diff --git a/DESCRIPTION b/DESCRIPTION
index 8c94596fa..de25c80fe 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,7 +1,7 @@
 Type: Package
 Package: parameters
 Title: Processing of Model Parameters
-Version: 0.28.2.5
+Version: 0.28.2.7
 Authors@R:
     c(person(given = "Daniel",
              family = "Lüdecke",
diff --git a/NAMESPACE b/NAMESPACE
index 159573895..7b593c795 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -243,11 +243,11 @@ S3method(model_parameters,draws)
 S3method(model_parameters,emmGrid)
 S3method(model_parameters,emm_list)
 S3method(model_parameters,epi.2by2)
-S3method(model_parameters,externVar)
-S3method(model_parameters,externX)
 S3method(model_parameters,estimate_contrasts)
 S3method(model_parameters,estimate_means)
 S3method(model_parameters,estimate_slopes)
+S3method(model_parameters,externVar)
+S3method(model_parameters,externX)
 S3method(model_parameters,fa)
 S3method(model_parameters,fa.ci)
 S3method(model_parameters,feglm)
@@ -360,6 +360,7 @@ S3method(model_parameters,summary_emm)
 S3method(model_parameters,survfit)
 S3method(model_parameters,svy2lme)
 S3method(model_parameters,svyglm)
+S3method(model_parameters,svyolr)
 S3method(model_parameters,svytable)
 S3method(model_parameters,systemfit)
 S3method(model_parameters,t1way)
@@ -803,11 +804,11 @@ S3method(standard_error,draws)
 S3method(standard_error,effectsize_table)
 S3method(standard_error,emmGrid)
 S3method(standard_error,emm_list)
-S3method(standard_error,externVar)
-S3method(standard_error,externX)
 S3method(standard_error,estimate_contrasts)
 S3method(standard_error,estimate_means)
 S3method(standard_error,estimate_slopes)
+S3method(standard_error,externVar)
+S3method(standard_error,externX)
 S3method(standard_error,factor)
 S3method(standard_error,feglm)
 S3method(standard_error,fitdistr)
diff --git a/R/1_model_parameters.R b/R/1_model_parameters.R
index fc632fe92..ec9cdd4b9 100644
--- a/R/1_model_parameters.R
+++ b/R/1_model_parameters.R
@@ -396,7 +396,6 @@ model_parameters <- function(model, ...) {
 
 # DF naming convention --------------------
 
-
 # DF column naming
 # F has df, df_error
 # t has df_error
@@ -404,7 +403,6 @@ model_parameters <- function(model, ...) {
 # Chisq has df
 # https://github.com/easystats/parameters/issues/455
 
-
 # Options -------------------------------------
 
 # Add new options to the docs in "print.parameters_model"
@@ -417,7 +415,6 @@ model_parameters <- function(model, ...) {
 # getOption("parameters_interaction"): separator char for interactions
 # getOption("parameters_select"): default for the `select` argument
 
-
 #' @rdname model_parameters
 #' @export
 parameters <- model_parameters
@@ -578,21 +575,23 @@ parameters <- model_parameters
 #' }
 #' @return A data frame of indices related to the model's parameters.
 #' @export
-model_parameters.default <- function(model,
-                                     ci = 0.95,
-                                     ci_method = NULL,
-                                     bootstrap = FALSE,
-                                     iterations = 1000,
-                                     standardize = NULL,
-                                     exponentiate = FALSE,
-                                     p_adjust = NULL,
-                                     vcov = NULL,
-                                     vcov_args = NULL,
-                                     include_info = getOption("parameters_info", FALSE),
-                                     keep = NULL,
-                                     drop = NULL,
-                                     verbose = TRUE,
-                                     ...) {
+model_parameters.default <- function(
+  model,
+  ci = 0.95,
+  ci_method = NULL,
+  bootstrap = FALSE,
+  iterations = 1000,
+  standardize = NULL,
+  exponentiate = FALSE,
+  p_adjust = NULL,
+  vcov = NULL,
+  vcov_args = NULL,
+  include_info = getOption("parameters_info", FALSE),
+  keep = NULL,
+  drop = NULL,
+  verbose = TRUE,
+  ...
+) {
   # validation check for inputs
   .is_model_valid(model)
 
@@ -642,13 +641,11 @@ model_parameters.default <- function(model,
       attr(out, "error")
     )
   } else if (is.null(out)) {
-    insight::format_error(
-      paste0(
-        "Sorry, `model_parameters()` does not currently work for objects of class `",
-        class(model)[1],
-        "`."
-      )
-    )
+    insight::format_error(paste0(
+      "Sorry, `model_parameters()` does not currently work for objects of class `",
+      class(model)[1],
+      "`."
+    ))
   }
 }
 
@@ -657,24 +654,26 @@ model_parameters.default <- function(model,
 # including a bunch of attributes required for further processing
 # (like printing etc.)
 
-.model_parameters_generic <- function(model,
-                                      ci = 0.95,
-                                      bootstrap = FALSE,
-                                      iterations = 1000,
-                                      merge_by = "Parameter",
-                                      standardize = NULL,
-                                      exponentiate = FALSE,
-                                      effects = "fixed",
-                                      component = "conditional",
-                                      ci_method = NULL,
-                                      p_adjust = NULL,
-                                      include_info = FALSE,
-                                      keep_parameters = NULL,
-                                      drop_parameters = NULL,
-                                      vcov = NULL,
-                                      vcov_args = NULL,
-                                      verbose = TRUE,
-                                      ...) {
+.model_parameters_generic <- function(
+  model,
+  ci = 0.95,
+  bootstrap = FALSE,
+  iterations = 1000,
+  merge_by = "Parameter",
+  standardize = NULL,
+  exponentiate = FALSE,
+  effects = "fixed",
+  component = "conditional",
+  ci_method = NULL,
+  p_adjust = NULL,
+  include_info = FALSE,
+  keep_parameters = NULL,
+  drop_parameters = NULL,
+  vcov = NULL,
+  vcov_args = NULL,
+  verbose = TRUE,
+  ...
+) {
   dots <- list(...)
 
   out <- tryCatch(
@@ -688,12 +687,7 @@ model_parameters.default <- function(model,
           ci_method <- "quantile"
         }
 
-        fun_args <- list(
-          model,
-          iterations = iterations,
-          ci = ci,
-          ci_method = ci_method
-        )
+        fun_args <- list(model, iterations = iterations, ci = ci, ci_method = ci_method)
         fun_args <- c(fun_args, dots)
         params <- do.call("bootstrap_parameters", fun_args)
 
@@ -723,13 +717,11 @@ model_parameters.default <- function(model,
         params <- do.call(".extract_parameters_generic", fun_args)
       }
 
-
       # ==== 2. second step, exponentiate -------
 
       # exponentiate coefficients and SE/CI, if requested
       params <- .exponentiate_parameters(params, model, exponentiate)
 
-
       # ==== 3. third step, add information as attributes -------
 
       # add further information as attributes
@@ -766,30 +758,31 @@ model_parameters.default <- function(model,
 
 #################### .glm ----------------------
 
-
 #' @export
-model_parameters.glm <- function(model,
-                                 ci = 0.95,
-                                 ci_method = NULL,
-                                 bootstrap = FALSE,
-                                 iterations = 1000,
-                                 standardize = NULL,
-                                 exponentiate = FALSE,
-                                 p_adjust = NULL,
-                                 vcov = NULL,
-                                 vcov_args = NULL,
-                                 include_info = getOption("parameters_info", FALSE),
-                                 keep = NULL,
-                                 drop = NULL,
-                                 verbose = TRUE,
-                                 ...) {
+model_parameters.glm <- function(
+  model,
+  ci = 0.95,
+  ci_method = NULL,
+  bootstrap = FALSE,
+  iterations = 1000,
+  standardize = NULL,
+  exponentiate = FALSE,
+  p_adjust = NULL,
+  vcov = NULL,
+  vcov_args = NULL,
+  include_info = getOption("parameters_info", FALSE),
+  keep = NULL,
+  drop = NULL,
+  verbose = TRUE,
+  ...
+) {
   dots <- list(...)
 
