diff --git a/DESCRIPTION b/DESCRIPTION
index 013843b..5198098 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -30,10 +30,10 @@ Suggests:
     car,
     coin,
     ggplot2,
+    lme4,
     MASS,
     pscl,
     pwr,
-    sjPlot,
     survey,
     testthat
 URL: https://strengejacke.github.io/sjstats/
diff --git a/NAMESPACE b/NAMESPACE
index 558b8f6..3e933bb 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -19,21 +19,15 @@ S3method(plot,sj_inequ_trend)
 S3method(predict,svyglm.nb)
 S3method(print,sj_anova_stat)
 S3method(print,sj_chi2gof)
-S3method(print,sj_grpmean)
 S3method(print,sj_htest_chi)
 S3method(print,sj_htest_kw)
 S3method(print,sj_htest_mwu)
-S3method(print,sj_mwu)
-S3method(print,sj_outliers)
 S3method(print,sj_resample)
 S3method(print,sj_ttest)
 S3method(print,sj_wcor)
-S3method(print,sj_wmwu)
 S3method(print,sj_xtab_stat)
-S3method(print,sj_xtab_stat2)
 S3method(print,svyglm.nb)
 S3method(print,svyglm.zip)
-S3method(print,tidy_stan)
 S3method(residuals,svyglm.nb)
 S3method(terms,svyglm.nb)
 S3method(weighted_correlation,default)
@@ -111,11 +105,6 @@ importFrom(bayestestR,equivalence_test)
 importFrom(datawizard,weighted_mean)
 importFrom(datawizard,weighted_median)
 importFrom(datawizard,weighted_sd)
-importFrom(insight,export_table)
-importFrom(insight,format_p)
-importFrom(insight,format_value)
 importFrom(insight,link_inverse)
-importFrom(insight,print_color)
 importFrom(performance,mse)
 importFrom(performance,rmse)
-importFrom(stats,family)
diff --git a/R/S3-methods.R b/R/S3-methods.R
index 131f3d9..cff58b4 100644
--- a/R/S3-methods.R
+++ b/R/S3-methods.R
@@ -24,7 +24,6 @@ print.svyglm.nb <- function(x, se = c("robust", "model"), digits = 4, ...) {
 }
 
 
-
 #' @export
 print.svyglm.zip <- function(x, se = c("robust", "model"), digits = 4, ...) {
   se <- match.arg(se)
@@ -47,7 +46,6 @@ print.svyglm.zip <- function(x, se = c("robust", "model"), digits = 4, ...) {
 }
 
 
-
 tidy_svyglm.nb <- function(x, digits = 4, v_se = c("robust", "model")) {
   v_se <- match.arg(v_se)
 
@@ -70,7 +68,6 @@ tidy_svyglm.nb <- function(x, digits = 4, v_se = c("robust", "model")) {
 }
 
 
-
 tidy_svyglm.zip <- function(x, digits = 4, v_se = c("robust", "model")) {
   v_se <- match.arg(v_se)
 
@@ -92,7 +89,6 @@ tidy_svyglm.zip <- function(x, digits = 4, v_se = c("robust", "model")) {
 }
 
 
-
 #' @export
 model.frame.svyglm.nb <- function(formula, ...) {
   pred <- attr(formula, "nb.terms", exact = TRUE)
@@ -100,7 +96,6 @@ model.frame.svyglm.nb <- function(formula, ...) {
 }
 
 
-
 #' @export
 model.frame.svyglm.zip <- function(formula, ...) {
   pred <- attr(formula, "zip.terms", exact = TRUE)
@@ -108,28 +103,24 @@ model.frame.svyglm.zip <- function(formula, ...) {
 }
 
 
-#' @importFrom stats family
 #' @export
 family.svyglm.nb <- function(object, ...) {
   attr(object, "family", exact = TRUE)
 }
 
 
-
 #' @export
 formula.svyglm.nb <- function(x, ...) {
   attr(x, "nb.formula", exact = TRUE)
 }
 
 
-
 #' @export
 formula.svyglm.zip <- function(x, ...) {
   attr(x, "zip.formula", exact = TRUE)
 }
 
 
-
 #' @export
 predict.svyglm.nb <- function(object, newdata = NULL,
                               type = c("link", "response", "terms"),
@@ -212,51 +203,18 @@ deviance.svyglm.nb <- function(object, ...) {
 }
 
