diff --git a/NAMESPACE b/NAMESPACE
index f586c75..7947a77 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -82,7 +82,6 @@ export(find_beta2)
 export(find_cauchy)
 export(find_normal)
 export(gmd)
-export(grpmean)
 export(icc)
 export(inequ_trend)
 export(is_prime)
@@ -131,14 +130,10 @@ importFrom(dplyr,case_when)
 importFrom(dplyr,filter)
 importFrom(dplyr,group_vars)
 importFrom(dplyr,mutate)
-importFrom(dplyr,n_distinct)
-importFrom(dplyr,pull)
 importFrom(dplyr,quos)
 importFrom(dplyr,select)
 importFrom(dplyr,select_if)
 importFrom(dplyr,summarise)
-importFrom(emmeans,contrast)
-importFrom(emmeans,emmeans)
 importFrom(insight,export_table)
 importFrom(insight,find_formula)
 importFrom(insight,find_response)
@@ -156,7 +151,6 @@ importFrom(performance,rmse)
 importFrom(purrr,flatten_df)
 importFrom(purrr,map)
 importFrom(purrr,map2)
-importFrom(purrr,map_chr)
 importFrom(purrr,map_dbl)
 importFrom(purrr,map_df)
 importFrom(purrr,map_lgl)
@@ -165,15 +159,10 @@ importFrom(rlang,.data)
 importFrom(rlang,enquo)
 importFrom(rlang,quo_name)
 importFrom(sjlabelled,as_numeric)
-importFrom(sjlabelled,drop_labels)
-importFrom(sjlabelled,get_label)
-importFrom(sjlabelled,get_labels)
-importFrom(sjmisc,add_variables)
 importFrom(sjmisc,is_empty)
 importFrom(sjmisc,is_float)
 importFrom(sjmisc,is_num_fac)
 importFrom(sjmisc,str_contains)
-importFrom(sjmisc,to_value)
 importFrom(sjmisc,trim)
 importFrom(sjmisc,typical_value)
 importFrom(stats,approx)
diff --git a/R/Deprecated.R b/R/Deprecated.R
index 862b8da..94a27e2 100644
--- a/R/Deprecated.R
+++ b/R/Deprecated.R
@@ -83,3 +83,19 @@ se <- function(x, ...) {
   .Defunct("parameters::standard_error()")
   parameters::standard_error(x)
 }
+
+
+#' @rdname r2
+#' @export
+means_by_group <- function(x, ...) {
+  .Defunct("datawizard::means_by_group()")
+  datawizard::means_by_group(x, ...)
+}
+
+
+#' @rdname r2
+#' @export
+mean_n <- function(x, ...) {
+  .Defunct("datawizard::row_means()")
+  datawizard::row_means(x, ...)
+}
diff --git a/R/grpmean.R b/R/grpmean.R
deleted file mode 100644
index ab2eb2d..0000000
--- a/R/grpmean.R
+++ /dev/null
@@ -1,335 +0,0 @@
-#' @title Summary of mean values by group
-#' @name means_by_group
-#'
-#' @description Computes mean, sd and se for each sub-group (indicated by \code{grp})
-#'                of \code{dv}.
-#'
-#' @param x A (grouped) data frame.
-#' @param dv Name of the dependent variable, for which the mean value, grouped
-#'   by \code{grp}, is computed.
-#' @param grp Factor with the cross-classifying variable, where \code{dv} is
-#'   grouped into the categories represented by \code{grp}. Numeric vectors
-#'   are coerced to factors.
-#' @param weights Name of variable in \code{x} that indicated the vector of
-#'   weights that will be applied to weight all observations. Default is
-#'   \code{NULL}, so no weights are used.
-#' @param digits Numeric, amount of digits after decimal point when rounding
-#'   estimates and values.
-#' @param file Destination file, if the output should be saved as file.
-#'   Only used when \code{out} is not \code{"txt"}.
