From 7614c387357d0a876b72ab7e24b70052cd298662 Mon Sep 17 00:00:00 2001
From: "Mattan S. Ben-Shachar" <mattansb@msbstats.info>
Date: Sat, 10 Sep 2022 19:03:00 +0300
Subject: [PATCH 01/14] initial rename

---
 NAMESPACE                        |  2 ++
 NEWS.md                          |  5 ++++-
 R/convert_stat_chisq.R           | 18 ++++++++--------
 R/effectsize.R                   |  2 +-
 R/effectsize.htest.R             |  9 ++++----
 R/interpret.R                    |  1 +
 R/is_effectsize_name.R           |  2 +-
 R/xtab.R                         | 37 ++++++++++++++++----------------
 R/zzz_deprecated.R               | 18 ++++++++++++++++
 man/chisq_to_phi.Rd              | 27 ++++++++---------------
 man/effectsize.Rd                |  2 +-
 man/effectsize_deprecated.Rd     |  8 +++++++
 man/phi.Rd                       | 35 +++++++++++++++---------------
 tests/testthat/test-effectsize.R |  4 ++--
 tests/testthat/test-xtab.R       | 14 ++++++------
 vignettes/simple_htests.Rmd      |  4 ++--
 16 files changed, 105 insertions(+), 83 deletions(-)

diff --git a/NAMESPACE b/NAMESPACE
index 2f38db059..342627d5a 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -53,6 +53,7 @@ export(F_to_omega2)
 export(F_to_r)
 export(chisq_to_cohens_w)
 export(chisq_to_cramers_v)
+export(chisq_to_fei)
 export(chisq_to_normalized)
 export(chisq_to_pearsons_c)
 export(chisq_to_phi)
@@ -93,6 +94,7 @@ export(eta_squared)
 export(eta_squared_posterior)
 export(f2_to_eta2)
 export(f_to_eta2)
+export(fei)
 export(format_standardize)
 export(get_effectsize_label)
 export(get_effectsize_name)
diff --git a/NEWS.md b/NEWS.md
index 064e34ba7..3e6c941d3 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,6 +1,9 @@
-
 # effectsize 0.7.0.9999
 
+## Breaking Changes
+
+- `normalized_chi()` has been renamed `fei()`.
+
 ## Bug fixes
 
 - Effect sizes for goodness-of-fit now work when passing a `p` that is a table.
diff --git a/R/convert_stat_chisq.R b/R/convert_stat_chisq.R
index e56605236..9a9a07a68 100644
--- a/R/convert_stat_chisq.R
+++ b/R/convert_stat_chisq.R
@@ -1,8 +1,8 @@
 #' Conversion Chi-Squared to Phi or Cramer's V
 #'
 #' Convert between Chi square (\eqn{\chi^2}), Cramer's V, phi (\eqn{\phi}),
-#' Cohen's *w*, normalized Chi (\eqn{\chi}) and Pearson's *C* for contingency
-#' tables or goodness of fit.
+#' Cohen's *w*, Fei (\eqn{פ}) and Pearson's *C* for contingency tables or
+#' goodness of fit.
 #'
 #' @param chisq The Chi-squared statistic.
 #' @param n Total sample size.
@@ -21,13 +21,13 @@
 #'
 #' @details These functions use the following formulae:
 #' \cr
-#' \deqn{\phi = \sqrt{\chi^2 / n}}{phi = sqrt(\chi^2 / n)}
+#' \deqn{\phi = w = \sqrt{\chi^2 / n}}{phi = w = sqrt(\chi^2 / n)}
 #' \cr
-#' \deqn{Cramer's V = \phi / \sqrt{min(nrow,ncol)-1}}{Cramer's V = \phi / sqrt(min(nrow,ncol)-1)}
+#' \deqn{\text{Cramer's }V = \phi / \sqrt{min(nrow,ncol)-1}}{Cramer's V = \phi / sqrt(min(nrow,ncol)-1)}
 #' \cr
-#' \deqn{Pearson's C = \sqrt{\chi^2 / (\chi^2 + n)}}{Pearson's C = sqrt(\chi^2 / (\chi^2 + n))}
+#' \deqn{\text{Pearson's }C = \sqrt{\chi^2 / (\chi^2 + n)}}{Pearson's C = sqrt(\chi^2 / (\chi^2 + n))}
 #' \cr
-#' \deqn{\chi_{Normalized} = w \times \sqrt{\frac{q}{1-q}}}{Chi (Normalized) = w * sqrt(q/(1-q))}
+#' \deqn{פ = w \times \sqrt{\frac{q}{1-q}}}{Fei = w * sqrt(q/(1-q))}
 #' Where `q` is the smallest of the expected probabilities.
 #' \cr\cr
 #' For adjusted versions of *phi* and *V*, see Bergsma, 2013.
@@ -65,7 +65,7 @@
 #' #> data:  Smoking_ASD
 #' #> X-squared = 7.8521, df = 2, p-value = 0.01972
 #'
-#' chisq_to_normalized(
+#' chisq_to_fei(
 #'   7.8521,
 #'   n = sum(Smoking_ASD),
 #'   nrow = 1,
@@ -229,7 +229,7 @@ chisq_to_cramers_v <- function(chisq, n, nrow, ncol, ci = 0.95, alternative = "g
 #' @rdname chisq_to_phi
 #' @export
 #' @param p Vector of expected values. See [stats::chisq.test()].
-chisq_to_normalized <- function(chisq, n, nrow, ncol, p,
+chisq_to_fei <- function(chisq, n, nrow, ncol, p,
                                 ci = 0.95, alternative = "greater", ...) {
   if (is.numeric(ci) || (!missing(nrow) && !missing(ncol))) {
     # This means that the user passed ncol/nrow
@@ -255,7 +255,7 @@ chisq_to_normalized <- function(chisq, n, nrow, ncol, p,
   N <- n * (1 - q) / q
 
   res <- chisq_to_phi(chisq, N, nrow, ncol, ci = ci, alternative = alternative, adjust = FALSE, dont_stop = TRUE)
-  colnames(res)[1] <- "normalized_chi"
+  colnames(res)[1] <- "Fei"
 
   if ("CI" %in% colnames(res)) {
     if ((alternative <- attr(res, "alternative")) == "less") {
diff --git a/R/effectsize.R b/R/effectsize.R
index 9067412bc..ff09bd9d9 100644
--- a/R/effectsize.R
+++ b/R/effectsize.R
@@ -13,7 +13,7 @@
 #' - For an object of class `htest`, data is extracted via [insight::get_data()], and passed to the relevant function according to:
 #'   - A **t-test** depending on `type`: `"cohens_d"` (default), `"hedges_g"`, or `"cles"`.
 #'   - A **Chi-squared tests of independence**, depending on `type`: `"cramers_v"` (default), `"phi"`, `"cohens_w"`, `"pearsons_c"`, `"cohens_h"`, `"oddsratio"`, or `"riskratio"`.
-#'   - A **Chi-squared tests of goodness-of-fit**, depending on `type`: `"normalized_chi"` (default) `"cohens_w"`, `"pearsons_c"`
+#'   - A **Chi-squared tests of goodness-of-fit**, depending on `type`: `"fei"` (default) `"cohens_w"`, `"pearsons_c"`
 #'   - A **One-way ANOVA test**, depending on `type`: `"eta"` (default), `"omega"` or `"epsilon"` -squared, `"f"`, or `"f2"`.
 #'   - A **McNemar test** returns *Cohen's g*.
 #'   - A **Wilcoxon test** depending on `type`: returns "`rank_biserial`" correlation (default) or `"cles"`.
diff --git a/R/effectsize.htest.R b/R/effectsize.htest.R
index a9baaf5f8..6cf02623d 100644
--- a/R/effectsize.htest.R
+++ b/R/effectsize.htest.R
@@ -114,14 +114,14 @@ effectsize.htest <- function(model, type = NULL, verbose = TRUE, ...) {
 
   if (is.null(type)) {
     if (nr == 1 || nc == 1) {
-      type <- "normalized_chi"
+      type <- "fei"
     } else {
       type <- "cramers_v"
     }
   }
 
-  if (grepl("(c|v|w|phi)$", tolower(type)) || tolower(type) %in% c("normalized_chi", "chi")) {
-    if (tolower(type) %in% c("normalized_chi", "chi")) {
+  if (grepl("(c|v|w|phi|fei)$", tolower(type))) {
+    if (tolower(type) == "fei") {
       p <- Exp
     } else {
       p <- NULL
@@ -136,7 +136,8 @@ effectsize.htest <- function(model, type = NULL, verbose = TRUE, ...) {
       c = ,
       pearsons_c = chisq_to_pearsons_c,
       chi = ,
-      normalized_chi = chisq_to_normalized
+      normalized_chi = ,
+      fei = chisq_to_fei
     )
 