   # set default
   if (is.null(ci_method)) {
     if (isTRUE(bootstrap)) {
       ci_method <- "quantile"
-    } else if (!is.null(vcov) || !is.null(vcov_args)) {
+    } else if (!is.null(vcov) || !is.null(vcov_args) || inherits(model, "svyolr")) {
       ci_method <- "wald"
     } else {
       ci_method <- "profile"
@@ -805,7 +798,11 @@ model_parameters.glm <- function(model,
   }
 
   # tell user that profiled CIs don't respect vcov-args
-  if (identical(ci_method, "profile") && (!is.null(vcov) || !is.null(vcov_args)) && isTRUE(verbose)) {
+  if (
+    identical(ci_method, "profile") &&
+      (!is.null(vcov) || !is.null(vcov_args)) &&
+      isTRUE(verbose)
+  ) {
     insight::format_alert(
       "When `ci_method=\"profile\"`, `vcov` only modifies standard errors, test-statistic and p-values, but not confidence intervals.", # nolint
       "Use `ci_method=\"wald\"` to return confidence intervals based on robust standard errors."
diff --git a/R/dof.R b/R/dof.R
index fb777adde..cb84690aa 100644
--- a/R/dof.R
+++ b/R/dof.R
@@ -80,7 +80,8 @@ dof <- degrees_of_freedom
   method <- tolower(method)
 
   # exceptions 1
-  if (inherits(model, c("polr", "glm", "svyglm"))) {
+  if (inherits(model, c("polr", "glm", "svyglm", "svyolr"))) {
+    # fmt: skip
     if (method %in% c(
       "analytical", "any", "fit", "profile", "residual",
       "wald", "nokr", "likelihood", "normal"
@@ -96,11 +97,17 @@ dof <- degrees_of_freedom
 
   # exceptions 2
   if (inherits(model, c("phylolm", "phyloglm"))) {
-    if (method %in% c("analytical", "any", "fit", "residual", "wald", "nokr", "normal", "boot")) {
+    if (
+      method %in%
+        c("analytical", "any", "fit", "residual", "wald", "nokr", "normal", "boot")
+    ) {
       return(TRUE)
     } else {
       if (verbose) {
-        insight::format_alert(sprintf("`%s` must be one of \"wald\", \"normal\" or \"boot\". Using \"wald\" now.", type)) # nolint
+        insight::format_alert(sprintf(
+          "`%s` must be one of \"wald\", \"normal\" or \"boot\". Using \"wald\" now.",
+          type
+        ))
       }
       return(FALSE)
     }
@@ -109,31 +116,52 @@ dof <- degrees_of_freedom
   info <- insight::model_info(model, verbose = FALSE)
   if (!is.null(info) && isFALSE(info$is_mixed) && method == "boot") {
     if (verbose) {
-      insight::format_alert(sprintf("`%s=boot` only works for mixed models of class `merMod`. To bootstrap this model, use `bootstrap=TRUE, ci_method=\"bcai\"`.", type)) # nolint
+      insight::format_alert(sprintf(
+        "`%s=boot` only works for mixed models of class `merMod`. To bootstrap this model, use `bootstrap=TRUE, ci_method=\"bcai\"`.",
+        type
+      ))
     }
     return(TRUE)
   }
 
+  # fmt: skip
   if (is.null(info) || !info$is_mixed) {
-    if (!(method %in% c("analytical", "any", "fit", "betwithin", "nokr", "wald", "ml1", "profile", "boot", "uniroot", "residual", "normal"))) { # nolint
+    if (!(method %in% c(
+      "analytical", "any", "fit", "betwithin", "nokr", "wald", "ml1",
+      "profile", "boot", "uniroot", "residual", "normal"
+    ))) {
       if (verbose) {
-        insight::format_alert(sprintf("`%s` must be one of \"residual\", \"wald\", \"normal\", \"profile\", \"boot\", \"uniroot\", \"betwithin\" or \"ml1\". Using \"wald\" now.", type)) # nolint
+        insight::format_alert(sprintf(
+          "`%s` must be one of \"residual\", \"wald\", \"normal\", \"profile\", \"boot\", \"uniroot\", \"betwithin\" or \"ml1\". Using \"wald\" now.",
+          type
+        ))
       }
       return(FALSE)
     }
     return(TRUE)
   }
 
-  if (!(method %in% c("analytical", "any", "fit", "satterthwaite", "betwithin", "kenward", "kr", "nokr", "wald", "ml1", "profile", "boot", "uniroot", "residual", "normal"))) { # nolint
+  # fmt: skip
+  if (!(method %in% c(
+    "analytical", "any", "fit", "satterthwaite", "betwithin", "kenward",
+    "kr", "nokr", "wald", "ml1", "profile", "boot", "uniroot", "residual",
+    "normal"
+  ))) {
     if (verbose) {
-      insight::format_alert(sprintf("`%s` must be one of \"residual\", \"wald\", \"normal\", \"profile\", \"boot\", \"uniroot\", \"kenward\", \"satterthwaite\", \"betwithin\" or \"ml1\". Using \"wald\" now.", type)) # nolint
+      insight::format_alert(sprintf(
+        "`%s` must be one of \"residual\", \"wald\", \"normal\", \"profile\", \"boot\", \"uniroot\", \"kenward\", \"satterthwaite\", \"betwithin\" or \"ml1\". Using \"wald\" now.",
+        type
+      ))
     }
     return(FALSE)
-  }
+}
 
   if (!info$is_linear && method %in% c("satterthwaite", "kenward", "kr")) {
     if (verbose) {
-      insight::format_alert(sprintf("`%s`-degrees of freedoms are only available for linear mixed models.", method))
+      insight::format_alert(sprintf(
+        "`%s`-degrees of freedoms are only available for linear mixed models.",
+        method
+      ))
     }
     return(FALSE)
   }
diff --git a/R/extract_parameters.R b/R/extract_parameters.R
index 347203320..cb4855446 100644
--- a/R/extract_parameters.R
+++ b/R/extract_parameters.R
@@ -80,9 +80,10 @@
   # ==== for ordinal models, first, clean parameter names and then indicate
   #      intercepts (alpha-coefficients) in the component column
 
-  if (inherits(model, "polr")) {
+  if (inherits(model, c("polr", "svyolr"))) {
     intercept_groups <- grep("Intercept:", parameters$Parameter, fixed = TRUE)
     parameters$Parameter <- gsub("Intercept: ", "", parameters$Parameter, fixed = TRUE)
+    statistic$Parameter <- gsub("Intercept: ", "", statistic$Parameter, fixed = TRUE)
   } else if (inherits(model, "clm") && !is.null(model$alpha)) {
     intercept_groups <- rep(
       c("intercept", "location", "scale"),
@@ -190,7 +191,9 @@
   } else {
     df_error <- insight::get_df(x = model, type = ci_method, verbose = FALSE)
   }
-  if (!is.null(df_error) && (length(df_error) == 1 || length(df_error) == nrow(parameters))) {
+  if (
+    !is.null(df_error) && (length(df_error) == 1 || length(df_error) == nrow(parameters))
+  ) {
     if (length(df_error) == 1) {
       parameters$df_error <- df_error
     } else {
@@ -215,18 +218,30 @@
         colnames(parameters),
         fixed = TRUE
       )
-      colnames(parameters) <- gsub("chi-squared", "Chi2", colnames(parameters), fixed = TRUE)
+      colnames(parameters) <- gsub(
+        "chi-squared",
+        "Chi2",
+        colnames(parameters),
+        fixed = TRUE
+      )
     }
   }
   colnames(parameters) <- gsub("(c|C)hisq", "Chi2", colnames(parameters))
-  colnames(parameters) <- gsub("Estimate", "Coefficient", colnames(parameters), fixed = TRUE)
+  colnames(parameters) <- gsub(
+    "Estimate",
+    "Coefficient",
+    colnames(parameters),
+    fixed = TRUE
+  )
 
   # ==== add intercept groups for ordinal models
 
-  if (inherits(model, "polr") && !is.null(intercept_groups)) {
+  if (inherits(model, c("polr", "svyolr")) && !is.null(intercept_groups)) {
     parameters$Component <- "beta"
     parameters$Component[intercept_groups] <- "alpha"
-  } else if (inherits(model, c("clm", "clm2", "ordinal_weightit")) && !is.null(intercept_groups)) {
+  } else if (
+    inherits(model, c("clm", "clm2", "ordinal_weightit")) && !is.null(intercept_groups)
+  ) {
     parameters$Component <- intercept_groups
   }
 
@@ -318,7 +333,6 @@
 
 # helper ----------------
 
-
 .add_sigma_residual_df <- function(params, model) {
   if (is.null(params$Component) || !"sigma" %in% params$Component) {
     sig <- .safe(suppressWarnings(insight::get_sigma(model, ci = NULL, verbose = FALSE)))
@@ -334,7 +348,8 @@
 .filter_parameters <- function(params, keep = NULL, drop = NULL, verbose = TRUE) {
   if (!is.null(keep) && is.list(keep)) {
     for (i in names(keep)) {
-      params <- .filter_parameters_vector(params,
+      params <- .filter_parameters_vector(
+        params,
         keep[[i]],
         drop = NULL,
         column = i,
@@ -342,7 +357,8 @@
       )
     }
   } else {
-    params <- .filter_parameters_vector(params,
+    params <- .filter_parameters_vector(
+      params,
       keep,
       drop,
       column = NULL,
@@ -353,18 +369,21 @@
 }
 