 
-#' @export
-print.tidy_stan <- function(x, ...) {
-  insight::print_color("\nSummary Statistics of Stan-Model\n\n", "blue")
-  digits <- attr(x, "digits")
-
-  for (i in x) {
-    insight::print_color(paste0("# ", attr(i, "main_title")), "blue")
-    cat(" ")
-    insight::print_color(attr(i, "sub_title"), "red")
-    cat("\n\n")
-    rem <- which(colnames(i) %in% c("Parameter", "Group", "Response", "Function"))
-    i <- i[, -rem]
-    colnames(i)[1] <- "Parameter"
-    i$ESS <- as.character(i$ESS)
-    i$pd <- sprintf("%.1f%%", 100 * i$pd)
-    i[] <- lapply(i, function(.j) {
-      if (is.numeric(.j)) .j <- sprintf("%.*f", digits, .j)
-      .j
-    })
-    print.data.frame(i, quote = FALSE, row.names = FALSE)
-    cat("\n\n")
-  }
-}
-
-
-clean_term_name <- function(x) {
-  x <- insight::trim_ws(x)
-  format(x, width = max(nchar(x)))
-}
-
-
 #' @export
 as.integer.sj_resample <- function(x, ...) {
   x$id
 }
 
 
-
 #' @export
 as.data.frame.sj_resample <- function(x, ...) {
   x$data[x$id, , drop = FALSE]
 }
 
 
-
 #' @export
 print.sj_resample <- function(x, ...) {
   n <- length(x$id)
@@ -275,7 +233,6 @@ print.sj_resample <- function(x, ...) {
 }
 
 
-
 #' @export
 plot.sj_inequ_trend <- function(x, ...) {
   .data <- NULL
@@ -319,78 +276,6 @@ plot.sj_inequ_trend <- function(x, ...) {
 }
 
 
-#' @export
-print.sj_mwu <- function(x, ...) {
-  insight::print_color("\n# Mann-Whitney-U-Test\n\n", "blue")
-  # get data
-  .dat <- x$df
-  # print to console
-  for (i in seq_len(nrow(.dat))) {
-    # get value labels
-    l1 <- .dat[i, "grp1.label"]
-    l2 <- .dat[i, "grp2.label"]
-    # do we have value labels?
-    if (!is.null(l1) && !is.na(l1) %% !is.null(l2) && !is.na(l2)) {
-      insight::print_color(
-        sprintf(
-          "Groups %i = %s (n = %i) | %i = %s (n = %i):\n",
-          .dat[i, "grp1"],
-          l1,
-          .dat[i, "grp1.n"],
-          .dat[i, "grp2"],
-          l2,
-          .dat[i, "grp2.n"]
-        ), "cyan"
-      )
-    } else {
-      insight::print_color(
-        sprintf("Groups (%i|%i), n = %i/%i:\n",
-                .dat[i, "grp1"], .dat[i, "grp2"],
-                .dat[i, "grp1.n"], .dat[i, "grp2.n"]),
-        "cyan"
-      )
-    }
-
-    cat(sprintf(
-      "  U = %.3f, W = %.3f, %s, Z = %.3f\n",
-      .dat[i, "u"], .dat[i, "w"], insight::format_p(.dat[i, "p"]), .dat[i, "z"]
-    ))
-
-    string_es <- "effect-size r"
-    string_r <- sprintf("%.3f", .dat[i, "r"])
-    string_group1 <- sprintf("rank-mean(%i)", .dat[i, "grp1"])
-    string_group2 <- sprintf("rank-mean(%i)", .dat[i, "grp2"])
-    string_rm1 <- sprintf("%.2f", .dat[i, "rank.mean.grp1"])
-    string_rm2 <- sprintf("%.2f", .dat[i, "rank.mean.grp2"])
-
-    space1 <- max(nchar(c(string_es, string_group1, string_group2)))
-    space2 <- max(nchar(c(string_r, string_rm1, string_rm2)))
-
-    cat(
-      sprintf("  %*s = %*s\n", space1, string_es, space2 + 1, string_r),
-      sprintf(" %*s = %*s\n", space1, string_group1, space2, string_rm1),
-      sprintf(" %*s = %*s\n\n", space1, string_group2, space2, string_rm2)
-    )
-  }
-
-  # if we have more than 2 groups, also perfom kruskal-wallis-test
-  if (length(unique(stats::na.omit(x$data$grp))) > 2) {
-    insight::print_color("# Kruskal-Wallis-Test\n\n", "blue")
-    kw <- stats::kruskal.test(x$data$dv, x$data$grp)
-    cat(sprintf("chi-squared = %.3f\n", kw$statistic))
-    cat(sprintf("df = %i\n", kw$parameter))
-    cat(paste(insight::format_p(kw$p.value, stars = TRUE), "\n"))
-  }
-}
-
-
-#' @export
-print.sj_outliers <- function(x, ...) {
-  print(x$result, ...)
-}
-
-
-#' @importFrom insight format_p
 #' @export
 print.sj_xtab_stat <- function(x, ...) {
   # get length of method name, to align output
@@ -414,63 +299,6 @@ print.sj_xtab_stat <- function(x, ...) {
 }
 