-#' @param encoding Character vector, indicating the charset encoding used
-#'   for variable and value labels. Default is \code{"UTF-8"}. Only used
-#'   when \code{out} is not \code{"txt"}.
-#' @param out Character vector, indicating whether the results should be printed
-#'    to console (\code{out = "txt"}) or as HTML-table in the viewer-pane
-#'    (\code{out = "viewer"}) or browser (\code{out = "browser"}), of if the
-#'    results should be plotted (\code{out = "plot"}, only applies to certain
-#'    functions). May be abbreviated.
-#'
-#' @return For non-grouped data frames, \code{means_by_group()} returns a data frame with
-#'   following columns: \code{term}, \code{mean}, \code{N}, \code{std.dev},
-#'   \code{std.error} and \code{p.value}. For grouped data frames, returns
-#'   a list of such data frames.
-#'
-#' @details This function performs a One-Way-Anova with \code{dv} as dependent
-#'   and \code{grp} as independent variable, by calling
-#'   \code{lm(count ~ as.factor(grp))}. Then \code{\link[emmeans]{contrast}}
-#'   is called to get p-values for each sub-group. P-values indicate whether
-#'   each group-mean is significantly different from the total mean.
-#'
-#' @examples
-#' data(efc)
-#' means_by_group(efc, c12hour, e42dep)
-#'
-#' data(iris)
-#' means_by_group(iris, Sepal.Width, Species)
-#'
-#' # also works for grouped data frames
-#' if (require("dplyr")) {
-#'   efc %>%
-#'     group_by(c172code) %>%
-#'     means_by_group(c12hour, e42dep)
-#' }
-#'
-#' # weighting
-#' efc$weight <- abs(rnorm(n = nrow(efc), mean = 1, sd = .5))
-#' means_by_group(efc, c12hour, e42dep, weights = weight)
-#' @importFrom sjlabelled get_label drop_labels get_labels
-#' @importFrom stats lm na.omit sd weighted.mean
-#' @importFrom purrr map_chr map_df
-#' @importFrom sjmisc to_value is_empty
-#' @importFrom rlang enquo .data quo_name
-#' @export
-means_by_group <- function(x,
-                          dv,
-                          grp,
-                          weights = NULL,
-                          digits = 2,
-                          out = c("txt", "viewer", "browser"),
-                          encoding = "UTF-8",
-                          file = NULL) {
-
-  out <- match.arg(out)
-
-  if (out != "txt" && !requireNamespace("sjPlot", quietly = TRUE)) {
-    message("Package `sjPlot` needs to be loaded to print HTML tables.")
-    out <- "txt"
-  }
-
-  # create quosures
-  grp.name <- rlang::quo_name(rlang::enquo(grp))
-  dv.name <- rlang::quo_name(rlang::enquo(dv))
-
-  # weights need extra checking, might be NULL
-  if (!missing(weights)) {
-    .weights <- try(rlang::quo_name(rlang::enquo(weights)), silent = TRUE)
-    if (inherits(.weights, "try-error")) .weights <- NULL
-
-    w.string <- try(eval(weights), silent = TRUE)
-    if (!inherits(w.string, "try-error") && !is.null(w.string) && is.character(w.string)) .weights <- w.string
-
-    if (sjmisc::is_empty(.weights) || .weights == "NULL") .weights <- NULL
-  } else
-    .weights <- NULL
-
-
-  # create string with variable names
-  vars <- c(grp.name, dv.name, .weights)
-
-  # get data
-  x <- suppressMessages(dplyr::select(x, !! vars))
-
-  # set value and row labels
-  varGrpLabel <- sjlabelled::get_label(x[[grp.name]], def.value = grp.name)
-  varCountLabel <- sjlabelled::get_label(x[[dv.name]], def.value = dv.name)
-
-  # first, drop unused labels
-  x[[grp.name]] <- sjlabelled::drop_labels(x[[grp.name]], drop.na = TRUE)
-
-  # now get valid value labels
-  value.labels <- sjlabelled::get_labels(
-    x[[grp.name]], attr.only = F, values = "n", non.labelled = TRUE
-  )
-
-  # return values
-  dataframes <- list()
-
-  # do we have a grouped data frame?