 
-.filter_parameters_vector <- function(params,
-                                      keep = NULL,
-                                      drop = NULL,
-                                      column = NULL,
-                                      verbose = TRUE) {
+.filter_parameters_vector <- function(
+  params,
+  keep = NULL,
+  drop = NULL,
+  column = NULL,
+  verbose = TRUE
+) {
   # check pattern
   if (!is.null(keep) && length(keep) > 1) {
     keep <- paste0("(", paste(keep, collapse = "|"), ")")
     if (verbose) {
-      insight::format_alert(
-        sprintf("The `keep` argument has more than 1 element. Merging into following regular expression: `%s`.", keep)
-      )
+      insight::format_alert(sprintf(
+        "The `keep` argument has more than 1 element. Merging into following regular expression: `%s`.",
+        keep
+      ))
     }
   }
 
@@ -372,9 +391,10 @@
   if (!is.null(drop) && length(drop) > 1) {
     drop <- paste0("(", paste(drop, collapse = "|"), ")")
     if (verbose) {
-      insight::format_alert(
-        sprintf("The `drop` argument has more than 1 element. Merging into following regular expression: `%s`.", drop)
-      )
+      insight::format_alert(sprintf(
+        "The `drop` argument has more than 1 element. Merging into following regular expression: `%s`.",
+        drop
+      ))
     }
   }
 
@@ -399,7 +419,6 @@
     rows_to_drop <- !grepl(drop, params[[column]], perl = TRUE)
   }
 
-
   out <- params[rows_to_keep & rows_to_drop, ]
 
   if (nrow(out) == 0) {
@@ -417,28 +436,34 @@
 
 # mixed models function ------------------------------------------------------
 
-
 #' @keywords internal
-.extract_parameters_mixed <- function(model,
-                                      ci = 0.95,
-                                      ci_method = "wald",
-                                      standardize = NULL,
-                                      p_adjust = NULL,
-                                      wb_component = FALSE,
-                                      keep_parameters = NULL,
-                                      drop_parameters = NULL,
-                                      include_sigma = FALSE,
-                                      include_info = FALSE,
-                                      vcov = NULL,
-                                      vcov_args = NULL,
-                                      verbose = TRUE,
-                                      ...) {
+.extract_parameters_mixed <- function(
+  model,
+  ci = 0.95,
+  ci_method = "wald",
+  standardize = NULL,
+  p_adjust = NULL,
+  wb_component = FALSE,
+  keep_parameters = NULL,
+  drop_parameters = NULL,
+  include_sigma = FALSE,
+  include_info = FALSE,
+  vcov = NULL,
+  vcov_args = NULL,
+  verbose = TRUE,
+  ...
+) {
   dots <- list(...)
 
   special_ci_methods <- c("betwithin", "satterthwaite", "ml1", "kenward", "kr")
 
   # get parameters and statistic
-  parameters <- insight::get_parameters(model, effects = "fixed", component = "all", verbose = FALSE)
+  parameters <- insight::get_parameters(
+    model,
+    effects = "fixed",
+    component = "all",
+    verbose = FALSE
+  )
   statistic <- insight::get_statistic(model, component = "all")
 
   # check if all estimates are non-NA
@@ -448,12 +473,15 @@
   original_order <- parameters$.id <- seq_len(nrow(parameters))
 
   # remove SE column
-  parameters <- datawizard::data_remove(parameters, c("SE", "Std. Error"), verbose = FALSE)
+  parameters <- datawizard::data_remove(
+    parameters,
+    c("SE", "Std. Error"),
+    verbose = FALSE
+  )
 
   # column name for coefficients, non-standardized
   coef_col <- "Coefficient"
 
-
   # Degrees of freedom
   if (.dof_method_ok(model, ci_method)) {
     dof <- insight::get_df(x = model, type = ci_method, verbose = FALSE)
@@ -469,14 +497,14 @@
   # for KR-dof, we have the SE as well, to save computation time
   df_error$SE <- attr(dof, "se", exact = TRUE)
 
-
   # CI - only if we don't already have CI for std. parameters
 
   ci_cols <- NULL
   if (!is.null(ci)) {
     # HC vcov?
     if (!is.null(vcov)) {
-      fun_args <- list(model,
+      fun_args <- list(
+        model,
         ci = ci,
         vcov = vcov,
         vcov_args = vcov_args,
@@ -497,15 +525,10 @@
     parameters <- merge(parameters, ci_df, by = "Parameter", sort = FALSE)
   }
 
-
   # standard error - only if we don't already have SE for std. parameters
   if (!"SE" %in% colnames(parameters)) {
     if (!is.null(vcov)) {
-      fun_args <- list(model,
-        vcov = vcov,
-        vcov_args = vcov_args,
-        verbose = verbose
-      )
+      fun_args <- list(model, vcov = vcov, vcov_args = vcov_args, verbose = verbose)
       fun_args <- c(fun_args, dots)
       parameters <- merge(
         parameters,
@@ -528,14 +551,9 @@
     }
   }
 
-
   # p value
   if (!is.null(vcov)) {
-    fun_args <- list(model,
-      vcov = vcov,
-      vcov_args = vcov_args,
-      verbose = verbose
-    )
+    fun_args <- list(model, vcov = vcov, vcov_args = vcov_args, verbose = verbose)
     fun_args <- c(fun_args, dots)
     parameters <- merge(
       parameters,
@@ -562,7 +580,6 @@
     )
   }
 
-
   # adjust standard errors and test-statistic as well
   if ((!is.null(vcov) || ci_method %in% special_ci_methods)) {
     parameters$Statistic <- parameters$Estimate / parameters$SE
@@ -570,7 +587,6 @@
     parameters <- merge(parameters, statistic, by = "Parameter", sort = FALSE)
   }
 
-
   # dof
   if (!"df" %in% names(parameters)) {
     if (!ci_method %in% special_ci_methods) {
@@ -589,7 +605,6 @@
     }
   }
 
-
   # Rematch order after merging
   parameters <- parameters[match(original_order, parameters$.id), ]
 
@@ -601,13 +616,19 @@
     fixed = TRUE
   )
   colnames(parameters) <- gsub("Std. Error", "SE", colnames(parameters), fixed = TRUE)
-  colnames(parameters) <- gsub("Estimate", "Coefficient", colnames(parameters), fixed = TRUE)
+  colnames(parameters) <- gsub(
+    "Estimate",
+    "Coefficient",
+    colnames(parameters),
+    fixed = TRUE
+  )
   colnames(parameters) <- gsub("t value", "t", colnames(parameters), fixed = TRUE)
   colnames(parameters) <- gsub("z value", "z", colnames(parameters), fixed = TRUE)
 
   # filter parameters, if requested
   if (!is.null(keep_parameters) || !is.null(drop_parameters)) {
-    parameters <- .filter_parameters(parameters,
+    parameters <- .filter_parameters(
+      parameters,
       keep = keep_parameters,
       drop = drop_parameters,
       verbose = verbose
@@ -645,19 +666,18 @@
   }
 
   # Reorder
+  # fmt: skip
   col_order <- c(
     "Parameter", coef_col, "SE", ci_cols, "t", "z", "df", "df_error", "p",
     "Component"
   )
   parameters <- parameters[col_order[col_order %in% colnames(parameters)]]
 
-
   # add sigma
   if (isTRUE(include_sigma) || isTRUE(include_info)) {
     parameters <- .add_sigma_residual_df(parameters, model)
   }
 
-
   rownames(parameters) <- NULL
   parameters
 }
@@ -684,22 +704,18 @@
   }
 
   if (!is.null(within_effects)) {
-    index <- unique(unlist(sapply(
-      within_effects,
-      grep,
-      x = parameters$Parameter,
-      fixed = TRUE
-    ), use.names = FALSE))
+    index <- unique(unlist(
+      sapply(within_effects, grep, x = parameters$Parameter, fixed = TRUE),
+      use.names = FALSE
+    ))
     parameters$Component[index] <- "within"
   }
 
   if (!is.null(between_effects)) {
-    index <- unique(unlist(sapply(
-      between_effects,
-      grep,
-      x = parameters$Parameter,
-      fixed = TRUE
-    ), use.names = FALSE))
+    index <- unique(unlist(
+      sapply(between_effects, grep, x = parameters$Parameter, fixed = TRUE),
+      use.names = FALSE
+    ))
     parameters$Component[index] <- "between"
   }
 
@@ -708,8 +724,11 @@
     parameters$Component[interactions] <- "interactions"
   }
 