     out <- f(
diff --git a/R/interpret.R b/R/interpret.R
index 7c6e68356..4fb013eea 100644
--- a/R/interpret.R
+++ b/R/interpret.R
@@ -172,6 +172,7 @@ interpret.effectsize_table <- function(x, rules, ...) {
     Cramers_v = ,
     Cramers_v_adjusted = ,
     normalized_chi = ,
+    fei = ,
     Cohens_w = ,
     phi = ,
     phi_adjusted = interpret_cramers_v(value, rules = rules),
diff --git a/R/is_effectsize_name.R b/R/is_effectsize_name.R
index ec353d654..bf548a24f 100644
--- a/R/is_effectsize_name.R
+++ b/R/is_effectsize_name.R
@@ -55,7 +55,7 @@ es_info <- matrix(
     "phi_adjusted", "Phi (adj.)", "onetail", 0, 1, 0,
     "Pearsons_c", "Pearson's C", "onetail", 0, 1, 0,
     "Cohens_w", "Cohen's w", "onetail", 0, Inf, 0,
-    "normalized_chi", "Norm. Chi", "onetail", 0, 1, 0,
+    "Fei", "Fei", "onetail", 0, 1, 0,
     "Cohens_g", "Cohen's g", "onetail", -0.5, 0.5, 0,
     "Cohens_h", "Cohen's h", "twotail", -pi, pi, 0,
     "Odds_ratio", "Odds ratio", "twotail", 0, Inf, 1,
diff --git a/R/xtab.R b/R/xtab.R
index ac8c4d211..33cba6292 100644
--- a/R/xtab.R
+++ b/R/xtab.R
@@ -1,9 +1,8 @@
 #' Effect size for contingency tables
 #'
-#' Compute Cramer's *V*, phi (\eqn{\phi}), Cohen's *w*, normalized Chi
-#' (\eqn{\chi}), Pearson's contingency coefficient, Odds ratios, Risk ratios,
-#' Cohen's *h* and Cohen's *g* for contingency tables or goodness-of-fit. See
-#' details.
+#' Compute Cramer's *V*, phi (\eqn{\phi}), Cohen's *w*, Fei (\eqn{פ}), Pearson's
+#' contingency coefficient, Odds ratios, Risk ratios, Cohen's *h* and Cohen's
+#' *g* for contingency tables or goodness-of-fit. See details.
 #'
 #' @inheritParams stats::chisq.test
 #' @param ci Confidence Interval (CI) level
@@ -30,17 +29,17 @@
 #' can also be used, but since it is not bounded at 1 (can be larger) its
 #' interpretation is more difficult.
 #' \cr \cr
-#' For goodness-of-fit in 1D tables Cohen's *W*, normalized Chi (\eqn{\chi}) or
-#' Pearson's *C* can be used. Cohen's *w* has no upper bound (can be arbitrarily
-#' large, depending on the expected distribution). Normalized Chi is an adjusted
-#' Cohen's *w*, accounting for the expected distribution, making it bounded
-#' between 0-1. Pearson's *C* is also bounded between 0-1.
+#' For goodness-of-fit in 1D tables Cohen's *W*, Fei (\eqn{פ}) or Pearson's *C*
+#' can be used. Cohen's *w* has no upper bound (can be arbitrarily large,
+#' depending on the expected distribution). Fei is an adjusted Cohen's *w*,
+#' accounting for the expected distribution, making it bounded between 0-1.
+#' Pearson's *C* is also bounded between 0-1.
 #' \cr \cr
 #' To summarize, for correlation-like effect sizes, we recommend:
 #'
 #' - For a 2x2 table, use `phi()`
 #' - For larger tables, use `cramers_v()`
-#' - For goodness-of-fit, use `normalized_chi()`
+#' - For goodness-of-fit, use `fei()`
 #'
 #' ## Other Effect Sizes for 2-by-2 xtabs
 #'
@@ -69,15 +68,15 @@
 #' \cr \cr
 #' See *Confidence (Compatibility) Intervals (CIs)*, *CIs and Significance Tests*,
 #' and *One-Sided CIs* sections for *phi*, Cohen's *w*, Cramer's *V*,
-#' Pearson's *C*, and normalized Chi.
+#' Pearson's *C*, and *Fei*.
 #'
 #' @inheritSection effectsize_CIs Confidence (Compatibility) Intervals (CIs)
 #' @inheritSection effectsize_CIs CIs and Significance Tests
 #'
 #' @return A data frame with the effect size (`Cramers_v`, `phi` (possibly with
-#'   the suffix `_adjusted`), `Cohens_w`, `normalized_chi`, `Odds_ratio`,
-#'   `Risk_ratio` (possibly with the prefix `log_`), `Cohens_h`, or `Cohens_g`)
-#'   and its CIs (`CI_low` and `CI_high`).
+#'   the suffix `_adjusted`), `Cohens_w`, `Fei`, `Odds_ratio`, `Risk_ratio`
+#'   (possibly with the prefix `log_`), `Cohens_h`, or `Cohens_g`) and its CIs
+#'   (`CI_low` and `CI_high`).
 #'
 #' @seealso [chisq_to_phi()] for details regarding estimation and CIs.
 #' @family effect size indices
@@ -141,14 +140,14 @@
 #'
 #' Smoking_ASD <- as.table(c(ASD = 17, ASP = 11, TD = 640))
 #'
-#' normalized_chi(Smoking_ASD)
+#' fei(Smoking_ASD)
 #'
 #' cohens_w(Smoking_ASD)
 #'
 #' pearsons_c(Smoking_ASD)
 #'
 #' # Use custom expected values:
-#' normalized_chi(Smoking_ASD, p = c(0.015, 0.010, 0.975))
+#' fei(Smoking_ASD, p = c(0.015, 0.010, 0.975))
 #'
 #' cohens_w(Smoking_ASD, p = c(0.015, 0.010, 0.975))
 #'
@@ -268,11 +267,11 @@ cramers_v <- function(x, y = NULL, ci = 0.95, alternative = "greater", adjust =
 
 #' @rdname phi
 #' @export
-normalized_chi <- function(x, y = NULL, ci = 0.95, alternative = "greater", ...) {
+fei <- function(x, y = NULL, ci = 0.95, alternative = "greater", ...) {
   alternative <- match.arg(alternative, c("greater", "two.sided", "less"))
 
   if (inherits(x, "BFBayesFactor")) {
-    stop("Normalized Chi is only applicable to goodness of fit tests.", call. = FALSE)
+    stop("Fei is only applicable to goodness of fit tests.", call. = FALSE)
   }
 
 
@@ -286,7 +285,7 @@ normalized_chi <- function(x, y = NULL, ci = 0.95, alternative = "greater", ...)
     x$data.name <- NULL
   }
 
-  effectsize(x, type = "normalized_chi", ci = ci, alternative = alternative)
+  effectsize(x, type = "fei", ci = ci, alternative = alternative)
 }
 
 #' @rdname phi
diff --git a/R/zzz_deprecated.R b/R/zzz_deprecated.R
index 5624fe0eb..f550fbeee 100644
--- a/R/zzz_deprecated.R
+++ b/R/zzz_deprecated.R
@@ -7,6 +7,8 @@
 #' - `interpret_g` is now [`interpret_hedges_g`].
 #' - `interpret_delta` is now [`interpret_glass_delta`].
 #' - `interpret_parameters` for *standardized parameters* was incorrect. Use [`interpret_r`] instead.
+#' - `normalized_chi` is now [`fei`].
+#' - `chisq_to_normalized` is now [`chisq_to_fei`].
 #'
 #' @rdname effectsize_deprecated
 #' @name effectsize_deprecated
@@ -40,3 +42,19 @@ interpret_parameters <- function(...) {
   .Deprecated("interpret_r")
   interpret_r(...)
 }
+
+#' @rdname effectsize_deprecated
+#' @export
+normalized_chi <- function(...) {
+  .Deprecated("fei")
+  fei(...)
+}
+
+#' @rdname effectsize_deprecated
+#' @export
+chisq_to_normalized <- function(...) {
+  .Deprecated("chisq_to_fei")
+  chisq_to_fei(...)
+}
+
+
diff --git a/man/chisq_to_phi.Rd b/man/chisq_to_phi.Rd
index 6ec6e15b9..29b02f958 100644
--- a/man/chisq_to_phi.Rd
+++ b/man/chisq_to_phi.Rd
@@ -4,7 +4,7 @@
 \alias{chisq_to_phi}
 \alias{chisq_to_cohens_w}
 \alias{chisq_to_cramers_v}
-\alias{chisq_to_normalized}
+\alias{chisq_to_fei}
 \alias{chisq_to_pearsons_c}
 \alias{phi_to_chisq}
 \title{Conversion Chi-Squared to Phi or Cramer's V}
@@ -41,16 +41,7 @@ chisq_to_cramers_v(
   ...
 )
 
-chisq_to_normalized(
-  chisq,
-  n,
-  nrow,
-  ncol,
-  p,
-  ci = 0.95,
-  alternative = "greater",
-  ...
-)
+chisq_to_fei(chisq, n, nrow, ncol, p, ci = 0.95, alternative = "greater", ...)
 
 chisq_to_pearsons_c(
   chisq,
@@ -93,19 +84,19 @@ A data frame with the effect size(s), and confidence interval(s). See
 }
 \description{
 Convert between Chi square (\eqn{\chi^2}), Cramer's V, phi (\eqn{\phi}),
-Cohen's \emph{w}, normalized Chi (\eqn{\chi}) and Pearson's \emph{C} for contingency
-tables or goodness of fit.
+Cohen's \emph{w}, Fei (\eqn{פ}) and Pearson's \emph{C} for contingency tables or
+goodness of fit.
 }
 \details{
 These functions use the following formulae:
 \cr
-\deqn{\phi = \sqrt{\chi^2 / n}}{phi = sqrt(\chi^2 / n)}
+\deqn{\phi = w = \sqrt{\chi^2 / n}}{phi = w = sqrt(\chi^2 / n)}
 \cr
-\deqn{Cramer's V = \phi / \sqrt{min(nrow,ncol)-1}}{Cramer's V = \phi / sqrt(min(nrow,ncol)-1)}
+\deqn{\text{Cramer's }V = \phi / \sqrt{min(nrow,ncol)-1}}{Cramer's V = \phi / sqrt(min(nrow,ncol)-1)}
 \cr
-\deqn{Pearson's C = \sqrt{\chi^2 / (\chi^2 + n)}}{Pearson's C = sqrt(\chi^2 / (\chi^2 + n))}
+\deqn{\text{Pearson's }C = \sqrt{\chi^2 / (\chi^2 + n)}}{Pearson's C = sqrt(\chi^2 / (\chi^2 + n))}
 \cr
-\deqn{\chi_{Normalized} = w \times \sqrt{\frac{q}{1-q}}}{Chi (Normalized) = w * sqrt(q/(1-q))}
+\deqn{פ = w \times \sqrt{\frac{q}{1-q}}}{Fei = w * sqrt(q/(1-q))}
 Where \code{q} is the smallest of the expected probabilities.
 \cr\cr
 For adjusted versions of \emph{phi} and \emph{V}, see Bergsma, 2013.
@@ -177,7 +168,7 @@ Smoking_ASD <- as.table(c(ASD = 17, ASP = 11, TD = 640))
 #> data:  Smoking_ASD
 #> X-squared = 7.8521, df = 2, p-value = 0.01972
 