-  if (inherits(x, "grouped_df")) {
-    # get grouped data
-    grps <- get_grouped_data(x)
-
-    # now plot everything
-    for (i in seq_len(nrow(grps))) {
-      # copy back labels to grouped data frame
-      tmp <- sjlabelled::copy_labels(grps$data[[i]], x)
-
-      # get grouped means table
-      dummy <- means_by_group_helper(
-        x = tmp,
-        dv = dv.name,
-        grp = grp.name,
-        weight.by = .weights,
-        value.labels = value.labels,
-        varCountLabel = varCountLabel,
-        varGrpLabel = varGrpLabel
-      )
-
-      attr(dummy, "group") <- get_grouped_title(x, grps, i, sep = "\n")
-
-      # save data frame for return value
-      dataframes[[length(dataframes) + 1]] <- dummy
-    }
-
-    # add class-attr for print-method()
-    if (out == "txt")
-      class(dataframes) <- c("sj_grpmeans", "list")
-    else
-      class(dataframes) <- c("sjt_grpmeans", "list")
-
-  } else {
-    dataframes <- means_by_group_helper(
-      x = x,
-      dv = dv.name,
-      grp = grp.name,
-      weight.by = .weights,
-      value.labels = value.labels,
-      varCountLabel = varCountLabel,
-      varGrpLabel = varGrpLabel
-    )
-
-    # add class-attr for print-method()
-    if (out == "txt")
-      class(dataframes) <- c("sj_grpmean", class(dataframes))
-    else
-      class(dataframes) <- c("sjt_grpmean", class(dataframes))
-  }
-
-  # save how to print output
-  attr(dataframes, "print") <- out
-  attr(dataframes, "encoding") <- encoding
-  attr(dataframes, "file") <- file
-  attr(dataframes, "digits") <- digits
-
-  dataframes
-}
-
-
-#' @importFrom stats pf lm weighted.mean na.omit sd
-#' @importFrom sjmisc to_value add_variables
-#' @importFrom emmeans emmeans contrast
-#' @importFrom dplyr pull select n_distinct
-#' @importFrom purrr map_chr
-#' @importFrom rlang .data
-means_by_group_helper <- function(x, dv, grp, weight.by, value.labels, varCountLabel, varGrpLabel) {
-  # copy vectors from data frame
-  dv <- x[[dv]]
-  grp <- x[[grp]]
-
-  if (!is.null(weight.by))
-    weight.by <- x[[weight.by]]
-  else
-    weight.by <- 1
-
-  # convert values to numeric
-  dv <- sjmisc::to_value(dv)
-
-  # create data frame, for emmeans
-  mydf <- stats::na.omit(data.frame(
-    dv = dv,
-    grp = as.factor(grp),
-    weight.by = weight.by
-  ))
-
-  # compute anova statistics for mean table
-  fit <- stats::lm(dv ~ grp, weights = weight.by, data = mydf)
-
-  # p-values of contrast-means
-  means.p <- fit %>%
-    emmeans::emmeans(specs = "grp") %>%
-    emmeans::contrast(method = "eff") %>%
-    summary() %>%
-    dplyr::pull("p.value")
-
-  ## TODO
-  # efc %>%
-  #   group_by(c172code, c161sex) %>%
-  #   means_by_group(c12hour, e42dep)
-
-
-  # check if value labels length matches group count
-  if (dplyr::n_distinct(mydf$grp) != length(value.labels)) {
-    # get unique factor levels and check if these are numeric.