-  if (((!all(c("within", "between") %in% parameters$Component)) && inherits(model, "merMod")) ||
-    all(parameters$Component == "rewb-contextual")) {
+  if (
+    ((!all(c("within", "between") %in% parameters$Component)) &&
+      inherits(model, "merMod")) ||
+      all(parameters$Component == "rewb-contextual")
+  ) {
     parameters$Component <- NULL
   }
 
@@ -719,34 +738,38 @@
 
 .find_within_between <- function(model, which_effect) {
   mf <- stats::model.frame(model)
-  unlist(sapply(names(mf), function(i) {
-    if (!is.null(attr(mf[[i]], which_effect, exact = TRUE))) {
-      i
-    }
-  }), use.names = FALSE)
+  unlist(
+    sapply(names(mf), function(i) {
+      if (!is.null(attr(mf[[i]], which_effect, exact = TRUE))) {
+        i
+      }
+    }),
+    use.names = FALSE
+  )
 }
 
 
 # Bayes function ------------------------------------------------------
 
-
 #' @keywords internal
-.extract_parameters_bayesian <- function(model,
-                                         centrality = "median",
-                                         dispersion = FALSE,
-                                         ci = 0.95,
-                                         ci_method = "eti",
-                                         test = "pd",
-                                         rope_range = "default",
-                                         rope_ci = 0.95,
-                                         bf_prior = NULL,
-                                         diagnostic = c("ESS", "Rhat"),
-                                         priors = FALSE,
-                                         standardize = NULL,
-                                         keep_parameters = NULL,
-                                         drop_parameters = NULL,
-                                         verbose = TRUE,
-                                         ...) {
+.extract_parameters_bayesian <- function(
+  model,
+  centrality = "median",
+  dispersion = FALSE,
+  ci = 0.95,
+  ci_method = "eti",
+  test = "pd",
+  rope_range = "default",
+  rope_ci = 0.95,
+  bf_prior = NULL,
+  diagnostic = c("ESS", "Rhat"),
+  priors = FALSE,
+  standardize = NULL,
+  keep_parameters = NULL,
+  drop_parameters = NULL,
+  verbose = TRUE,
+  ...
+) {
   # no ROPE for multi-response models
   if (insight::is_multivariate(model) && any(c("rope", "p_rope") %in% test)) {
     test <- setdiff(test, c("rope", "p_rope"))
@@ -811,7 +834,10 @@
 
       parameters <- merge(
         std_parameters,
-        parameters[c("Parameter", setdiff(colnames(parameters), colnames(std_parameters)))],
+        parameters[c(
+          "Parameter",
+          setdiff(colnames(parameters), colnames(std_parameters))
+        )],
         sort = FALSE
       )
     }
@@ -822,10 +848,18 @@
   }
 
   # Remove unnecessary columns
-  if ("CI" %in% names(parameters) && insight::has_single_value(parameters$CI, remove_na = TRUE)) {
+  if (
+    "CI" %in%
+      names(parameters) &&
+      insight::has_single_value(parameters$CI, remove_na = TRUE)
+  ) {
     parameters$CI <- NULL
   }
-  if ("ROPE_CI" %in% names(parameters) && insight::has_single_value(parameters$ROPE_CI, remove_na = TRUE)) {
+  if (
+    "ROPE_CI" %in%
+      names(parameters) &&
+      insight::has_single_value(parameters$ROPE_CI, remove_na = TRUE)
+  ) {
     parameters$ROPE_CI <- NULL
   }
   if ("ROPE_low" %in% names(parameters) && "ROPE_high" %in% names(parameters)) {
@@ -835,7 +869,8 @@
 
   # filter parameters, if requested
   if (!is.null(keep_parameters) || !is.null(drop_parameters)) {
-    parameters <- .filter_parameters(parameters,
+    parameters <- .filter_parameters(
+      parameters,
       keep = keep_parameters,
       drop = drop_parameters,
       verbose = verbose
@@ -852,18 +887,18 @@
 
 # SEM function ------------------------------------------------------
 
-
 #' @keywords internal
-.extract_parameters_lavaan <- function(model,
-                                       ci = 0.95,
-                                       standardize = FALSE,
-                                       keep_parameters = NULL,
-                                       drop_parameters = NULL,
-                                       verbose = TRUE,
-                                       ...) {
+.extract_parameters_lavaan <- function(
+  model,
+  ci = 0.95,
+  standardize = FALSE,
+  keep_parameters = NULL,
+  drop_parameters = NULL,
+  verbose = TRUE,
+  ...
+) {
   insight::check_if_installed("lavaan")
 
-
   # lavaan::parameterEstimates does not accept NULL `level`, but a lot of our
   # other methods do. It is often useful to pass `NULL` to speed things up,
   # but it doesn't work here.
@@ -892,9 +927,11 @@
   if (length(ci) > 1L) {
     ci <- ci[1]
     if (verbose) {
-      insight::format_alert(
-        paste0("lavaan models only accept one level of CI. Keeping the first one: `ci = ", ci, "`.")
-      )
+      insight::format_alert(paste0(
+        "lavaan models only accept one level of CI. Keeping the first one: `ci = ",
+        ci,
+        "`."
+      ))
     }
   }
 
@@ -902,6 +939,7 @@
   dot_args <- list(...)
 
   # list all argument names from the `lavaan` function
+  # fmt: skip
   dot_args <- dot_args[names(dot_args) %in% c(
     "zstat", "pvalue", "standardized", "fmi", "level", "boot.ci.type", "cov.std",
     "fmi.options", "rsquare", "remove.system.eq", "remove.eq", "remove.ineq",
@@ -911,10 +949,7 @@
   # Get estimates
   sem_data <- do.call(
     lavaan::parameterEstimates,
-    c(
-      list(object = model, se = TRUE, ci = TRUE, level = ci),
-      dot_args
-    )
+    c(list(object = model, se = TRUE, ci = TRUE, level = ci), dot_args)
   )
 
   label <- sem_data$label
@@ -925,7 +960,8 @@
       standardize <- "all"
     }
 
-    type <- switch(standardize,
+    type <- switch(
+      standardize,
       all = ,
       std.all = "std.all",
       latent = ,
@@ -945,7 +981,6 @@
     names(sem_data)[names(sem_data) == "est.std"] <- "est"
   }
 
-
   params <- data.frame(
     To = sem_data$lhs,
     Operator = sem_data$op,
@@ -982,7 +1017,8 @@
 
   # filter parameters, if requested
   if (!is.null(keep_parameters) || !is.null(drop_parameters)) {
-    params <- .filter_parameters(params,
+    params <- .filter_parameters(
+      params,
       keep = keep_parameters,
       drop = drop_parameters,
       verbose = verbose
@@ -995,7 +1031,6 @@
 
 # tools -------------------------
 
-
 .check_rank_deficiency <- function(model, p, verbose = TRUE) {
   # for cox-panel models, we have non-linear parameters with NA coefficient,
   # but test statistic and p-value - don't check for NA estimates in this case
@@ -1004,12 +1039,10 @@
   }
   if (anyNA(p$Estimate)) {
     if (isTRUE(verbose)) {
-      insight::format_alert(
-        sprintf(
-          "Model matrix is rank deficient. Parameters `%s` were not estimable.",
-          toString(p$Parameter[is.na(p$Estimate)])
-        )
-      )
+      insight::format_alert(sprintf(
+        "Model matrix is rank deficient. Parameters `%s` were not estimable.",
+        toString(p$Parameter[is.na(p$Estimate)])
+      ))
     }
     p <- p[!is.na(p$Estimate), ]
   }
diff --git a/R/format_parameters.R b/R/format_parameters.R
index 84a80766b..9e21d599f 100644
--- a/R/format_parameters.R
+++ b/R/format_parameters.R
@@ -64,9 +64,17 @@ format_parameters.parameters_model <- function(model, ...) {
 
 # Utilities ---------------------------------------------------------------
 
-
-.format_parameter_default <- function(model, effects = "fixed", brackets = c("[", "]"), ...) {
-  original_names <- parameter_names <- insight::find_parameters(model, effects = effects, flatten = TRUE)
+.format_parameter_default <- function(
+  model,
+  effects = "fixed",
+  brackets = c("[", "]"),
+  ...
+) {
+  original_names <- parameter_names <- insight::find_parameters(
+    model,
+    effects = effects,
+    flatten = TRUE
+  )
 
   # save some time, if model info is passed as argument
   dot_args <- list(...)
@@ -83,11 +91,14 @@ format_parameters.parameters_model <- function(model, ...) {
 
   # quick fix, for multivariate response models, we use
   # info from first model only
-  if (insight::is_multivariate(model) && !"is_zero_inflated" %in% names(info) && !inherits(model, c("vgam", "vglm"))) {
+  if (
+    insight::is_multivariate(model) &&
+      !"is_zero_inflated" %in% names(info) &&
+      !inherits(model, c("vgam", "vglm"))
+  ) {
     info <- info[[1]]
   }
 