-chisq_to_normalized(
+chisq_to_fei(
   7.8521,
   n = sum(Smoking_ASD),
   nrow = 1,
diff --git a/man/effectsize.Rd b/man/effectsize.Rd
index f1ded3589..10b3f7dd7 100644
--- a/man/effectsize.Rd
+++ b/man/effectsize.Rd
@@ -47,7 +47,7 @@ input model. See details.
 \itemize{
 \item A \strong{t-test} depending on \code{type}: \code{"cohens_d"} (default), \code{"hedges_g"}, or \code{"cles"}.
 \item A \strong{Chi-squared tests of independence}, depending on \code{type}: \code{"cramers_v"} (default), \code{"phi"}, \code{"cohens_w"}, \code{"pearsons_c"}, \code{"cohens_h"}, \code{"oddsratio"}, or \code{"riskratio"}.
-\item A \strong{Chi-squared tests of goodness-of-fit}, depending on \code{type}: \code{"normalized_chi"} (default) \code{"cohens_w"}, \code{"pearsons_c"}
+\item A \strong{Chi-squared tests of goodness-of-fit}, depending on \code{type}: \code{"fei"} (default) \code{"cohens_w"}, \code{"pearsons_c"}
 \item A \strong{One-way ANOVA test}, depending on \code{type}: \code{"eta"} (default), \code{"omega"} or \code{"epsilon"} -squared, \code{"f"}, or \code{"f2"}.
 \item A \strong{McNemar test} returns \emph{Cohen's g}.
 \item A \strong{Wilcoxon test} depending on \code{type}: returns "\code{rank_biserial}" correlation (default) or \code{"cles"}.
diff --git a/man/effectsize_deprecated.Rd b/man/effectsize_deprecated.Rd
index 823654fde..89dec7152 100644
--- a/man/effectsize_deprecated.Rd
+++ b/man/effectsize_deprecated.Rd
@@ -6,6 +6,8 @@
 \alias{interpret_g}
 \alias{interpret_delta}
 \alias{interpret_parameters}
+\alias{normalized_chi}
+\alias{chisq_to_normalized}
 \title{Deprecated functions}
 \usage{
 interpret_d(...)
@@ -15,6 +17,10 @@ interpret_g(...)
 interpret_delta(...)
 
 interpret_parameters(...)
+
+normalized_chi(...)
+
+chisq_to_normalized(...)
 }
 \arguments{
 \item{...}{Arguments to the deprecated function.}
@@ -28,5 +34,7 @@ Deprecated functions
 \item \code{interpret_g} is now \code{\link{interpret_hedges_g}}.
 \item \code{interpret_delta} is now \code{\link{interpret_glass_delta}}.
 \item \code{interpret_parameters} for \emph{standardized parameters} was incorrect. Use \code{\link{interpret_r}} instead.
+\item \code{normalized_chi} is now \code{\link{fei}}.
+\item \code{chisq_to_normalized} is now \code{\link{chisq_to_fei}}.
 }
 }
diff --git a/man/phi.Rd b/man/phi.Rd
index 69e3e1992..b56b929cf 100644
--- a/man/phi.Rd
+++ b/man/phi.Rd
@@ -4,7 +4,7 @@
 \alias{phi}
 \alias{cohens_w}
 \alias{cramers_v}
-\alias{normalized_chi}
+\alias{fei}
 \alias{pearsons_c}
 \alias{oddsratio}
 \alias{riskratio}
@@ -18,7 +18,7 @@ cohens_w(x, y = NULL, ci = 0.95, alternative = "greater", ...)
 
 cramers_v(x, y = NULL, ci = 0.95, alternative = "greater", adjust = FALSE, ...)
 
-normalized_chi(x, y = NULL, ci = 0.95, alternative = "greater", ...)
+fei(x, y = NULL, ci = 0.95, alternative = "greater", ...)
 
 pearsons_c(
   x,
@@ -62,15 +62,14 @@ goodness-of-fit. Ignored for \code{cohens_g()}.}
 }
 \value{
 A data frame with the effect size (\code{Cramers_v}, \code{phi} (possibly with
-the suffix \verb{_adjusted}), \code{Cohens_w}, \code{normalized_chi}, \code{Odds_ratio},
-\code{Risk_ratio} (possibly with the prefix \code{log_}), \code{Cohens_h}, or \code{Cohens_g})
-and its CIs (\code{CI_low} and \code{CI_high}).
+the suffix \verb{_adjusted}), \code{Cohens_w}, \code{Fei}, \code{Odds_ratio}, \code{Risk_ratio}
+(possibly with the prefix \code{log_}), \code{Cohens_h}, or \code{Cohens_g}) and its CIs
+(\code{CI_low} and \code{CI_high}).
 }
 \description{
-Compute Cramer's \emph{V}, phi (\eqn{\phi}), Cohen's \emph{w}, normalized Chi
-(\eqn{\chi}), Pearson's contingency coefficient, Odds ratios, Risk ratios,
-Cohen's \emph{h} and Cohen's \emph{g} for contingency tables or goodness-of-fit. See
-details.
+Compute Cramer's \emph{V}, phi (\eqn{\phi}), Cohen's \emph{w}, Fei (\eqn{פ}), Pearson's
+contingency coefficient, Odds ratios, Risk ratios, Cohen's \emph{h} and Cohen's
+\emph{g} for contingency tables or goodness-of-fit. See details.
 }
 \details{
 \subsection{Correlation-like Effect Sizes}{
@@ -84,17 +83,17 @@ Pearson's \emph{C} should be used, as they are bounded between 0-1. Cohen's \emp
 can also be used, but since it is not bounded at 1 (can be larger) its
 interpretation is more difficult.
 \cr \cr
-For goodness-of-fit in 1D tables Cohen's \emph{W}, normalized Chi (\eqn{\chi}) or
-Pearson's \emph{C} can be used. Cohen's \emph{w} has no upper bound (can be arbitrarily
-large, depending on the expected distribution). Normalized Chi is an adjusted
-Cohen's \emph{w}, accounting for the expected distribution, making it bounded
-between 0-1. Pearson's \emph{C} is also bounded between 0-1.
+For goodness-of-fit in 1D tables Cohen's \emph{W}, Fei (\eqn{פ}) or Pearson's \emph{C}
+can be used. Cohen's \emph{w} has no upper bound (can be arbitrarily large,
+depending on the expected distribution). Fei is an adjusted Cohen's \emph{w},
+accounting for the expected distribution, making it bounded between 0-1.
+Pearson's \emph{C} is also bounded between 0-1.
 \cr \cr
 To summarize, for correlation-like effect sizes, we recommend:
 \itemize{
 \item For a 2x2 table, use \code{phi()}
 \item For larger tables, use \code{cramers_v()}
-\item For goodness-of-fit, use \code{normalized_chi()}
+\item For goodness-of-fit, use \code{fei()}
 }
 }
 
@@ -127,7 +126,7 @@ Szumilas, 2010).
 \cr \cr
 See \emph{Confidence (Compatibility) Intervals (CIs)}, \emph{CIs and Significance Tests},
 and \emph{One-Sided CIs} sections for \emph{phi}, Cohen's \emph{w}, Cramer's \emph{V},
-Pearson's \emph{C}, and normalized Chi.
+Pearson's \emph{C}, and \emph{Fei}.
 }
 
 \section{Confidence (Compatibility) Intervals (CIs)}{
@@ -228,14 +227,14 @@ pearsons_c(M)
 
 Smoking_ASD <- as.table(c(ASD = 17, ASP = 11, TD = 640))
 
-normalized_chi(Smoking_ASD)
+fei(Smoking_ASD)
 
 cohens_w(Smoking_ASD)
 
 pearsons_c(Smoking_ASD)
 
 # Use custom expected values:
-normalized_chi(Smoking_ASD, p = c(0.015, 0.010, 0.975))
+fei(Smoking_ASD, p = c(0.015, 0.010, 0.975))
 
 cohens_w(Smoking_ASD, p = c(0.015, 0.010, 0.975))
 
diff --git a/tests/testthat/test-effectsize.R b/tests/testthat/test-effectsize.R
index f27f7aea3..dafcd6c48 100644
--- a/tests/testthat/test-effectsize.R
+++ b/tests/testthat/test-effectsize.R
@@ -88,8 +88,8 @@ if (require("testthat") && require("effectsize")) {
     expect_error(effectsize(x, type = "v"), "goodness")
     expect_error(effectsize(x, type = "phi"), "appropriate")
     expect_equal(effectsize(x), effectsize(x, type = "chi"))
-    expect_equal(effectsize(x, type = "chi"), nchi <- normalized_chi(observed.dfc, p = expected.dfc))
-    expect_equal(normalized_chi(x), nchi)
+    expect_equal(effectsize(x, type = "fei"), Fei <- fei(observed.dfc, p = expected.dfc))
+    expect_equal(fei(x), Fei)
   })
 
   test_that("cor.test / other", {
diff --git a/tests/testthat/test-xtab.R b/tests/testthat/test-xtab.R
index a86ed3bb1..26581744a 100644
--- a/tests/testthat/test-xtab.R
+++ b/tests/testthat/test-xtab.R
@@ -84,14 +84,14 @@ if (require("testthat") && require("effectsize")) {
     w1 <- cohens_w(table(mtcars$cyl), p = c(0.34375, 0.21875, 0.43750))
     w2 <- cohens_w(table(mtcars$cyl), p = c(0.8, 0.1, 0.1))
 