-    # if so, we match the values from value labels and the remaining
-    # factor levels, so we get the correct value labels for printing
-    nl <- unique(mydf$grp)
-    if (sjmisc::is_num_fac(nl))
-      value.labels <- value.labels[names(value.labels) %in% levels(nl)]
-    else
-      value.labels <- nl
-  }
-
-
-  # create summary
-  dat <- mydf %>%
-    dplyr::group_by(.data$grp) %>%
-    summarise(
-      mean = stats::weighted.mean(.data$dv, w = .data$weight.by, na.rm = TRUE),
-      N = round(sum(.data$weight.by)),
-      std.dev = weighted_sd(.data$dv, .data$weight.by),
-      std.error = weighted_se(.data$dv, .data$weight.by)
-    ) %>%
-    mutate(p.value = means.p) %>%
-    dplyr::select(-.data$grp)
-
-  # finally, add total-row
-  dat <- dplyr::bind_rows(
-    dat,
-    data_frame(
-      mean = stats::weighted.mean(mydf$dv, w = mydf$weight.by, na.rm = TRUE),
-      N = nrow(mydf),
-      std.dev = weighted_sd(mydf$dv, mydf$weight.by),
-      std.error = weighted_se(mydf$dv, mydf$weight.by),
-      p.value = NA
-    )
-  )
-
-
-  # add row labels
-  dat <- sjmisc::add_variables(
-    dat,
-    term = c(unname(value.labels), "Total"),
-    .after = -1
-  )
-
-
-  # get anova statistics for mean table
-  sum.fit <- summary(fit)
-
-  # r-squared values
-  r2 <- sum.fit$r.squared
-  r2.adj <- sum.fit$adj.r.squared
-
-  # F-statistics
-  fstat <- sum.fit$fstatistic
-  pval <- stats::pf(fstat[1], fstat[2], fstat[3], lower.tail = F)
-
-
-  # copy as attributes
-  attr(dat, "r2") <- r2
-  attr(dat, "adj.r2") <- r2.adj
-  attr(dat, "fstat") <- fstat[1]
-  attr(dat, "p.value") <- pval
-  attr(dat, "dv.label") <- varCountLabel
-  attr(dat, "grp.label") <- varGrpLabel
-
-  dat
-}
-
-
-get_grouped_title <- function(x, grps, i, sep = "\n") {
-  # create title for first grouping level
-  tp <- get_title_part(x, grps, 1, i)
-  title <- sprintf("%s: %s", tp[1], tp[2])
-
-  # do we have another groupng variable?
-  if (length(dplyr::group_vars(x)) > 1) {
-    tp <- get_title_part(x, grps, 2, i)
-    title <- sprintf("%s%s%s: %s", title, sep, tp[1], tp[2])
-  }
-
-  # return title
-  title
-}
-
-
-get_title_part <- function(x, grps, level, i) {
-  # prepare title for group
-  var.name <- colnames(grps)[level]
-
-  # get values from value labels
-  vals <- sjlabelled::get_values(x[[var.name]])
-  # if we have no value labels, get values directly
-  if (is.null(vals)) {
-    vals <- unique(x[[var.name]])
-    lab.pos <- i
-  } else {
-    # find position of value labels for current group
-    lab.pos <- which(vals == grps[[var.name]][i])
-  }
-
-  # get variable and value labels
-  t1 <- sjlabelled::get_label(x[[var.name]], def.value = var.name)
-  t2 <- sjlabelled::get_labels(x[[var.name]])[lab.pos]
-
-  # if we have no value label, use value instead
-  if (is.null(t2)) t2 <- vals[lab.pos]
-
-  # generate title
-  c(t1, t2)
-}
-
-
-#' @rdname means_by_group
-#' @export
-grpmean <- means_by_group
diff --git a/R/mean_n.R b/R/mean_n.R
deleted file mode 100644
index 5d6a2e0..0000000
--- a/R/mean_n.R
+++ /dev/null
@@ -1,78 +0,0 @@
-#' @title Row means with min amount of valid values
-#' @name mean_n
-#' @description This function is similar to the SPSS \code{MEAN.n} function and computes
-#'                row means from a \code{data.frame} or \code{matrix} if at least \code{n}
-#'                values of a row are valid (and not \code{NA}).