-
   # Type-specific changes
   types <- parameters_type(model)
   if (is.null(types)) {
@@ -95,7 +106,6 @@ format_parameters.parameters_model <- function(model, ...) {
   }
   types$Parameter <- .clean_parameter_names(types$Parameter, full = TRUE)
 
-
   # special handling hurdle- and zeroinfl-models ---------------------
   if (isTRUE(info$is_zero_inflated) || isTRUE(info$is_hurdle)) {
     parameter_names <- gsub("^(count_|zero_)", "", parameter_names)
@@ -103,7 +113,7 @@ format_parameters.parameters_model <- function(model, ...) {
   }
 
   # special handling polr ---------------------
-  if (inherits(model, "polr")) {
+  if (inherits(model, c("polr", "svyolr"))) {
     original_names <- gsub("Intercept: ", "", original_names, fixed = TRUE)
     parameter_names <- gsub("Intercept: ", "", parameter_names, fixed = TRUE)
   }
@@ -121,7 +131,11 @@ format_parameters.parameters_model <- function(model, ...) {
       pattern <- paste0("(", paste(model$varnames, collapse = "|"), ")\\.(.*)")
       dirich_names <- parameter_names <- gsub(pattern, "\\2", names(unlist(cf)))
     } else {
-      dirich_names <- parameter_names <- gsub("(.*)\\.(.*)\\.(.*)", "\\3", names(unlist(cf)))
+      dirich_names <- parameter_names <- gsub(
+        "(.*)\\.(.*)\\.(.*)",
+        "\\3",
+        names(unlist(cf))
+      )
     }
     original_names <- parameter_names
     if (!is.null(dirich_names)) {
@@ -129,11 +143,9 @@ format_parameters.parameters_model <- function(model, ...) {
     }
   }
 
-
   # remove "as.factor()", "log()" etc. from parameter names
   parameter_names <- .clean_parameter_names(parameter_names)
 
-
   for (i in seq_len(nrow(types))) {
     name <- types$Parameter[i]
 
@@ -161,7 +173,9 @@ format_parameters.parameters_model <- function(model, ...) {
           # variable for each response, thus we have multiple rows here,
           # where only one row is required.
 
-          if (nrow(type) > 1) type <- type[1, ]
+          if (nrow(type) > 1) {
+            type <- type[1, ]
+          }
 
           components[j] <- .format_parameter(
             components[j],
@@ -171,7 +185,10 @@ format_parameters.parameters_model <- function(model, ...) {
             brackets = brackets
           )
         } else if (components[j] %in% types$Secondary_Parameter) {
-          type <- types[!is.na(types$Secondary_Parameter) & types$Secondary_Parameter == components[j], ]
+          type <- types[
+            !is.na(types$Secondary_Parameter) &
+              types$Secondary_Parameter == components[j],
+          ]
           components[j] <- .format_parameter(
             components[j],
             variable = type[1, ]$Secondary_Variable,
@@ -227,27 +244,54 @@ format_parameters.parameters_model <- function(model, ...) {
 
   # Polynomials
   if (type %in% c("poly", "poly_raw")) {
-    name <- .format_poly(name = name, variable = variable, type = type, degree = level, brackets = brackets)
+    name <- .format_poly(
+      name = name,
+      variable = variable,
+      type = type,
+      degree = level,
+      brackets = brackets
+    )
   }
 
   # Splines
   if (type == "spline") {
-    name <- .format_poly(name = name, variable = variable, type = type, degree = level, brackets = brackets)
+    name <- .format_poly(
+      name = name,
+      variable = variable,
+      type = type,
+      degree = level,
+      brackets = brackets
+    )
   }
 
   # log-transformation
   if (type == "logarithm") {
-    name <- .format_log(name = name, variable = variable, type = type, brackets = brackets)
+    name <- .format_log(
+      name = name,
+      variable = variable,
+      type = type,
+      brackets = brackets
+    )
   }
 
   # exp-transformation
   if (type == "exponentiation") {
-    name <- .format_log(name = name, variable = variable, type = type, brackets = brackets)
+    name <- .format_log(
+      name = name,
+      variable = variable,
+      type = type,
+      brackets = brackets
+    )
   }
 
   # log-transformation
   if (type == "squareroot") {
-    name <- .format_log(name = name, variable = variable, type = type, brackets = brackets)
+    name <- .format_log(
+      name = name,
+      variable = variable,
+      type = type,
+      brackets = brackets
+    )
   }
 
   # As Is
@@ -271,12 +315,14 @@ format_parameters.parameters_model <- function(model, ...) {
 
 
 #' @keywords internal
-.format_interaction <- function(components,
-                                type,
-                                is_nested = FALSE,
-                                is_simple = FALSE,
-                                interaction_mark = NULL,
-                                ...) {
+.format_interaction <- function(
+  components,
+  type,
+  is_nested = FALSE,
+  is_simple = FALSE,
+  interaction_mark = NULL,
+  ...
+) {
   # sep <- ifelse(is_nested | is_simple, " : ", " * ")
   # sep <- ifelse(is_nested, " / ", " * ")
   # sep <- ifelse(is_simple, " : ", ifelse(is_nested, " / ", " * "))
@@ -337,7 +383,14 @@ format_parameters.parameters_model <- function(model, ...) {
 
 #' @keywords internal
 .format_poly <- function(name, variable, type, degree, brackets = c("[", "]")) {
-  paste0(variable, " ", brackets[1], format_order(as.numeric(degree), textual = FALSE), " degree", brackets[2])
+  paste0(
+    variable,
+    " ",
+    brackets[1],
+    format_order(as.numeric(degree), textual = FALSE),
+    " degree",
+    brackets[2]
+  )
 }
 
 
@@ -349,13 +402,17 @@ format_parameters.parameters_model <- function(model, ...) {
 
 #' @keywords internal
 .format_ordered <- function(degree, brackets = c("[", "]")) {
-  switch(degree,
+  switch(
+    degree,
     .L = paste0(brackets[1], "linear", brackets[2]),
     .Q = paste0(brackets[1], "quadratic", brackets[2]),
     .C = paste0(brackets[1], "cubic", brackets[2]),
     paste0(
       brackets[1],
-      parameters::format_order(as.numeric(gsub("^", "", degree, fixed = TRUE)), textual = FALSE),
+      parameters::format_order(
+        as.numeric(gsub("^", "", degree, fixed = TRUE)),
+        textual = FALSE
+      ),
       " degree",
       brackets[2]
     )
@@ -376,7 +433,11 @@ format_parameters.parameters_model <- function(model, ...) {
     # get data, but exclude response - we have no need for that label
     mf <- insight::get_data(model, source = "mf", verbose = FALSE)
     # sanity check - any labels?
-    has_labels <- vapply(mf, function(i) !is.null(attr(i, "labels", exact = TRUE)), logical(1))
+    has_labels <- vapply(
+      mf,
+      function(i) !is.null(attr(i, "labels", exact = TRUE)),
+      logical(1)
+    )
     # if we don't have labels, we try to get data from environment
     if (!any(has_labels)) {
       mf <- insight::get_data(model, source = "environment", verbose = FALSE)
@@ -447,7 +508,10 @@ format_parameters.parameters_model <- function(model, ...) {
           # extract single coefficient names from interaction term
           out <- unlist(strsplit(i, ":", fixed = TRUE))
           # combine labels
-          labs <- c(labs, paste(sapply(out, function(l) pretty_labels[l]), collapse = " * "))
+          labs <- c(
+            labs,
+            paste(sapply(out, function(l) pretty_labels[l]), collapse = " * ")
+          )
         }
         # add interaction terms to labels string
         names(labs) <- names(interactions)
@@ -486,5 +550,6 @@ format_parameters.parameters_model <- function(model, ...) {
       TRUE
     }
   )
-  l10n_info()[["UTF-8"]] && ((win_os && getRversion() >= "4.2") || (!win_os && getRversion() >= "4.0"))
+  l10n_info()[["UTF-8"]] &&
+    ((win_os && getRversion() >= "4.2") || (!win_os && getRversion() >= "4.0"))
 }
diff --git a/R/methods_survey.R b/R/methods_survey.R
index 4e431b533..c16b6ba66 100644
--- a/R/methods_survey.R
+++ b/R/methods_survey.R
@@ -1,17 +1,19 @@
 # model_parameters -----------------------------------------
 