-    nchi1 <- normalized_chi(table(mtcars$cyl), p = c(0.34375, 0.21875, 0.43750))
-    nchi2 <- normalized_chi(table(mtcars$cyl), p = c(0.8, 0.1, 0.1))
+    Fei1 <- fei(table(mtcars$cyl), p = c(0.34375, 0.21875, 0.43750))
+    Fei2 <- fei(table(mtcars$cyl), p = c(0.8, 0.1, 0.1))
 
     expect_equal(w1[[1]], 0)
     expect_true(w1[[1]] < w2[[1]])
-    expect_true(nchi1[[1]] < nchi2[[1]])
-    expect_true(nchi2[[1]] < w2[[1]])
-    expect_equal(w2[[1]] * sqrt(0.1 / 0.9), nchi2[[1]])
+    expect_true(Fei1[[1]] < Fei2[[1]])
+    expect_true(Fei2[[1]] < w2[[1]])
+    expect_equal(w2[[1]] * sqrt(0.1 / 0.9), Fei2[[1]])
     expect_true(w1$CI_low < w2$CI_low)
     expect_true(w2$CI_low < w2$CI_high)
     expect_equal(w2$CI_high, Inf)
@@ -116,8 +116,8 @@ if (require("testthat") && require("effectsize")) {
       cohens_w(O, p = E_tab, rescale.p = TRUE)
     )
     expect_equal(
-      normalized_chi(O, p = E_vec, rescale.p = TRUE),
-      normalized_chi(O, p = E_tab, rescale.p = TRUE)
+      fei(O, p = E_vec, rescale.p = TRUE),
+      fei(O, p = E_tab, rescale.p = TRUE)
     )
     expect_equal(
       pearsons_c(O, p = E_vec, rescale.p = TRUE),
diff --git a/vignettes/simple_htests.Rmd b/vignettes/simple_htests.Rmd
index 9966ef8bd..9d4ca51ac 100644
--- a/vignettes/simple_htests.Rmd
+++ b/vignettes/simple_htests.Rmd
@@ -252,10 +252,10 @@ cohens_w(O, p = E)
 
 However, Cohen's *w* does not account for the distribution of expected probabilities, 
 and as such may be seen is an inflated effect size, and since it can be larger than 1 it is also harder to interpret. 
-For these reasons, we recommend the Normalized $\chi$ (Chi), which adjusted Cohen's *w* to account for the expected distribution of probabilities, making it range between 0 (observed distribution matches the expected distribution perfectly) and 1 (the observed distribution is maximally different than the expected one).
+For these reasons, we recommend the $פ$ (Fei) coefficient, which adjusted Cohen's *w* to account for the expected distribution of probabilities, making it range between 0 (observed distribution matches the expected distribution perfectly) and 1 (the observed distribution is maximally different than the expected one).
 
 ```{r}
-normalized_chi(O, p = E)
+fei(O, p = E)
 ```
 
 ### Paired Contingency Tables

From 996536d5a7f66816b6d87bdc5e0071e2a7f1ec40 Mon Sep 17 00:00:00 2001
From: "Mattan S. Ben-Shachar" <mattansb@msbstats.info>
Date: Sat, 10 Sep 2022 19:17:57 +0300
Subject: [PATCH 02/14] fix test

---
 tests/testthat/test-effectsize.R | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tests/testthat/test-effectsize.R b/tests/testthat/test-effectsize.R
index dafcd6c48..04e2c478f 100644
--- a/tests/testthat/test-effectsize.R
+++ b/tests/testthat/test-effectsize.R
@@ -87,7 +87,7 @@ if (require("testthat") && require("effectsize")) {
     x <- chisq.test(x = observed.dfc, p = expected.dfc)
     expect_error(effectsize(x, type = "v"), "goodness")
     expect_error(effectsize(x, type = "phi"), "appropriate")
-    expect_equal(effectsize(x), effectsize(x, type = "chi"))
+    expect_equal(effectsize(x), effectsize(x, type = "fei"))
     expect_equal(effectsize(x, type = "fei"), Fei <- fei(observed.dfc, p = expected.dfc))
     expect_equal(fei(x), Fei)
   })

From 2ab8a809e6518f788c86b0873afccefa62736be1 Mon Sep 17 00:00:00 2001
From: "Mattan S. Ben-Shachar" <mattansb@msbstats.info>
Date: Sat, 10 Sep 2022 19:29:31 +0300
Subject: [PATCH 03/14] clean docs

---
 R/convert_stat_chisq.R | 6 +++---
 man/chisq_to_phi.Rd    | 6 +++---
 2 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/R/convert_stat_chisq.R b/R/convert_stat_chisq.R
index 9a9a07a68..ac2e0df09 100644
--- a/R/convert_stat_chisq.R
+++ b/R/convert_stat_chisq.R
@@ -25,10 +25,10 @@
 #' \cr
 #' \deqn{\text{Cramer's }V = \phi / \sqrt{min(nrow,ncol)-1}}{Cramer's V = \phi / sqrt(min(nrow,ncol)-1)}
 #' \cr
-#' \deqn{\text{Pearson's }C = \sqrt{\chi^2 / (\chi^2 + n)}}{Pearson's C = sqrt(\chi^2 / (\chi^2 + n))}
+#' \deqn{פ = w / \sqrt{\frac{1}{min(p_E)}-1}}{Fei = w / sqrt(1/min(p_E) - 1))}
+#' Where `p_E` are the expected probabilities.
 #' \cr
-#' \deqn{פ = w \times \sqrt{\frac{q}{1-q}}}{Fei = w * sqrt(q/(1-q))}
-#' Where `q` is the smallest of the expected probabilities.
+#' \deqn{\text{Pearson's }C = \sqrt{\chi^2 / (\chi^2 + n)}}{Pearson's C = sqrt(\chi^2 / (\chi^2 + n))}
 #' \cr\cr
 #' For adjusted versions of *phi* and *V*, see Bergsma, 2013.
 #'
diff --git a/man/chisq_to_phi.Rd b/man/chisq_to_phi.Rd
index 29b02f958..eabefd0b4 100644
--- a/man/chisq_to_phi.Rd
+++ b/man/chisq_to_phi.Rd
@@ -94,10 +94,10 @@ These functions use the following formulae:
 \cr
 \deqn{\text{Cramer's }V = \phi / \sqrt{min(nrow,ncol)-1}}{Cramer's V = \phi / sqrt(min(nrow,ncol)-1)}
 \cr
-\deqn{\text{Pearson's }C = \sqrt{\chi^2 / (\chi^2 + n)}}{Pearson's C = sqrt(\chi^2 / (\chi^2 + n))}
+\deqn{פ = w / \sqrt{\frac{1}{min(p_E)}-1}}{Fei = w / sqrt(1/min(p_E) - 1))}
+Where \code{p_E} are the expected probabilities.
 \cr
-\deqn{פ = w \times \sqrt{\frac{q}{1-q}}}{Fei = w * sqrt(q/(1-q))}
-Where \code{q} is the smallest of the expected probabilities.
+\deqn{\text{Pearson's }C = \sqrt{\chi^2 / (\chi^2 + n)}}{Pearson's C = sqrt(\chi^2 / (\chi^2 + n))}
 \cr\cr
 For adjusted versions of \emph{phi} and \emph{V}, see Bergsma, 2013.
 }

From df64ae984828ed6cd5d0a59a2f6fe9d85b286c5d Mon Sep 17 00:00:00 2001
From: "Mattan S. Ben-Shachar" <mattansb@msbstats.info>
Date: Sat, 10 Sep 2022 19:40:07 +0300
Subject: [PATCH 04/14] clean up some more

---
 R/convert_stat_chisq.R | 2 +-
 man/chisq_to_phi.Rd    | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/R/convert_stat_chisq.R b/R/convert_stat_chisq.R
index ac2e0df09..cce0d5d57 100644
--- a/R/convert_stat_chisq.R
+++ b/R/convert_stat_chisq.R
@@ -1,6 +1,6 @@
 #' Conversion Chi-Squared to Phi or Cramer's V
 #'
-#' Convert between Chi square (\eqn{\chi^2}), Cramer's V, phi (\eqn{\phi}),
+#' Convert between Chi square (\eqn{\chi^2}), phi (\eqn{\phi}), Cramer's V,
 #' Cohen's *w*, Fei (\eqn{פ}) and Pearson's *C* for contingency tables or
 #' goodness of fit.
 #'
diff --git a/man/chisq_to_phi.Rd b/man/chisq_to_phi.Rd
index eabefd0b4..1bc017d47 100644
--- a/man/chisq_to_phi.Rd
+++ b/man/chisq_to_phi.Rd
@@ -83,7 +83,7 @@ A data frame with the effect size(s), and confidence interval(s). See
 \code{\link[=cramers_v]{cramers_v()}}.
 }
 \description{
-Convert between Chi square (\eqn{\chi^2}), Cramer's V, phi (\eqn{\phi}),
+Convert between Chi square (\eqn{\chi^2}), phi (\eqn{\phi}), Cramer's V,
 Cohen's \emph{w}, Fei (\eqn{פ}) and Pearson's \emph{C} for contingency tables or
 goodness of fit.
 }

From 683843869fdd753c418bca66f0757d5964918d99 Mon Sep 17 00:00:00 2001
From: "Mattan S. Ben-Shachar" <mattansb@msbstats.info>
Date: Sat, 10 Sep 2022 19:41:12 +0300
Subject: [PATCH 05/14] clean up xtab docs

---
 R/xtab.R   | 42 +++++++++++++++++++++++-------------------
 man/phi.Rd | 30 +++++++++++++++++++++++-------
 2 files changed, 46 insertions(+), 26 deletions(-)

diff --git a/R/xtab.R b/R/xtab.R
index 33cba6292..61e81e8c3 100644
--- a/R/xtab.R
+++ b/R/xtab.R
@@ -13,8 +13,8 @@
 #'   Partial matching is allowed (e.g., `"g"`, `"l"`, `"two"`...). See
 #'   *One-Sided CIs* in [effectsize_CIs].
 #' @param adjust Should the effect size be bias-corrected? Defaults to `FALSE`.
-#' @param ... Arguments passed to [stats::chisq.test()], such as `p` for
-#'   goodness-of-fit. Ignored for `cohens_g()`.
+#' @param ... For goodness-of-fit effect sizes, can pass `rescale.p` (see
+#'   [stats::chisq.test()]). Else, ignored.
 #'
 #' @details
 #'
@@ -204,7 +204,7 @@ phi <- function(x, y = NULL, ci = 0.95, alternative = "greater", adjust = FALSE,
       stop("'x' is not a Chi-squared test!", call. = FALSE)
     }
   } else {
-    x <- suppressWarnings(stats::chisq.test(x, y, ...))
+    x <- suppressWarnings(stats::chisq.test(x, y))
     x$data.name <- NULL
   }
 