-#'
-#' @param dat A data frame with at least two columns, where row means are applied.
-#' @param n May either be
-#'          \itemize{
-#'            \item a numeric value that indicates the amount of valid values per row to calculate the row mean;
-#'            \item or a value between 0 and 1, indicating a proportion of valid values per row to calculate the row mean (see 'Details').
-#'          }
-#'          If a row's sum of valid values is less than \code{n}, \code{NA} will be returned as row mean value.
-#' @param digits Numeric value indicating the number of decimal places to be used for rounding mean
-#'          value. Negative values are allowed (see 'Details').
-#'
-#' @return A vector with row mean values of \code{df} for those rows with at least \code{n}
-#'           valid values. Else, \code{NA} is returned.
-#'
-#' @details Rounding to a negative number of \code{digits} means rounding to a power of
-#'            ten, so for example mean_n(df, 3, digits = -2) rounds to the
-#'            nearest hundred. \cr \cr
-#'          For \code{n}, must be a numeric value from \code{0} to \code{ncol(dat)}. If
-#'            a \emph{row} in \code{dat} has at least \code{n} non-missing values, the
-#'            row mean is returned. If \code{n} is a non-integer value from 0 to 1,
-#'            \code{n} is considered to indicate the proportion of necessary non-missing
-#'            values per row. E.g., if \code{n = .75}, a row must have at least \code{ncol(dat) * n}
-#'            non-missing values for the row mean to be calculated. See 'Examples'.
-#'
-#' @references \href{https://r4stats.com/2014/09/03/adding-the-spss-mean-n-function-to-r/}{r4stats.com}
-#'
-#' @examples
-#' dat <- data.frame(c1 = c(1,2,NA,4),
-#'                   c2 = c(NA,2,NA,5),
-#'                   c3 = c(NA,4,NA,NA),
-#'                   c4 = c(2,3,7,8))
-#'
-#' # needs at least 4 non-missing values per row
-#' mean_n(dat, 4) # 1 valid return value
-#'
-#' # needs at least 3 non-missing values per row
-#' mean_n(dat, 3) # 2 valid return values
-#'
-#' # needs at least 2 non-missing values per row
-#' mean_n(dat, 2)
-#'
-#' # needs at least 1 non-missing value per row
-#' mean_n(dat, 1) # all means are shown
-#'
-#' # needs at least 50% of non-missing values per row
-#' mean_n(dat, .5) # 3 valid return values
-#'
-#' # needs at least 75% of non-missing values per row
-#' mean_n(dat, .75) # 2 valid return values
-#'
-#' @export
-mean_n <- function(dat, n, digits = 2) {
-  # is 'n' indicating a proportion?