 #' @export
-model_parameters.svyglm <- function(model,
-                                    ci = 0.95,
-                                    ci_method = "wald",
-                                    standardize = NULL,
-                                    exponentiate = FALSE,
-                                    p_adjust = NULL,
-                                    include_info = getOption("parameters_info", FALSE),
-                                    keep = NULL,
-                                    drop = NULL,
-                                    verbose = TRUE,
-                                    ...) {
+model_parameters.svyglm <- function(
+  model,
+  ci = 0.95,
+  ci_method = "wald",
+  standardize = NULL,
+  exponentiate = FALSE,
+  p_adjust = NULL,
+  include_info = getOption("parameters_info", FALSE),
+  keep = NULL,
+  drop = NULL,
+  verbose = TRUE,
+  ...
+) {
   if (insight::n_obs(model) > 1e4 && ci_method == "likelihood") {
     insight::format_alert(
       "Likelihood confidence intervals may take longer time to compute. Use 'ci_method=\"wald\"' for faster computation of CIs." # nolint
@@ -45,6 +47,9 @@ model_parameters.svyglm <- function(model,
   out
 }
 
+#' @export
+model_parameters.svyolr <- model_parameters.glm
+
 
 # simulate_model -----------------------------------------
 
@@ -64,10 +69,7 @@ standard_error.svyglm.nb <- function(model, ...) {
     requireNamespace("survey", quietly = TRUE)
   }
   se <- sqrt(diag(stats::vcov(model, stderr = "robust")))
-  .data_frame(
-    Parameter = .remove_backticks_from_string(names(se)),
-    SE = as.vector(se)
-  )
+  .data_frame(Parameter = .remove_backticks_from_string(names(se)), SE = as.vector(se))
 }
 
 
@@ -86,7 +88,7 @@ standard_error.svyglm <- function(model, ...) {
 
 
 #' @export
-standard_error.svyolr <- standard_error.svyglm
+standard_error.svyolr <- standard_error.polr
 
 
 # confidence intervals -----------------------------------
@@ -106,7 +108,7 @@ ci.svyglm <- function(x, ci = 0.95, method = "wald", ...) {
 }
 
 #' @export
-ci.svyolr <- ci.svyglm
+ci.svyolr <- ci.polr
 
 
 # p values -----------------------------------------------
@@ -118,15 +120,12 @@ p_value.svyglm <- function(model, verbose = TRUE, ...) {
   statistic <- insight::get_statistic(model)
   dof <- insight::get_df(model, type = "residual")
   p <- 2 * stats::pt(-abs(statistic$Statistic), df = dof)
-  .data_frame(
-    Parameter = statistic$Parameter,
-    p = as.vector(p)
-  )
+  .data_frame(Parameter = statistic$Parameter, p = as.vector(p))
 }
 
 
 #' @export
-p_value.svyolr <- p_value.svyglm
+p_value.svyolr <- p_value.polr
 
 
 #' @export
@@ -137,12 +136,14 @@ p_value.svyglm.nb <- function(model, ...) {
 
   est <- stats::coef(model)
   se <- sqrt(diag(stats::vcov(model, stderr = "robust")))
-  p <- 2 * stats::pt(abs(est / se), df = insight::get_df(model, type = "wald"), lower.tail = FALSE)
+  p <- 2 *
+    stats::pt(
+      abs(est / se),
+      df = insight::get_df(model, type = "wald"),
+      lower.tail = FALSE
+    )
 
-  .data_frame(
-    Parameter = .remove_backticks_from_string(names(p)),
-    p = as.vector(p)
-  )
+  .data_frame(Parameter = .remove_backticks_from_string(names(p)), p = as.vector(p))
 }
 
 
@@ -155,11 +156,18 @@ p_value.svyglm.zip <- p_value.svyglm.nb
 .ci_likelihood <- function(model, ci) {
   glm_ci <- tryCatch(
     {
-      out <- as.data.frame(stats::confint(model, level = ci, method = "likelihood"), stringsAsFactors = FALSE)
+      out <- as.data.frame(
+        stats::confint(model, level = ci, method = "likelihood"),
+        stringsAsFactors = FALSE
+      )
       names(out) <- c("CI_low", "CI_high")
 
       out$CI <- ci
-      out$Parameter <- insight::get_parameters(model, effects = "fixed", component = "conditional")$Parameter
+      out$Parameter <- insight::get_parameters(
+        model,
+        effects = "fixed",
+        component = "conditional"
+      )$Parameter
 
       out <- out[c("Parameter", "CI", "CI_low", "CI_high")]
       rownames(out) <- NULL
diff --git a/R/parameters_type.R b/R/parameters_type.R
index 0024459b9..334157cd8 100644
--- a/R/parameters_type.R
+++ b/R/parameters_type.R
@@ -61,7 +61,7 @@ parameters_type <- function(model, ...) {
   )
 
   # Special case
-  if (inherits(model, "polr")) {
+  if (inherits(model, c("polr", "svyolr"))) {
     params$Parameter <- gsub("Intercept: ", "", params$Parameter, fixed = TRUE)
   }
 
@@ -81,7 +81,6 @@ parameters_type <- function(model, ...) {
     }
   }
 
-
   # Remove "as.factor()", "log()" etc. from parameter names but save original parameter before
   original_parameter <- params$Parameter
   params$Parameter <- .clean_parameter_names(params$Parameter, full = TRUE)
@@ -91,7 +90,6 @@ parameters_type <- function(model, ...) {
     params$Parameter <- gsub("^(count_|zero_)", "", params$Parameter)
   }
 
-
   data <- insight::get_data(model, source = "mf", verbose = FALSE)
   if (is.null(data) || inherits(data, "ts") || nrow(data) == 0) {
     return(NULL)
@@ -112,12 +110,17 @@ parameters_type <- function(model, ...) {
   main <- .parameters_type_table(names = params$Parameter, data, reference)
   secondary <- .parameters_type_table(names = main$Secondary_Parameter, data, reference)
   names(secondary) <- paste0("Secondary_", names(secondary))
-  names(secondary)[names(secondary) == "Secondary_Secondary_Parameter"] <- "Tertiary_Parameter"
+  names(secondary)[
+    names(secondary) == "Secondary_Secondary_Parameter"
+  ] <- "Tertiary_Parameter"
 
   out <- cbind(params, main, secondary)
 
   # Deal with nested interactions
-  for (i in unique(paste0(out[out$Type == "interaction", "Variable"], out[out$Type == "interaction", "Secondary_Variable"]))) {
+  for (i in unique(paste0(
+    out[out$Type == "interaction", "Variable"],
+    out[out$Type == "interaction", "Secondary_Variable"]
+  ))) {
     interac <- out[paste0(out$Variable, out$Secondary_Variable) == i, ]
     if (!all(interac$Term %in% out$Parameter)) {
       out[paste0(out$Variable, out$Secondary_Variable) == i, "Type"] <- "nested"
@@ -133,9 +136,17 @@ parameters_type <- function(model, ...) {
   for (i in unique(out$Secondary_Parameter)) {
     if (!is.na(i) && i %in% out$Parameter) {
       .param_type <- out[!is.na(out$Parameter) & out$Parameter == i, "Type"]
-      .param_secondary_type <- out[!is.na(out$Secondary_Parameter) & out$Secondary_Parameter == i, "Secondary_Type"]
-      if (length(.param_type) == length(.param_secondary_type) || length(.param_type) == 1) {
-        out[!is.na(out$Secondary_Parameter) & out$Secondary_Parameter == i, "Secondary_Type"] <- .param_type
+      .param_secondary_type <- out[
+        !is.na(out$Secondary_Parameter) & out$Secondary_Parameter == i,
+        "Secondary_Type"
+      ]
+      if (
+        length(.param_type) == length(.param_secondary_type) || length(.param_type) == 1
+      ) {
+        out[
+          !is.na(out$Secondary_Parameter) & out$Secondary_Parameter == i,
+          "Secondary_Type"
+        ] <- .param_type
       }
     }
   }
@@ -166,7 +177,12 @@ parameters_type <- function(model, ...) {
     }
 
     # Check if any is factor
-    types <- unlist(lapply(var, function(x, data, reference) .parameters_type_basic(x, data, reference)[1], data = data, reference = reference))
+    types <- unlist(lapply(
+      var,
+      function(x, data, reference) .parameters_type_basic(x, data, reference)[1],
+      data = data,
+      reference = reference
+    ))
     link <- ifelse(any("factor" %in% types), "Difference", "Association")
     # Get type
     main <- .parameters_type_basic(var[1], data, reference)
@@ -214,14 +230,7 @@ parameters_type <- function(model, ...) {
     # Factors
   } else if (cleaned_name %in% reference$levels) {
     fac <- reference$levels_parent[match(cleaned_name, reference$levels)]
-    return(c(
-      "factor",
-      "Difference",
-      name,
-      fac,
-      gsub(fac, "", name, fixed = TRUE),
-      NA
-    ))
+    return(c("factor", "Difference", name, fac, gsub(fac, "", name, fixed = TRUE), NA))
 