 
-
-#' @export
-print.sj_xtab_stat2 <- function(x, ...) {
-  # get length of method name, to align output
-  l <- max(nchar(c(x$stat.name, "p-value", "Observations")))
-
-  # headline
-  insight::print_color(paste0("\n# ", x$method, "\n\n"), "blue")
-
-  # print test statistic
-  cat(sprintf("  %*s: %.4f\n", l, x$stat.name, x$estimate))
-  cat(sprintf("  %*s: %g\n", l, "df", x$df))
-  cat(sprintf("  %*s: %s\n", l, "p-value", insight::format_p(x$p.value, stars = TRUE, name = NULL)))
-  cat(sprintf("  %*s: %g\n", l, "Observations", x$n_obs))
-}
-
-
-
-#' @export
-print.sj_grpmean <- function(x, ...) {
-  cat("\n")
-  print_grpmean(x, digits = attributes(x)$digits, ...)
-}
-
-
-#' @importFrom insight export_table print_color format_value format_p
-print_grpmean <- function(x, digits = NULL, ...) {
-  # headline
-  insight::print_color(sprintf(
-    "# Grouped Means for %s by %s\n\n",
-    attr(x, "dv.label", exact = TRUE),
-    attr(x, "grp.label", exact = TRUE)
-  ), "blue")
-
-  if (is.null(digits)) {
-    digits <- 2
-  }
-
-  x$mean <- insight::format_value(x$mean, digits = digits)
-  x$std.dev <- insight::format_value(x$std.dev, digits = digits)
-  x$std.error <- insight::format_value(x$std.error, digits = digits)
-  x$p.value <- insight::format_p(x$p.value, name = NULL)
-
-  colnames(x) <- c("Category", "Mean", "N", "SD", "SE", "p")
-  cat(insight::export_table(x))
-
-  # statistics
-  cat(sprintf(
-    "\nAnova: R2=%.3f; adj.R2=%.3f; F=%.3f; p=%.3f\n",
-    attr(x, "r2", exact = TRUE),
-    attr(x, "adj.r2", exact = TRUE),
-    attr(x, "fstat", exact = TRUE),
-    attr(x, "p.value", exact = TRUE)
-  ))
-}
-
-
 #' @export
 print.sj_chi2gof <- function(x, ...) {
   insight::print_color("\n# Chi-squared Goodness-of-Fit Test\n\n", "blue")
@@ -538,18 +366,6 @@ print.sj_ttest <- function(x, ...) {
 }
 
 
-#' @export
-print.sj_wmwu <- function(x, ...) {
-  insight::print_color(sprintf("\n# %s\n", x$method), "blue")
-
-  group <- attr(x, "group.name", exact = TRUE)
-  xn <- attr(x, "x.name", exact = TRUE)
-
-  insight::print_color(sprintf("\n  comparison of %s by %s\n", xn, group), "cyan")
-  cat(sprintf("  Chisq=%.2f  df=%i  p-value=%.3f\n\n", x$statistic, as.integer(x$parameter), x$p.value))
-}
-
-
 #' @export
 print.sj_wcor <- function(x, ...) {
   insight::print_color(sprintf("\nWeighted %s\n\n", x$method), "blue")
@@ -565,7 +381,6 @@ print.sj_wcor <- function(x, ...) {
 }
 