@@ -212,42 +212,44 @@ phi <- function(x, y = NULL, ci = 0.95, alternative = "greater", adjust = FALSE,
 }
 
 #' @rdname phi
+#' @importFrom stats chisq.test
 #' @export
-cohens_w <- function(x, y = NULL, ci = 0.95, alternative = "greater", ...) {
+cramers_v <- function(x, y = NULL, ci = 0.95, alternative = "greater", adjust = FALSE, ...) {
   alternative <- match.arg(alternative, c("greater", "two.sided", "less"))
 
   if (inherits(x, "BFBayesFactor")) {
     if (!inherits(x@numerator[[1]], "BFcontingencyTable")) {
       stop("'x' is not a Chi-squared test!", call. = FALSE)
     }
-    return(effectsize(x, type = "phi", ci = ci, ...))
+    return(effectsize(x, type = "cramers_v", adjust = adjust, ci = ci, ...))
   }
 
 
   if (inherits(x, "htest")) {
     if (!(grepl("Pearson's Chi-squared", x$method) ||
-      grepl("Chi-squared test for given probabilities", x$method))) {
+          grepl("Chi-squared test for given probabilities", x$method))) {
       stop("'x' is not a Chi-squared test!", call. = FALSE)
     }
   } else {
-    x <- suppressWarnings(stats::chisq.test(x, y, ...))
+    x <- suppressWarnings(stats::chisq.test(x, y))
     x$data.name <- NULL
   }
 
-  effectsize(x, type = "cohens_w", ci = ci, alternative = alternative)
+  effectsize(x, type = "cramers_v", adjust = adjust, ci = ci, alternative = alternative)
 }
 
 #' @rdname phi
 #' @importFrom stats chisq.test
 #' @export
-cramers_v <- function(x, y = NULL, ci = 0.95, alternative = "greater", adjust = FALSE, ...) {
+cohens_w <- function(x, y = NULL, p = rep(1/length(x), length(x)),
+                     ci = 0.95, alternative = "greater", ...) {
   alternative <- match.arg(alternative, c("greater", "two.sided", "less"))
 
   if (inherits(x, "BFBayesFactor")) {
     if (!inherits(x@numerator[[1]], "BFcontingencyTable")) {
       stop("'x' is not a Chi-squared test!", call. = FALSE)
     }
-    return(effectsize(x, type = "cramers_v", adjust = adjust, ci = ci, ...))
+    return(effectsize(x, type = "phi", ci = ci, ...))
   }
 
 
@@ -257,17 +259,18 @@ cramers_v <- function(x, y = NULL, ci = 0.95, alternative = "greater", adjust =
       stop("'x' is not a Chi-squared test!", call. = FALSE)
     }
   } else {
-    x <- suppressWarnings(stats::chisq.test(x, y, ...))
+    x <- suppressWarnings(stats::chisq.test(x, y, p = p, ...))
     x$data.name <- NULL
   }
 
-  effectsize(x, type = "cramers_v", adjust = adjust, ci = ci, alternative = alternative)
+  effectsize(x, type = "cohens_w", ci = ci, alternative = alternative)
 }
 
 
 #' @rdname phi
+#' @importFrom stats chisq.test
 #' @export
-fei <- function(x, y = NULL, ci = 0.95, alternative = "greater", ...) {
+fei <- function(x, p = rep(1/length(x), length(x)), ci = 0.95, alternative = "greater", ...) {
   alternative <- match.arg(alternative, c("greater", "two.sided", "less"))
 
   if (inherits(x, "BFBayesFactor")) {
@@ -281,7 +284,7 @@ fei <- function(x, y = NULL, ci = 0.95, alternative = "greater", ...) {
       stop("'x' is not a Chi-squared test!", call. = FALSE)
     }
   } else {
-    x <- suppressWarnings(stats::chisq.test(x, y, ...))
+    x <- suppressWarnings(stats::chisq.test(x, y = NULL, p = p, ...))
     x$data.name <- NULL
   }
 
@@ -291,7 +294,8 @@ fei <- function(x, y = NULL, ci = 0.95, alternative = "greater", ...) {
 #' @rdname phi
 #' @importFrom stats chisq.test
 #' @export
-pearsons_c <- function(x, y = NULL, ci = 0.95, alternative = "greater", adjust = FALSE, ...) {
+pearsons_c <- function(x, y = NULL, p = rep(1/length(x), length(x)),
+                     ci = 0.95, alternative = "greater", ...) {
   alternative <- match.arg(alternative, c("greater", "two.sided", "less"))
 
   if (inherits(x, "BFBayesFactor")) {
@@ -308,7 +312,7 @@ pearsons_c <- function(x, y = NULL, ci = 0.95, alternative = "greater", adjust =
       stop("'x' is not a Chi-squared test!", call. = FALSE)
     }
   } else {
-    x <- suppressWarnings(stats::chisq.test(x, y, ...))
+    x <- suppressWarnings(stats::chisq.test(x, y, p = p, ...))
     x$data.name <- NULL
   }
 
@@ -339,7 +343,7 @@ oddsratio <- function(x, y = NULL, ci = 0.95, alternative = "two.sided", log = F
     return(effectsize(x, type = "or", log = log, ci = ci, ...))
   }
 
-  res <- suppressWarnings(stats::chisq.test(x, y, ...))
+  res <- suppressWarnings(stats::chisq.test(x, y))
   Obs <- res$observed
 
   if (any(c(colSums(Obs), rowSums(Obs)) == 0L)) {
@@ -415,7 +419,7 @@ riskratio <- function(x, y = NULL, ci = 0.95, alternative = "two.sided", log = F
     return(effectsize(x, type = "rr", log = log, ci = ci, ...))
   }
 
-  res <- suppressWarnings(stats::chisq.test(x, y, ...))
+  res <- suppressWarnings(stats::chisq.test(x, y))
   Obs <- res$observed
 
   if (any(c(colSums(Obs), rowSums(Obs)) == 0L)) {
@@ -493,7 +497,7 @@ cohens_h <- function(x, y = NULL, ci = 0.95, alternative = "two.sided", ...) {
     return(effectsize(x, type = "cohens_h", ci = ci, ...))
   }
 
-  res <- suppressWarnings(stats::chisq.test(x, y, ...))
+  res <- suppressWarnings(stats::chisq.test(x, y))
   Obs <- res$observed
 
   if (any(c(colSums(Obs), rowSums(Obs)) == 0L)) {
diff --git a/man/phi.Rd b/man/phi.Rd
index b56b929cf..7c497911a 100644
--- a/man/phi.Rd
+++ b/man/phi.Rd
@@ -2,8 +2,8 @@
 % Please edit documentation in R/xtab.R
 \name{phi}
 \alias{phi}
-\alias{cohens_w}
 \alias{cramers_v}
+\alias{cohens_w}
 \alias{fei}
 \alias{pearsons_c}
 \alias{oddsratio}
@@ -14,18 +14,31 @@
 \usage{
 phi(x, y = NULL, ci = 0.95, alternative = "greater", adjust = FALSE, ...)
 
-cohens_w(x, y = NULL, ci = 0.95, alternative = "greater", ...)
-
 cramers_v(x, y = NULL, ci = 0.95, alternative = "greater", adjust = FALSE, ...)
 
-fei(x, y = NULL, ci = 0.95, alternative = "greater", ...)
+cohens_w(
+  x,
+  y = NULL,
+  p = rep(1/length(x), length(x)),
+  ci = 0.95,
+  alternative = "greater",
+  ...
+)
+
+fei(
+  x,
+  p = rep(1/length(x), length(x)),
+  ci = 0.95,
+  alternative = "greater",
+  ...
+)
 
 pearsons_c(
   x,
   y = NULL,
+  p = rep(1/length(x), length(x)),
   ci = 0.95,
   alternative = "greater",
-  adjust = FALSE,
   ...
 )
 
@@ -55,8 +68,11 @@ Partial matching is allowed (e.g., \code{"g"}, \code{"l"}, \code{"two"}...). See
 
 \item{adjust}{Should the effect size be bias-corrected? Defaults to \code{FALSE}.}
 
-\item{...}{Arguments passed to \code{\link[stats:chisq.test]{stats::chisq.test()}}, such as \code{p} for
-goodness-of-fit. Ignored for \code{cohens_g()}.}
+\item{...}{For goodness-of-fit effect sizes, can pass \code{rescale.p} (see
+\code{\link[stats:chisq.test]{stats::chisq.test()}}). Else, ignored.}
+
+\item{p}{a vector of probabilities of the same length as \code{x}.
+    An error is given if any entry of \code{p} is negative.}
 
 \item{log}{Take in or output the log of the ratio (such as in logistic models).}
 }

From 8fef7cfcdf706d9ca1bdfc203460cc2d1b134f06 Mon Sep 17 00:00:00 2001
From: "Mattan S. Ben-Shachar" <mattansb@msbstats.info>
Date: Sat, 10 Sep 2022 19:45:54 +0300
Subject: [PATCH 06/14] remove redundant adjust argument from pearson_c

---
 R/xtab.R | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/R/xtab.R b/R/xtab.R
index 61e81e8c3..f8aac48b1 100644
--- a/R/xtab.R
+++ b/R/xtab.R
@@ -302,7 +302,7 @@ pearsons_c <- function(x, y = NULL, p = rep(1/length(x), length(x)),
     if (!inherits(x@numerator[[1]], "BFcontingencyTable")) {
       stop("'x' is not a Chi-squared test!", call. = FALSE)
     }
-    return(effectsize(x, type = "pearsons_c", adjust = adjust, ci = ci, ...))
+    return(effectsize(x, type = "pearsons_c", ci = ci, ...))
   }
 