     # Polynomials
   } else if (grepl("poly(", name, fixed = TRUE)) {
@@ -284,7 +293,14 @@ parameters_type <- function(model, ...) {
 
     # Smooth
   } else if (startsWith(name, "smooth_")) {
-    return(c("smooth", "Association", gsub("^smooth_(.*)\\[(.*)\\]", "\\2", name), NA, NA, NA))
+    return(c(
+      "smooth",
+      "Association",
+      gsub("^smooth_(.*)\\[(.*)\\]", "\\2", name),
+      NA,
+      NA,
+      NA
+    ))
   } else {
     return(c("unknown", NA, NA, NA, NA, NA))
   }
@@ -355,7 +371,9 @@ parameters_type <- function(model, ...) {
   out$ordered <- names(data[vapply(data, is.ordered, TRUE)])
 
   # Factors
-  out$factor <- names(data[vapply(data, is.factor, TRUE) | vapply(data, is.character, TRUE)])
+  out$factor <- names(data[
+    vapply(data, is.factor, TRUE) | vapply(data, is.character, TRUE)
+  ])
 
   out$levels <- NA
   out$levels_parent <- NA
@@ -370,15 +388,35 @@ parameters_type <- function(model, ...) {
   }
 
   for (fac in out$factor) {
-    if ((fac %in% out$ordered && is.null(contrast_coding[[fac]])) || (!is.null(contrast_coding[[fac]]) && any(contrast_coding[[fac]] %in% "contr.poly"))) {
-      levels <- paste0(fac, c(".L", ".Q", ".C", paste0("^", 4:1000))[seq_along(unique(data[[fac]]))])
-    } else if (!is.null(contrast_coding[[fac]]) && any(contrast_coding[[fac]] %in% c("contr.SAS2", "contr.sum", "contr.bayes", "contr.helmert"))) {
+    if (
+      (fac %in% out$ordered && is.null(contrast_coding[[fac]])) ||
+        (!is.null(contrast_coding[[fac]]) &&
+          any(contrast_coding[[fac]] %in% "contr.poly"))
+    ) {
+      levels <- paste0(
+        fac,
+        c(".L", ".Q", ".C", paste0("^", 4:1000))[seq_along(unique(data[[fac]]))]
+      )
+    } else if (
+      !is.null(contrast_coding[[fac]]) &&
+        any(
+          contrast_coding[[fac]] %in%
+            c("contr.SAS2", "contr.sum", "contr.bayes", "contr.helmert")
+        )
+    ) {
       levels <- paste0(fac, seq_along(unique(data[[fac]])))
-    } else if (!is.null(contrast_coding[[fac]]) && any(contrast_coding[[fac]] %in% "contr.treatment2")) {
+    } else if (
+      !is.null(contrast_coding[[fac]]) &&
+        any(contrast_coding[[fac]] %in% "contr.treatment2")
+    ) {
       levels <- paste0(fac, 2:length(unique(data[[fac]])))
-    } else if (!is.null(contrast_coding[[fac]]) && any(contrast_coding[[fac]] %in% "contr.SAS")) {
+    } else if (
+      !is.null(contrast_coding[[fac]]) && any(contrast_coding[[fac]] %in% "contr.SAS")
+    ) {
       levels <- paste0(fac, rev(unique(data[[fac]])))
-    } else if (!is.null(contrast_coding[[fac]]) && any(contrast_coding[[fac]] %in% "contr.custom")) {
+    } else if (
+      !is.null(contrast_coding[[fac]]) && any(contrast_coding[[fac]] %in% "contr.custom")
+    ) {
       levels <- paste0(fac, attributes(contrast_coding[[fac]])$column_names)
     } else {
       levels <- paste0(fac, unique(data[[fac]]))
diff --git a/R/pool_parameters.R b/R/pool_parameters.R
index cd5c0fb35..9a01fd419 100644
--- a/R/pool_parameters.R
+++ b/R/pool_parameters.R
@@ -63,26 +63,34 @@
 #' pool_parameters(models, ci_method = "residual")$df_error
 #' @return A data frame of indices related to the model's parameters.
 #' @export
-pool_parameters <- function(x,
-                            exponentiate = FALSE,
-                            effects = "fixed",
-                            component = "all",
-                            verbose = TRUE,
-                            ...) {
+pool_parameters <- function(
+  x,
+  exponentiate = FALSE,
+  effects = "fixed",
+  component = "all",
+  verbose = TRUE,
+  ...
+) {
   # check input, save original model -----
 
   original_model <- random_params <- NULL
   obj_name <- insight::safe_deparse_symbol(substitute(x))
 
-  if (all(vapply(x, insight::is_model, TRUE)) && all(vapply(x, insight::is_model_supported, TRUE))) {
+  if (
+    all(vapply(x, insight::is_model, TRUE)) &&
+      all(vapply(x, insight::is_model_supported, TRUE))
+  ) {
     original_model <- x[[1]]
 
     # Add exceptions for models with uncommon components here ---------------
-    exception_model_class <- "polr"
+    exception_model_class <- c("polr", "svyolr")
 
     # exceptions for "component" argument. Eg, MASS::polr has components
     # "alpha" and "beta", and "component" needs to be set to all by default
-    if (identical(component, "conditional") && inherits(original_model, exception_model_class)) {
+    if (
+      identical(component, "conditional") &&
+        inherits(original_model, exception_model_class)
+    ) {
       component <- "all"
     }
 
@@ -105,29 +113,38 @@ pool_parameters <- function(x,
     )
   }
 
-
   # only pool for specific component -----
 
   original_x <- x
-  if ("Component" %in% colnames(x[[1]]) && !insight::is_empty_object(component) && component != "all") {
+  if (
+    "Component" %in%
+      colnames(x[[1]]) &&
+      !insight::is_empty_object(component) &&
+      component != "all"
+  ) {
     x <- lapply(x, function(i) {
       i <- i[i$Component == component, ]
       i$Component <- NULL
       i
     })
     if (verbose) {
-      insight::format_alert(paste0("Pooling applied to the ", component, " model component."))
+      insight::format_alert(paste0(
+        "Pooling applied to the ",
+        component,
+        " model component."
+      ))
     }
   }
 
-
   # preparation ----
 
   params <- do.call(rbind, x)
 
   len <- length(x)
   ci <- attributes(original_x[[1]])$ci
-  if (is.null(ci)) ci <- 0.95
+  if (is.null(ci)) {
+    ci <- 0.95
+  }
   parameter_values <- x[[1]]$Parameter
 
   # exceptions ----
@@ -156,7 +173,9 @@ pool_parameters <- function(x,
   # but only for fixed effects. Filter random effects,
   # and save parameter names from fixed effects for later use...
 
-  if (effects == "all" && "Effects" %in% colnames(params) && "random" %in% params$Effects) {
+  if (
+    effects == "all" && "Effects" %in% colnames(params) && "random" %in% params$Effects
+  ) {
     random_params <- params[params$Effects == "random", ]
     params <- params[params$Effects != "random", ]
     parameter_values <- x[[1]]$Parameter[x[[1]]$Effects != "random"]
@@ -182,98 +201,123 @@ pool_parameters <- function(x,
 
   # pool estimates etc. -----
 
-  pooled_params <- do.call(rbind, lapply(estimates, function(i) {
-    # if we split by "component", some of the data frames might be empty
-    # in this case, just skip...
-    if (nrow(i) > 0) {
-      # pooled estimate
-      pooled_estimate <- mean(i$Coefficient)
+  pooled_params <- do.call(
+    rbind,
+    lapply(estimates, function(i) {
+      # if we split by "component", some of the data frames might be empty
+      # in this case, just skip...
+      if (nrow(i) > 0) {
+        # pooled estimate
+        pooled_estimate <- mean(i$Coefficient)
+
+        # special models that have no standard errors (like "htest" objects)
+        if (is.null(i$SE) || all(is.na(i$SE))) {
+          out <- data.frame(
+            Coefficient = pooled_estimate,
+            SE = NA,
+            CI_low = NA,
+            CI_high = NA,
+            Statistic = NA,
+            df_error = NA,
+            p = NA,
+            stringsAsFactors = FALSE
+          )
+
+          if (verbose) {
+            insight::format_alert(
+              "Model objects had no standard errors. Cannot compute pooled confidence intervals and p-values."
+            )
+          }
 