-  digs <- n %% 1
-  if (digs != 0) n <- round(ncol(dat) * digs)
-
-  # coerce matrix to data frame
-  if (is.matrix(dat)) dat <- as.data.frame(dat)
-
-  # check if we have a data framme with at least two columns
-  if (!is.data.frame(dat) || ncol(dat) < 2) {
-    warning("`dat` must be a data frame with at least two columns.", call. = TRUE)
-    return(NA)
-  }
-
-  # n may not be larger as df's amount of columns
-  if (ncol(dat) < n) {
-    warning("`n` must be smaller or equal to number of columns in data frame.", call. = TRUE)
-    return(NA)
-  }
-
-  round(apply(dat, 1, function(x) ifelse(sum(!is.na(x)) >= n, mean(x, na.rm = TRUE), NA)), digits)
-}
diff --git a/_pkgdown.yml b/_pkgdown.yml
index cf57a80..bfe8987 100644
--- a/_pkgdown.yml
+++ b/_pkgdown.yml
@@ -32,8 +32,6 @@ reference:
   - gmd
   - mann_whitney_test
   - chi_squared_test
-  - mean_n
-  - means_by_group
   - var_pop
 
 - title: "Tools for Regression Models"
diff --git a/man/crosstable_statistics.Rd b/man/crosstable_statistics.Rd
index 358c283..20b2f03 100644
--- a/man/crosstable_statistics.Rd
+++ b/man/crosstable_statistics.Rd
@@ -81,10 +81,6 @@ will be irgnored.}
 \item{statistics}{Name of measure of association that should be computed. May
 be one of \code{"auto"}, \code{"cramer"}, \code{"phi"}, \code{"spearman"},
 \code{"kendall"}, \code{"pearson"} or \code{"fisher"}. See 'Details'.}
-
-\item{weights}{Name of variable in \code{x} that indicated the vector of
-weights that will be applied to weight all observations. Default is
-\code{NULL}, so no weights are used.}
 }
 \value{
 For \code{phi()}, the table's Phi value. For \code{cramer()}, the
diff --git a/man/mean_n.Rd b/man/mean_n.Rd
deleted file mode 100644
index 35f6915..0000000
--- a/man/mean_n.Rd
+++ /dev/null
@@ -1,69 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/mean_n.R
-\name{mean_n}
-\alias{mean_n}
-\title{Row means with min amount of valid values}
-\usage{
-mean_n(dat, n, digits = 2)
-}
-\arguments{
-\item{dat}{A data frame with at least two columns, where row means are applied.}
-
-\item{n}{May either be
-\itemize{
-\item a numeric value that indicates the amount of valid values per row to calculate the row mean;
-\item or a value between 0 and 1, indicating a proportion of valid values per row to calculate the row mean (see 'Details').
-}
-If a row's sum of valid values is less than \code{n}, \code{NA} will be returned as row mean value.}
-
-\item{digits}{Numeric value indicating the number of decimal places to be used for rounding mean
-value. Negative values are allowed (see 'Details').}
-}
-\value{
-A vector with row mean values of \code{df} for those rows with at least \code{n}
-valid values. Else, \code{NA} is returned.
-}
-\description{
-This function is similar to the SPSS \code{MEAN.n} function and computes
-row means from a \code{data.frame} or \code{matrix} if at least \code{n}
-values of a row are valid (and not \code{NA}).
-}
-\details{
-Rounding to a negative number of \code{digits} means rounding to a power of
-ten, so for example mean_n(df, 3, digits = -2) rounds to the
-nearest hundred. \cr \cr
-For \code{n}, must be a numeric value from \code{0} to \code{ncol(dat)}. If
-a \emph{row} in \code{dat} has at least \code{n} non-missing values, the
-row mean is returned. If \code{n} is a non-integer value from 0 to 1,
-\code{n} is considered to indicate the proportion of necessary non-missing
-values per row. E.g., if \code{n = .75}, a row must have at least \code{ncol(dat) * n}
-non-missing values for the row mean to be calculated. See 'Examples'.