 
-#' @importFrom insight export_table
 #' @export
 print.sj_anova_stat <- function(x, digits = 3, ...) {
   x$p.value <- insight::format_p(x$p.value, name = NULL)
diff --git a/R/boot_ci.R b/R/boot_ci.R
index 4645031..33b8b9f 100644
--- a/R/boot_ci.R
+++ b/R/boot_ci.R
@@ -8,7 +8,7 @@
 #' @param data A data frame that containts the vector with bootstrapped
 #' estimates, or directly the vector (see 'Examples').
 #' @param ci.lvl Numeric, the level of the confidence intervals.
-#' @param Select Optional, unquoted names of variables (as character vector)
+#' @param select Optional, unquoted names of variables (as character vector)
 #' with bootstrapped estimates. Required, if either `data` is a data frame
 #' (and no vector), and only selected variables from `data` should be processed.
 #' @param method Character vector, indicating if confidence intervals should be
diff --git a/R/helpfunctions.R b/R/helpfunctions.R
index e3f2a69..21d54f6 100644
--- a/R/helpfunctions.R
+++ b/R/helpfunctions.R
@@ -33,7 +33,7 @@ get_glm_family <- function(fit) {
   # create logical for family
   binom_fam <- fitfam %in% c("binomial", "quasibinomial")
   poisson_fam <- fitfam %in% c("poisson", "quasipoisson") ||
-    grepl("negative binomial", fitfram, ignore.case = TRUE, fixed = TRUE)
+    grepl("negative binomial", fitfam, ignore.case = TRUE, fixed = TRUE)
 
   list(is_bin = binom_fam, is_pois = poisson_fam, is_logit = logit_link)
 }
diff --git a/R/se_ybar.R b/R/se_ybar.R
index a0066fa..642dc85 100644
--- a/R/se_ybar.R
+++ b/R/se_ybar.R
@@ -12,11 +12,9 @@
 #'
 #' @references Gelman A, Hill J. 2007. Data analysis using regression and multilevel/hierarchical models. Cambridge, New York: Cambridge University Press
 #'
-#' @examples
-#' if (require("lme4")) {
-#'   fit <- lmer(Reaction ~ 1 + (1 | Subject), sleepstudy)
-#'   se_ybar(fit)
-#' }
+#' @examplesIf require("lme4")
+#' fit <- lmer(Reaction ~ 1 + (1 | Subject), sleepstudy)
+#' se_ybar(fit)
 #' @export
 se_ybar <- function(fit) {
   # get model icc
diff --git a/R/select_helpers.R b/R/select_helpers.R
index 72c01f9..87d6104 100644
--- a/R/select_helpers.R
+++ b/R/select_helpers.R
@@ -31,5 +31,5 @@ obj_has_name <- function(x, name) {
 }
 
 obj_has_rownames <- function(x) {
-  !identical(as.character(1:nrow(x)), rownames(x))
+  !identical(as.character(seq_len(nrow(x))), rownames(x))
 }
diff --git a/man/boot_ci.Rd b/man/boot_ci.Rd
index eec0835..893080e 100644
--- a/man/boot_ci.Rd
+++ b/man/boot_ci.Rd
@@ -19,16 +19,16 @@ boot_est(data, select = NULL)
 \item{data}{A data frame that containts the vector with bootstrapped
 estimates, or directly the vector (see 'Examples').}
 
+\item{select}{Optional, unquoted names of variables (as character vector)
+with bootstrapped estimates. Required, if either \code{data} is a data frame
+(and no vector), and only selected variables from \code{data} should be processed.}
+
 \item{method}{Character vector, indicating if confidence intervals should be
 based on bootstrap standard error, multiplied by the value of the quantile
 function of the t-distribution (default), or on sample quantiles of the
 bootstrapped values. See 'Details' in \code{boot_ci()}. May be abbreviated.}
 
 \item{ci.lvl}{Numeric, the level of the confidence intervals.}
-
-\item{Select}{Optional, unquoted names of variables (as character vector)
-with bootstrapped estimates. Required, if either \code{data} is a data frame
-(and no vector), and only selected variables from \code{data} should be processed.}
 }
 \value{
 A data frame with either bootstrap estimate, standard error, the
diff --git a/man/se_ybar.Rd b/man/se_ybar.Rd
index 3b976fc..5a16baf 100644
--- a/man/se_ybar.Rd
+++ b/man/se_ybar.Rd
@@ -19,10 +19,10 @@ May be used as part of the multilevel power calculation for cluster sampling
 (see \cite{Gelman and Hill 2007, 447ff}).
 }
 \examples{
-if (require("lme4")) {
-  fit <- lmer(Reaction ~ 1 + (1 | Subject), sleepstudy)
-  se_ybar(fit)
-}
+\dontshow{if (require("lme4")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf}
+fit <- lmer(Reaction ~ 1 + (1 | Subject), sleepstudy)
+se_ybar(fit)
+\dontshow{\}) # examplesIf}
 }
 \references{
 Gelman A, Hill J. 2007. Data analysis using regression and multilevel/hierarchical models. Cambridge, New York: Cambridge University Press