the
 #' **brms**-package, `icc()` might fail due to the large variety of
 #' models and families supported by the **brms**-package. In such cases, an
 #' alternative to the ICC is the `variance_decomposition()`, which is based
@@ -63,6 +63,8 @@
 #'    applications and data analysis methods (2nd ed). Thousand Oaks: Sage
 #'    Publications.
 #'
+#' @inheritSection r2_nakagawa Supported models and model families
+#'
 #' @details
 #' ## Interpretation
 #' The ICC can be interpreted as "the proportion of the variance explained by

R/r2_nakagawa.R

@@ -18,6 +18,32 @@
 #'
 #' @return A list with the conditional and marginal R2 values.
 #'
+#' @section Supported models and model families:
+#' The single variance components that are required to calculate the marginal
+#' and conditional r-squared values are calculated using the [`insight::get_variance()`]
+#' function. The results are validated against the solutions provided by
+#' _Nakagawa et al. (2017)_, in particular examples shown in the Supplement 2
+#' of the paper. Other model families are validated against results from the
+#' **MuMIn*+ package. This means that the r-squared values returned by `r2_nakagawa()`
+#' should be accurate and reliable for following mixed models or model families:
+#'
+#' - Bernoulli (logistic) regression
+#' - Binomial regression (with other than binary outcomes)
+#' - Poisson and Quasi-Poisson regression
+#' - Negative binomial regression (including nbinom1 and nbinom2 families)
+#' - Gaussian regression (linear models)
+#' - Gamma regression
+#' - Tweedie regression
+#' - Beta regression
+#' - Ordered beta regression
+#'
+#' Following model families are not yet validated, but should work:
+#'
+#' - Zero-inflated and hurdle models
+#' - Beta-binomial regression
+#' - Compound Poisson regression
+#' - Generalized Poisson regression
+#'
 #' @details
 #' Marginal and conditional r-squared values for mixed models are calculated
 #' based on _Nakagawa et al. (2017)_. For more details on the computation of