 
@@ -316,7 +316,7 @@ pearsons_c <- function(x, y = NULL, p = rep(1/length(x), length(x)),
     x$data.name <- NULL
   }
 
-  effectsize(x, type = "pearsons_c", adjust = adjust, ci = ci, alternative = alternative)
+  effectsize(x, type = "pearsons_c", ci = ci, alternative = alternative)
 }
 
 

From ff122726ab76e7562792178e81a74f6b7de64697 Mon Sep 17 00:00:00 2001
From: "Mattan S. Ben-Shachar" <mattansb@msbstats.info>
Date: Sat, 10 Sep 2022 20:15:29 +0300
Subject: [PATCH 07/14] Skip this test on older R versions

---
 tests/testthat/test-eta_squared.R | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/tests/testthat/test-eta_squared.R b/tests/testthat/test-eta_squared.R
index c73a0de8e..37bfb5f55 100644
--- a/tests/testthat/test-eta_squared.R
+++ b/tests/testthat/test-eta_squared.R
@@ -647,8 +647,7 @@ if (require("testthat") && require("effectsize")) {
     expect_output(print(out), "Type II")
 
     skip_if_not_installed("car")
-    skip_if(getRversion() <= "3.6" &&
-      Sys.info()["sysname"] == "Darwin")
+    skip_if(getRversion() <= "3.6")
     b_lm <- car::Anova(lm(mpg ~ cyl + am, data = mtcars), type = 2)
     out_lm <- eta_squared(b_lm)
     expect_equal(out[1:2, ], out_lm, ignore_attr = TRUE)

From 64f2c24c7ab66da9000e98ceb27d4948dc6666dc Mon Sep 17 00:00:00 2001
From: "Mattan S. Ben-Shachar" <mattansb@msbstats.info>
Date: Sat, 10 Sep 2022 21:33:47 +0300
Subject: [PATCH 08/14] Use XXX as test

---
 R/convert_stat_chisq.R      | 4 ++--
 R/xtab.R                    | 4 ++--
 man/chisq_to_phi.Rd         | 4 ++--
 man/phi.Rd                  | 4 ++--
 vignettes/simple_htests.Rmd | 2 +-
 5 files changed, 9 insertions(+), 9 deletions(-)

diff --git a/R/convert_stat_chisq.R b/R/convert_stat_chisq.R
index cce0d5d57..5bd0cfab8 100644
--- a/R/convert_stat_chisq.R
+++ b/R/convert_stat_chisq.R
@@ -1,7 +1,7 @@
 #' Conversion Chi-Squared to Phi or Cramer's V
 #'
 #' Convert between Chi square (\eqn{\chi^2}), phi (\eqn{\phi}), Cramer's V,
-#' Cohen's *w*, Fei (\eqn{פ}) and Pearson's *C* for contingency tables or
+#' Cohen's *w*, Fei (\eqn{XXX}) and Pearson's *C* for contingency tables or
 #' goodness of fit.
 #'
 #' @param chisq The Chi-squared statistic.
@@ -25,7 +25,7 @@
 #' \cr
 #' \deqn{\text{Cramer's }V = \phi / \sqrt{min(nrow,ncol)-1}}{Cramer's V = \phi / sqrt(min(nrow,ncol)-1)}
 #' \cr
-#' \deqn{פ = w / \sqrt{\frac{1}{min(p_E)}-1}}{Fei = w / sqrt(1/min(p_E) - 1))}
+#' \deqn{XXX = w / \sqrt{\frac{1}{min(p_E)}-1}}{Fei = w / sqrt(1/min(p_E) - 1))}
 #' Where `p_E` are the expected probabilities.
 #' \cr
 #' \deqn{\text{Pearson's }C = \sqrt{\chi^2 / (\chi^2 + n)}}{Pearson's C = sqrt(\chi^2 / (\chi^2 + n))}
diff --git a/R/xtab.R b/R/xtab.R
index f8aac48b1..6db7ca69f 100644
--- a/R/xtab.R
+++ b/R/xtab.R
@@ -1,6 +1,6 @@
 #' Effect size for contingency tables
 #'
-#' Compute Cramer's *V*, phi (\eqn{\phi}), Cohen's *w*, Fei (\eqn{פ}), Pearson's
+#' Compute Cramer's *V*, phi (\eqn{\phi}), Cohen's *w*, Fei (\eqn{XXX}), Pearson's
 #' contingency coefficient, Odds ratios, Risk ratios, Cohen's *h* and Cohen's
 #' *g* for contingency tables or goodness-of-fit. See details.
 #'
@@ -29,7 +29,7 @@
 #' can also be used, but since it is not bounded at 1 (can be larger) its
 #' interpretation is more difficult.
 #' \cr \cr
-#' For goodness-of-fit in 1D tables Cohen's *W*, Fei (\eqn{פ}) or Pearson's *C*
+#' For goodness-of-fit in 1D tables Cohen's *W*, Fei (\eqn{XXX}) or Pearson's *C*
 #' can be used. Cohen's *w* has no upper bound (can be arbitrarily large,
 #' depending on the expected distribution). Fei is an adjusted Cohen's *w*,
 #' accounting for the expected distribution, making it bounded between 0-1.
diff --git a/man/chisq_to_phi.Rd b/man/chisq_to_phi.Rd
index 1bc017d47..f6f12523b 100644
--- a/man/chisq_to_phi.Rd
+++ b/man/chisq_to_phi.Rd
@@ -84,7 +84,7 @@ A data frame with the effect size(s), and confidence interval(s). See
 }
 \description{
 Convert between Chi square (\eqn{\chi^2}), phi (\eqn{\phi}), Cramer's V,
-Cohen's \emph{w}, Fei (\eqn{פ}) and Pearson's \emph{C} for contingency tables or
+Cohen's \emph{w}, Fei (\eqn{XXX}) and Pearson's \emph{C} for contingency tables or
 goodness of fit.
 }
 \details{
@@ -94,7 +94,7 @@ These functions use the following formulae:
 \cr
 \deqn{\text{Cramer's }V = \phi / \sqrt{min(nrow,ncol)-1}}{Cramer's V = \phi / sqrt(min(nrow,ncol)-1)}
 \cr
-\deqn{פ = w / \sqrt{\frac{1}{min(p_E)}-1}}{Fei = w / sqrt(1/min(p_E) - 1))}
+\deqn{XXX = w / \sqrt{\frac{1}{min(p_E)}-1}}{Fei = w / sqrt(1/min(p_E) - 1))}
 Where \code{p_E} are the expected probabilities.
 \cr
 \deqn{\text{Pearson's }C = \sqrt{\chi^2 / (\chi^2 + n)}}{Pearson's C = sqrt(\chi^2 / (\chi^2 + n))}
diff --git a/man/phi.Rd b/man/phi.Rd
index 7c497911a..fbf47dafa 100644
--- a/man/phi.Rd
+++ b/man/phi.Rd
@@ -83,7 +83,7 @@ the suffix \verb{_adjusted}), \code{Cohens_w}, \code{Fei}, \code{Odds_ratio}, \c
 (\code{CI_low} and \code{CI_high}).
 }
 \description{
-Compute Cramer's \emph{V}, phi (\eqn{\phi}), Cohen's \emph{w}, Fei (\eqn{פ}), Pearson's
+Compute Cramer's \emph{V}, phi (\eqn{\phi}), Cohen's \emph{w}, Fei (\eqn{XXX}), Pearson's
 contingency coefficient, Odds ratios, Risk ratios, Cohen's \emph{h} and Cohen's
 \emph{g} for contingency tables or goodness-of-fit. See details.
 }
@@ -99,7 +99,7 @@ Pearson's \emph{C} should be used, as they are bounded between 0-1. Cohen's \emp
 can also be used, but since it is not bounded at 1 (can be larger) its
 interpretation is more difficult.
 \cr \cr
-For goodness-of-fit in 1D tables Cohen's \emph{W}, Fei (\eqn{פ}) or Pearson's \emph{C}
+For goodness-of-fit in 1D tables Cohen's \emph{W}, Fei (\eqn{XXX}) or Pearson's \emph{C}
 can be used. Cohen's \emph{w} has no upper bound (can be arbitrarily large,
 depending on the expected distribution). Fei is an adjusted Cohen's \emph{w},
 accounting for the expected distribution, making it bounded between 0-1.
diff --git a/vignettes/simple_htests.Rmd b/vignettes/simple_htests.Rmd
index 9d4ca51ac..e79c69cbb 100644
--- a/vignettes/simple_htests.Rmd
+++ b/vignettes/simple_htests.Rmd
@@ -252,7 +252,7 @@ cohens_w(O, p = E)
 
 However, Cohen's *w* does not account for the distribution of expected probabilities, 
 and as such may be seen is an inflated effect size, and since it can be larger than 1 it is also harder to interpret. 
-For these reasons, we recommend the $פ$ (Fei) coefficient, which adjusted Cohen's *w* to account for the expected distribution of probabilities, making it range between 0 (observed distribution matches the expected distribution perfectly) and 1 (the observed distribution is maximally different than the expected one).
+For these reasons, we recommend the $XXX$ (Fei) coefficient, which adjusted Cohen's *w* to account for the expected distribution of probabilities, making it range between 0 (observed distribution matches the expected distribution perfectly) and 1 (the observed distribution is maximally different than the expected one).
 
 ```{r}
 fei(O, p = E)

From 2b684eeec578a656e367109330a94e5fcfa0957b Mon Sep 17 00:00:00 2001
From: "Mattan S. Ben-Shachar" <mattansb@msbstats.info>
Date: Sat, 10 Sep 2022 21:51:10 +0300
Subject: [PATCH 09/14] try 1

---
 vignettes/simple_htests.Rmd | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/vignettes/simple_htests.Rmd b/vignettes/simple_htests.Rmd
index e79c69cbb..9d4ca51ac 100644
--- a/vignettes/simple_htests.Rmd
+++ b/vignettes/simple_htests.Rmd
@@ -252,7 +252,7 @@ cohens_w(O, p = E)
 
 However, Cohen's *w* does not account for the distribution of expected probabilities, 
 and as such may be seen is an inflated effect size, and since it can be larger than 1 it is also harder to interpret. 
-For these reasons, we recommend the $XXX$ (Fei) coefficient, which adjusted Cohen's *w* to account for the expected distribution of probabilities, making it range between 0 (observed distribution matches the expected distribution perfectly) and 1 (the observed distribution is maximally different than the expected one).
+For these reasons, we recommend the $פ$ (Fei) coefficient, which adjusted Cohen's *w* to account for the expected distribution of probabilities, making it range between 0 (observed distribution matches the expected distribution perfectly) and 1 (the observed distribution is maximally different than the expected one).
 