-}
-\examples{
-dat <- data.frame(c1 = c(1,2,NA,4),
-                  c2 = c(NA,2,NA,5),
-                  c3 = c(NA,4,NA,NA),
-                  c4 = c(2,3,7,8))
-
-# needs at least 4 non-missing values per row
-mean_n(dat, 4) # 1 valid return value
-
-# needs at least 3 non-missing values per row
-mean_n(dat, 3) # 2 valid return values
-
-# needs at least 2 non-missing values per row
-mean_n(dat, 2)
-
-# needs at least 1 non-missing value per row
-mean_n(dat, 1) # all means are shown
-
-# needs at least 50\% of non-missing values per row
-mean_n(dat, .5) # 3 valid return values
-
-# needs at least 75\% of non-missing values per row
-mean_n(dat, .75) # 2 valid return values
-
-}
-\references{
-\href{https://r4stats.com/2014/09/03/adding-the-spss-mean-n-function-to-r/}{r4stats.com}
-}
diff --git a/man/means_by_group.Rd b/man/means_by_group.Rd
deleted file mode 100644
index ad82ba3..0000000
--- a/man/means_by_group.Rd
+++ /dev/null
@@ -1,94 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/grpmean.R
-\name{means_by_group}
-\alias{means_by_group}
-\alias{grpmean}
-\title{Summary of mean values by group}
-\usage{
-means_by_group(
-  x,
-  dv,
-  grp,
-  weights = NULL,
-  digits = 2,
-  out = c("txt", "viewer", "browser"),
-  encoding = "UTF-8",
-  file = NULL
-)
-
-grpmean(
-  x,
-  dv,
-  grp,
-  weights = NULL,
-  digits = 2,
-  out = c("txt", "viewer", "browser"),
-  encoding = "UTF-8",
-  file = NULL
-)
-}
-\arguments{
-\item{x}{A (grouped) data frame.}
-
-\item{dv}{Name of the dependent variable, for which the mean value, grouped
-by \code{grp}, is computed.}
-
-\item{grp}{Factor with the cross-classifying variable, where \code{dv} is
-grouped into the categories represented by \code{grp}. Numeric vectors
-are coerced to factors.}
-
-\item{weights}{Name of variable in \code{x} that indicated the vector of
-weights that will be applied to weight all observations. Default is
-\code{NULL}, so no weights are used.}
-
-\item{digits}{Numeric, amount of digits after decimal point when rounding
-estimates and values.}
-
-\item{out}{Character vector, indicating whether the results should be printed
-to console (\code{out = "txt"}) or as HTML-table in the viewer-pane
-(\code{out = "viewer"}) or browser (\code{out = "browser"}), of if the
-results should be plotted (\code{out = "plot"}, only applies to certain
-functions). May be abbreviated.}
-
-\item{encoding}{Character vector, indicating the charset encoding used
-for variable and value labels. Default is \code{"UTF-8"}. Only used
-when \code{out} is not \code{"txt"}.}
-
-\item{file}{Destination file, if the output should be saved as file.
-Only used when \code{out} is not \code{"txt"}.}
-}
-\value{
-For non-grouped data frames, \code{means_by_group()} returns a data frame with
-following columns: \code{term}, \code{mean}, \code{N}, \code{std.dev},
-\code{std.error} and \code{p.value}. For grouped data frames, returns
-a list of such data frames.
-}
-\description{
-Computes mean, sd and se for each sub-group (indicated by \code{grp})
-of \code{dv}.
-}
-\details{
-This function performs a One-Way-Anova with \code{dv} as dependent
-and \code{grp} as independent variable, by calling
-\code{lm(count ~ as.factor(grp))}. Then \code{\link[emmeans]{contrast}}
-is called to get p-values for each sub-group. P-values indicate whether
-each group-mean is significantly different from the total mean.
-}
-\examples{
-data(efc)
-means_by_group(efc, c12hour, e42dep)
-
-data(iris)
-means_by_group(iris, Sepal.Width, Species)
-
-# also works for grouped data frames
-if (require("dplyr")) {
-  efc \%>\%
-    group_by(c172code) \%>\%
-    means_by_group(c12hour, e42dep)
-}
-
-# weighting
-efc$weight <- abs(rnorm(n = nrow(efc), mean = 1, sd = .5))
-means_by_group(efc, c12hour, e42dep, weights = weight)
-}
diff --git a/man/r2.Rd b/man/r2.Rd
index 4b64705..d31c90e 100644
--- a/man/r2.Rd
+++ b/man/r2.Rd
@@ -11,6 +11,8 @@
 \alias{icc}
 \alias{p_value}
 \alias{se}
+\alias{means_by_group}
+\alias{mean_n}
 \title{Deprecated functions}
 \usage{
 r2(x)
@@ -32,6 +34,10 @@ icc(x)
 p_value(x, ...)
 
 se(x, ...)
+
+means_by_group(x, ...)
+
+mean_n(x, ...)
 }
 \arguments{
 \item{x}{An object.}