-      # special models that have no standard errors (like "htest" objects)
-      if (is.null(i$SE) || all(is.na(i$SE))) {
-        out <- data.frame(
-          Coefficient = pooled_estimate,
-          SE = NA,
-          CI_low = NA,
-          CI_high = NA,
-          Statistic = NA,
-          df_error = NA,
-          p = NA,
-          stringsAsFactors = FALSE
-        )
+          # regular models that have coefficients and standard errors
+        } else {
+          # pooled standard error
+          ubar <- mean(i$SE^2)
+          tmp <- ubar + (1 + 1 / len) * stats::var(i$Coefficient)
+          pooled_se <- sqrt(tmp)
+
+          # pooled degrees of freedom, Barnard-Rubin adjustment for small samples
+          df_column <- grep("(\\bdf\\b|\\bdf_error\\b)", colnames(i), value = TRUE)[1]
+          if (length(df_column)) {
+            pooled_df <- .barnad_rubin(
+              m = nrow(i),
+              b = stats::var(i$Coefficient),
+              t = tmp,
+              dfcom = unique(i[[df_column]])
+            )
+            # validation check length
+            if (length(pooled_df) > 1 && length(pooled_se) == 1) {
+              pooled_df <- round(mean(pooled_df, na.rm = TRUE))
+            }
+          } else {
+            pooled_df <- Inf
+          }
 
-        if (verbose) {
-          insight::format_alert("Model objects had no standard errors. Cannot compute pooled confidence intervals and p-values.")
+          # pooled statistic
+          pooled_statistic <- pooled_estimate / pooled_se
+
+          # confidence intervals
+          alpha <- (1 + ci) / 2
+          fac <- suppressWarnings(stats::qt(alpha, df = pooled_df))
+
+          out <- data.frame(
+            Coefficient = pooled_estimate,
+            SE = pooled_se,
+            CI_low = pooled_estimate - pooled_se * fac,
+            CI_high = pooled_estimate + pooled_se * fac,
+            Statistic = pooled_statistic,
+            df_error = pooled_df,
+            p = 2 * stats::pt(abs(pooled_statistic), df = pooled_df, lower.tail = FALSE),
+            stringsAsFactors = FALSE
+          )
         }
-
-        # regular models that have coefficients and standard errors
+        out
       } else {
-        # pooled standard error
-        ubar <- mean(i$SE^2)
-        tmp <- ubar + (1 + 1 / len) * stats::var(i$Coefficient)
-        pooled_se <- sqrt(tmp)
-
-        # pooled degrees of freedom, Barnard-Rubin adjustment for small samples
-        df_column <- grep("(\\bdf\\b|\\bdf_error\\b)", colnames(i), value = TRUE)[1]
-        if (length(df_column)) {
-          pooled_df <- .barnad_rubin(m = nrow(i), b = stats::var(i$Coefficient), t = tmp, dfcom = unique(i[[df_column]]))
-          # validation check length
-          if (length(pooled_df) > 1 && length(pooled_se) == 1) {
-            pooled_df <- round(mean(pooled_df, na.rm = TRUE))
-          }
-        } else {
-          pooled_df <- Inf
-        }
-
-        # pooled statistic
-        pooled_statistic <- pooled_estimate / pooled_se
-
-        # confidence intervals
-        alpha <- (1 + ci) / 2
-        fac <- suppressWarnings(stats::qt(alpha, df = pooled_df))
-
-        out <- data.frame(
-          Coefficient = pooled_estimate,
-          SE = pooled_se,
-          CI_low = pooled_estimate - pooled_se * fac,
-          CI_high = pooled_estimate + pooled_se * fac,
-          Statistic = pooled_statistic,
-          df_error = pooled_df,
-          p = 2 * stats::pt(abs(pooled_statistic), df = pooled_df, lower.tail = FALSE),
-          stringsAsFactors = FALSE
-        )
+        NULL
       }
-      out
-    } else {
-      NULL
-    }
-  }))
-
+    })
+  )
 
   # pool random effect variances -----
 
   pooled_random <- NULL
   if (!is.null(random_params)) {
-    estimates <- split(random_params, factor(random_params$Parameter, levels = unique(random_params$Parameter)))
-    pooled_random <- do.call(rbind, lapply(estimates, function(i) {
-      pooled_estimate <- mean(i$Coefficient, na.rm = TRUE)
-      data.frame(
-        Parameter = unique(i$Parameter),
-        Coefficient = pooled_estimate,
-        Effects = "random",
-        stringsAsFactors = FALSE
-      )
-    }))
+    estimates <- split(
+      random_params,
+      factor(random_params$Parameter, levels = unique(random_params$Parameter))
+    )
+    pooled_random <- do.call(
+      rbind,
+      lapply(estimates, function(i) {
+        pooled_estimate <- mean(i$Coefficient, na.rm = TRUE)
+        data.frame(
+          Parameter = unique(i$Parameter),
+          Coefficient = pooled_estimate,
+          Effects = "random",
+          stringsAsFactors = FALSE
+        )
+      })
+    )
     pooled_params$Effects <- "fixed"
   }
 
   # reorder ------
 
   pooled_params$Parameter <- parameter_values
-  columns <- c("Parameter", "Coefficient", "SE", "CI_low", "CI_high", "Statistic", "df_error", "p", "Effects", "Component")
+  columns <- c(
+    "Parameter",
+    "Coefficient",
+    "SE",
+    "CI_low",
+    "CI_high",
+    "Statistic",
+    "df_error",
+    "p",
+    "Effects",
+    "Component"
+  )
   pooled_params <- pooled_params[intersect(columns, colnames(pooled_params))]
 
-
   # final attributes -----
 
   # exponentiate coefficients and SE/CI, if requested
@@ -299,7 +343,6 @@ pool_parameters <- function(x,
   )
   attr(pooled_params, "object_name") <- obj_name
 
-
   # pool sigma ----
 
   sig <- unlist(insight::compact_list(lapply(original_x, function(i) {
@@ -310,15 +353,17 @@ pool_parameters <- function(x,
     attr(pooled_params, "sigma") <- mean(sig, na.rm = TRUE)
   }
 
-
-  class(pooled_params) <- c("parameters_model", "see_parameters_model", class(pooled_params))
+  class(pooled_params) <- c(
+    "parameters_model",
+    "see_parameters_model",
+    class(pooled_params)
+  )
   pooled_params
 }
 
 
 # helper ------
 
-
 .barnad_rubin <- function(m, b, t, dfcom = 999999) {
   # fix for z-statistic
   if (is.null(dfcom) || all(is.na(dfcom)) || all(is.infinite(dfcom))) {
@@ -332,11 +377,21 @@ pool_parameters <- function(x,
 }
 
 
-.add_pooled_params_attributes <- function(pooled_params, model_params, model, ci, exponentiate, verbose = TRUE) {
+.add_pooled_params_attributes <- function(
+  pooled_params,
+  model_params,
+  model,
+  ci,
+  exponentiate,
+  verbose = TRUE
+) {
   info <- insight::model_info(model, verbose = FALSE)
   pretty_names <- attributes(model_params)$pretty_names
   if (length(pretty_names) < nrow(model_params)) {
-    pretty_names <- c(pretty_names, model_params$Parameter[(length(pretty_names) + 1):nrow(model_params)])
+    pretty_names <- c(
+      pretty_names,
+      model_params$Parameter[(length(pretty_names) + 1):nrow(model_params)]
+    )
   }
   attr(pooled_params, "ci") <- ci
   attr(pooled_params, "exponentiate") <- exponentiate
diff --git a/tests/testthat/test-svyolr.R b/tests/testthat/test-svyolr.R
new file mode 100644
index 000000000..5de420a8c
--- /dev/null
+++ b/tests/testthat/test-svyolr.R
@@ -0,0 +1,44 @@
+skip_if_not_installed("MASS")
+skip_if_not_installed("survey")
+skip_on_cran()
+
+test_that("robust-se polr", {
+  data(api, package = "survey")
+  dclus1 <- survey::svydesign(id = ~dnum, weights = ~pw, data = apiclus1, fpc = ~fpc)
+  dclus1 <- update(dclus1, mealcat = cut(meals, c(0, 25, 50, 75, 100)))
+
+  m <- survey::svyolr(mealcat ~ avg.ed + mobility + stype, design = dclus1)
+  out <- model_parameters(m)
+  expect_identical(attributes(out)$coefficient_name, "Log-Odds")
+  expect_identical(
+    out$Component,
+    c("alpha", "alpha", "alpha", "beta", "beta", "beta", "beta")
+  )
+  expect_identical(
+    out$Parameter,
+    c(
+      "(0,25]|(25,50]",
+      "(25,50]|(50,75]",
+      "(50,75]|(75,100]",
+      "avg.ed",
+      "mobility",
+      "stypeH",
+      "stypeM"
+    )
+  )
+  expect_named(
+    out,
+    c(
+      "Parameter",
+      "Coefficient",
+      "SE",
+      "CI",
+      "CI_low",
+      "CI_high",
+      "t",
+      "df_error",
+      "p",
+      "Component"
+    )
+  )
+})