 ```{r}
 fei(O, p = E)

From 63df210da0d756af936e1afe276ec10dd83405b0 Mon Sep 17 00:00:00 2001
From: "Mattan S. Ben-Shachar" <mattansb@msbstats.info>
Date: Sat, 10 Sep 2022 21:59:08 +0300
Subject: [PATCH 10/14] try 2

---
 R/convert_stat_chisq.R | 2 +-
 man/chisq_to_phi.Rd    | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/R/convert_stat_chisq.R b/R/convert_stat_chisq.R
index 5bd0cfab8..fb6bce4e6 100644
--- a/R/convert_stat_chisq.R
+++ b/R/convert_stat_chisq.R
@@ -1,7 +1,7 @@
 #' Conversion Chi-Squared to Phi or Cramer's V
 #'
 #' Convert between Chi square (\eqn{\chi^2}), phi (\eqn{\phi}), Cramer's V,
-#' Cohen's *w*, Fei (\eqn{XXX}) and Pearson's *C* for contingency tables or
+#' Cohen's *w*, Fei (*פ*) and Pearson's *C* for contingency tables or
 #' goodness of fit.
 #'
 #' @param chisq The Chi-squared statistic.
diff --git a/man/chisq_to_phi.Rd b/man/chisq_to_phi.Rd
index f6f12523b..2fe695427 100644
--- a/man/chisq_to_phi.Rd
+++ b/man/chisq_to_phi.Rd
@@ -84,7 +84,7 @@ A data frame with the effect size(s), and confidence interval(s). See
 }
 \description{
 Convert between Chi square (\eqn{\chi^2}), phi (\eqn{\phi}), Cramer's V,
-Cohen's \emph{w}, Fei (\eqn{XXX}) and Pearson's \emph{C} for contingency tables or
+Cohen's \emph{w}, Fei (\emph{פ}) and Pearson's \emph{C} for contingency tables or
 goodness of fit.
 }
 \details{

From 9e9ac9e0fd741ac3b433463b18f0a0ca970d456d Mon Sep 17 00:00:00 2001
From: "Mattan S. Ben-Shachar" <mattansb@msbstats.info>
Date: Sat, 10 Sep 2022 22:16:30 +0300
Subject: [PATCH 11/14] try 3

---
 R/convert_stat_chisq.R | 2 ++
 man/chisq_to_phi.Rd    | 1 +
 2 files changed, 3 insertions(+)

diff --git a/R/convert_stat_chisq.R b/R/convert_stat_chisq.R
index fb6bce4e6..a7ce86469 100644
--- a/R/convert_stat_chisq.R
+++ b/R/convert_stat_chisq.R
@@ -4,6 +4,8 @@
 #' Cohen's *w*, Fei (*פ*) and Pearson's *C* for contingency tables or
 #' goodness of fit.
 #'
+#' @encoding UTF-8
+#'
 #' @param chisq The Chi-squared statistic.
 #' @param n Total sample size.
 #' @param nrow,ncol The number of rows/columns in the contingency table.
diff --git a/man/chisq_to_phi.Rd b/man/chisq_to_phi.Rd
index 2fe695427..cd6486827 100644
--- a/man/chisq_to_phi.Rd
+++ b/man/chisq_to_phi.Rd
@@ -1,5 +1,6 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/convert_stat_chisq.R
+\encoding{UTF-8}
 \name{chisq_to_phi}
 \alias{chisq_to_phi}
 \alias{chisq_to_cohens_w}

From 218bc282960581360f775638cc97f427d2098222 Mon Sep 17 00:00:00 2001
From: "Mattan S. Ben-Shachar" <mattansb@msbstats.info>
Date: Sun, 11 Sep 2022 08:14:17 +0300
Subject: [PATCH 12/14] try 4

---
 R/convert_stat_chisq.R | 5 ++---
 man/chisq_to_phi.Rd    | 5 ++---
 2 files changed, 4 insertions(+), 6 deletions(-)

diff --git a/R/convert_stat_chisq.R b/R/convert_stat_chisq.R
index a7ce86469..2172db823 100644
--- a/R/convert_stat_chisq.R
+++ b/R/convert_stat_chisq.R
@@ -1,10 +1,9 @@
 #' Conversion Chi-Squared to Phi or Cramer's V
 #'
 #' Convert between Chi square (\eqn{\chi^2}), phi (\eqn{\phi}), Cramer's V,
-#' Cohen's *w*, Fei (*פ*) and Pearson's *C* for contingency tables or
+#' Cohen's *w*, Fei (\u05e4) and Pearson's *C* for contingency tables or
 #' goodness of fit.
 #'
-#' @encoding UTF-8
 #'
 #' @param chisq The Chi-squared statistic.
 #' @param n Total sample size.
@@ -27,7 +26,7 @@
 #' \cr
 #' \deqn{\text{Cramer's }V = \phi / \sqrt{min(nrow,ncol)-1}}{Cramer's V = \phi / sqrt(min(nrow,ncol)-1)}
 #' \cr
-#' \deqn{XXX = w / \sqrt{\frac{1}{min(p_E)}-1}}{Fei = w / sqrt(1/min(p_E) - 1))}
+#' \deqn{Fei = w / \sqrt{\frac{1}{min(p_E)}-1}}{Fei = w / sqrt(1/min(p_E) - 1))}
 #' Where `p_E` are the expected probabilities.
 #' \cr
 #' \deqn{\text{Pearson's }C = \sqrt{\chi^2 / (\chi^2 + n)}}{Pearson's C = sqrt(\chi^2 / (\chi^2 + n))}
diff --git a/man/chisq_to_phi.Rd b/man/chisq_to_phi.Rd
index cd6486827..ac5bfc666 100644
--- a/man/chisq_to_phi.Rd
+++ b/man/chisq_to_phi.Rd
@@ -1,6 +1,5 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/convert_stat_chisq.R
-\encoding{UTF-8}
 \name{chisq_to_phi}
 \alias{chisq_to_phi}
 \alias{chisq_to_cohens_w}
@@ -85,7 +84,7 @@ A data frame with the effect size(s), and confidence interval(s). See
 }
 \description{
 Convert between Chi square (\eqn{\chi^2}), phi (\eqn{\phi}), Cramer's V,
-Cohen's \emph{w}, Fei (\emph{פ}) and Pearson's \emph{C} for contingency tables or
+Cohen's \emph{w}, Fei (\u05e4) and Pearson's \emph{C} for contingency tables or
 goodness of fit.
 }
 \details{
@@ -95,7 +94,7 @@ These functions use the following formulae:
 \cr
 \deqn{\text{Cramer's }V = \phi / \sqrt{min(nrow,ncol)-1}}{Cramer's V = \phi / sqrt(min(nrow,ncol)-1)}
 \cr
-\deqn{XXX = w / \sqrt{\frac{1}{min(p_E)}-1}}{Fei = w / sqrt(1/min(p_E) - 1))}
+\deqn{Fei = w / \sqrt{\frac{1}{min(p_E)}-1}}{Fei = w / sqrt(1/min(p_E) - 1))}
 Where \code{p_E} are the expected probabilities.
 \cr
 \deqn{\text{Pearson's }C = \sqrt{\chi^2 / (\chi^2 + n)}}{Pearson's C = sqrt(\chi^2 / (\chi^2 + n))}

From e70a1ca410d45e2df6e50099688edffee617b8d0 Mon Sep 17 00:00:00 2001
From: "Brenton M. Wiernik" <bwiernik@users.noreply.github.com>
Date: Mon, 12 Sep 2022 11:42:13 -0400
Subject: [PATCH 13/14] Update documentation formatting

---
 DESCRIPTION                               |  2 +-
 R/convert_stat_chisq.R                    | 55 +++++++++++++----------
 man/{chisq_to_phi.Rd => convert_chisq.Rd} | 43 ++++++++++--------
 3 files changed, 58 insertions(+), 42 deletions(-)
 rename man/{chisq_to_phi.Rd => convert_chisq.Rd} (83%)

diff --git a/DESCRIPTION b/DESCRIPTION
index 6938f3e94..53e871b13 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -89,7 +89,7 @@ VignetteBuilder:
 Encoding: UTF-8
 Language: en-US
 Roxygen: list(markdown = TRUE)
-RoxygenNote: 7.2.1
+RoxygenNote: 7.2.1.9000
 Config/testthat/edition: 3
 Config/testthat/parallel: true
 Config/Needs/website:
diff --git a/R/convert_stat_chisq.R b/R/convert_stat_chisq.R
index 2172db823..cdf2f707c 100644
--- a/R/convert_stat_chisq.R
+++ b/R/convert_stat_chisq.R
@@ -1,11 +1,13 @@
-#' Conversion Chi-Squared to Phi or Cramer's V
+#' Convert chi-square to correlation-like effect sizes (phi, V, Fei, C)
 #'
-#' Convert between Chi square (\eqn{\chi^2}), phi (\eqn{\phi}), Cramer's V,
-#' Cohen's *w*, Fei (\u05e4) and Pearson's *C* for contingency tables or
-#' goodness of fit.
+#' Convert between \eqn{\chi^2} (chi-square), \eqn{\phi} (phi), Cramer's \eqn{V},
+#' Cohen's \eqn{w}, \ifelse{latex}{\eqn{Fei}}{פ (Fei)} and Pearson's \eqn{C}
+#' for contingency tables or goodness of fit.
 #'
+#' @name chisq_to_phi
+#' @rdname convert_chisq
 #'
-#' @param chisq The Chi-squared statistic.
+#' @param chisq The \eqn{\chi^2} (chi-square) statistic.
 #' @param n Total sample size.
 #' @param nrow,ncol The number of rows/columns in the contingency table.
 #' @param ci Confidence Interval (CI) level
@@ -20,18 +22,25 @@
 #' @return A data frame with the effect size(s), and confidence interval(s). See
 #'   [cramers_v()].
 #'
-#' @details These functions use the following formulae:
-#' \cr
-#' \deqn{\phi = w = \sqrt{\chi^2 / n}}{phi = w = sqrt(\chi^2 / n)}
-#' \cr
-#' \deqn{\text{Cramer's }V = \phi / \sqrt{min(nrow,ncol)-1}}{Cramer's V = \phi / sqrt(min(nrow,ncol)-1)}
-#' \cr
-#' \deqn{Fei = w / \sqrt{\frac{1}{min(p_E)}-1}}{Fei = w / sqrt(1/min(p_E) - 1))}
-#' Where `p_E` are the expected probabilities.
-#' \cr
-#' \deqn{\text{Pearson's }C = \sqrt{\chi^2 / (\chi^2 + n)}}{Pearson's C = sqrt(\chi^2 / (\chi^2 + n))}
-#' \cr\cr
-#' For adjusted versions of *phi* and *V*, see Bergsma, 2013.
+#' @details These functions use the following formulas:
+#'
+#' \deqn{\phi = w = \sqrt{\chi^2 / n}}{phi = w = sqrt(\frac{\chi^2}{n})}
+#' \ifelse{latex}{
+#' \deqn{\textrm{Cramer's } V = \phi / \sqrt{\min(\textit{nrow}, \textit{ncol}) - 1}}
+#' }{
+#' \deqn{\textrm{Cramer's } V = \phi / \sqrt{\min(\textit{nrow}, \textit{ncol }) - 1}}{Cramer's V = \phi / sqrt(min(nrow, ncol) - 1)}
+#' }
+#'
+#' \ifelse{latex}{
+#' \deqn{\textit{Fei} = \phi / \sqrt{[1 / \min(p_E)] - 1}}
+#' }{
+#' \deqn{פ = \phi / \sqrt{[1 / \min(p_E)] - 1}}{פ = w / sqrt(1 / min(p_E) - 1))}
+#' }
+#' Where \eqn{p_E} are the expected probabilities.
+#'
+#' \deqn{\textrm{Pearson's } C = \sqrt{\chi^2 / (\chi^2 + n)}}{Pearson's C = sqrt(\chi^2 / (\chi^2 + n))}
+#'
+#' For adjusted versions of \eqn{\phi} and \eqn{V}, see Bergsma, 2013.
 #'
 #' @inheritSection effectsize_CIs Confidence (Compatibility) Intervals (CIs)
 #' @inheritSection effectsize_CIs CIs and Significance Tests
@@ -161,7 +170,7 @@ chisq_to_phi <- function(chisq, n, nrow = 2, ncol = 2, ci = 0.95, alternative =
 }
 
 
-#' @rdname chisq_to_phi
+#' @rdname convert_chisq
 #' @export
 chisq_to_cohens_w <- function(chisq, n, nrow, ncol, ci = 0.95, alternative = "greater", ...) {
   res <- chisq_to_phi(chisq, n, nrow, ncol, ci = ci, alternative = alternative, adjust = FALSE, dont_stop = TRUE)
@@ -192,7 +201,7 @@ chisq_to_cohens_w <- function(chisq, n, nrow, ncol, ci = 0.95, alternative = "gr
   return(res)
 }
 
-#' @rdname chisq_to_phi
+#' @rdname convert_chisq
 #' @export
 chisq_to_cramers_v <- function(chisq, n, nrow, ncol, ci = 0.95, alternative = "greater", adjust = FALSE, ...) {
   if (ncol == 1 || nrow == 1) {
@@ -227,7 +236,7 @@ chisq_to_cramers_v <- function(chisq, n, nrow, ncol, ci = 0.95, alternative = "g
 }
 
 
-#' @rdname chisq_to_phi
+#' @rdname convert_chisq
 #' @export
 #' @param p Vector of expected values. See [stats::chisq.test()].
 chisq_to_fei <- function(chisq, n, nrow, ncol, p,
@@ -273,7 +282,7 @@ chisq_to_fei <- function(chisq, n, nrow, ncol, p,
   return(res)
 }
 
-#' @rdname chisq_to_phi
+#' @rdname convert_chisq
 #' @export
 chisq_to_pearsons_c <- function(chisq, n, nrow, ncol, ci = 0.95, alternative = "greater", ...) {
   res <- chisq_to_phi(chisq, n, nrow, ncol, ci = ci, alternative = alternative, adjust = FALSE, dont_stop = TRUE)
@@ -293,8 +302,8 @@ chisq_to_pearsons_c <- function(chisq, n, nrow, ncol, ci = 0.95, alternative = "
 
 # Reverse -----------------------------------------------------------------
 
-#' @rdname chisq_to_phi
-#' @param phi The Phi statistic.
+#' @rdname convert_chisq
+#' @param phi The \eqn{\phi} (phi) statistic.
 #' @export
 phi_to_chisq <- function(phi, n, ...) {
   n * (phi^2)
diff --git a/man/chisq_to_phi.Rd b/man/convert_chisq.Rd
similarity index 83%
rename from man/chisq_to_phi.Rd
rename to man/convert_chisq.Rd
index ac5bfc666..1589c7b4f 100644
--- a/man/chisq_to_phi.Rd
+++ b/man/convert_chisq.Rd
@@ -7,7 +7,7 @@
 \alias{chisq_to_fei}
 \alias{chisq_to_pearsons_c}
 \alias{phi_to_chisq}
-\title{Conversion Chi-Squared to Phi or Cramer's V}
+\title{Convert chi-square to correlation-like effect sizes (phi, V, Fei, C)}
 \usage{
 chisq_to_phi(
   chisq,
@@ -56,7 +56,7 @@ chisq_to_pearsons_c(
 phi_to_chisq(phi, n, ...)
 }
 \arguments{
-\item{chisq}{The Chi-squared statistic.}
+\item{chisq}{The \eqn{\chi^2} (chi-square) statistic.}
 
 \item{n}{Total sample size.}
 
@@ -76,30 +76,37 @@ is allowed (e.g., \code{"g"}, \code{"l"}, \code{"two"}...). See \emph{One-Sided
 
 \item{p}{Vector of expected values. See \code{\link[stats:chisq.test]{stats::chisq.test()}}.}
 
-\item{phi}{The Phi statistic.}
+\item{phi}{The \eqn{\phi} (phi) statistic.}
 }
 \value{
 A data frame with the effect size(s), and confidence interval(s). See
 \code{\link[=cramers_v]{cramers_v()}}.
 }
 \description{
-Convert between Chi square (\eqn{\chi^2}), phi (\eqn{\phi}), Cramer's V,
-Cohen's \emph{w}, Fei (\u05e4) and Pearson's \emph{C} for contingency tables or
-goodness of fit.
+Convert between \eqn{\chi^2} (chi-square), \eqn{\phi} (phi), Cramer's \eqn{V},
+Cohen's \eqn{w}, \ifelse{latex}{\eqn{Fei}}{פ (Fei)} and Pearson's \eqn{C}
+for contingency tables or goodness of fit.
 }
 \details{
-These functions use the following formulae:
-\cr
-\deqn{\phi = w = \sqrt{\chi^2 / n}}{phi = w = sqrt(\chi^2 / n)}
-\cr
-\deqn{\text{Cramer's }V = \phi / \sqrt{min(nrow,ncol)-1}}{Cramer's V = \phi / sqrt(min(nrow,ncol)-1)}
-\cr
-\deqn{Fei = w / \sqrt{\frac{1}{min(p_E)}-1}}{Fei = w / sqrt(1/min(p_E) - 1))}
-Where \code{p_E} are the expected probabilities.
-\cr
-\deqn{\text{Pearson's }C = \sqrt{\chi^2 / (\chi^2 + n)}}{Pearson's C = sqrt(\chi^2 / (\chi^2 + n))}
-\cr\cr
-For adjusted versions of \emph{phi} and \emph{V}, see Bergsma, 2013.
+These functions use the following formulas:
+
+\deqn{\phi = w = \sqrt{\chi^2 / n}}{phi = w = sqrt(\frac{\chi^2}{n})}
+\ifelse{latex}{
+\deqn{\textrm{Cramer's } V = \phi / \sqrt{\min(\textit{nrow}, \textit{ncol}) - 1}}
+}{
+\deqn{\textrm{Cramer's } V = \phi / \sqrt{\min(\textit{nrow}, \textit{ncol }) - 1}}{Cramer's V = \phi / sqrt(min(nrow, ncol) - 1)}
+}
+
+\ifelse{latex}{
+\deqn{\textit{Fei} = \phi / \sqrt{[1 / \min(p_E)] - 1}}
+}{
+\deqn{פ = \phi / \sqrt{[1 / \min(p_E)] - 1}}{פ = w / sqrt(1 / min(p_E) - 1))}
+}
+Where \eqn{p_E} are the expected probabilities.
+
+\deqn{\textrm{Pearson's } C = \sqrt{\chi^2 / (\chi^2 + n)}}{Pearson's C = sqrt(\chi^2 / (\chi^2 + n))}
+
+For adjusted versions of \eqn{\phi} and \eqn{V}, see Bergsma, 2013.
 }
 \section{Confidence (Compatibility) Intervals (CIs)}{
 

From d1a25fdd02644b2b06f6c52219374610a4ca941d Mon Sep 17 00:00:00 2001
From: "Mattan S. Ben-Shachar" <mattansb@msbstats.info>
Date: Mon, 12 Sep 2022 20:48:24 +0300
Subject: [PATCH 14/14] add missing Fei

---
 R/xtab.R   | 17 +++++++++--------
 man/phi.Rd | 17 +++++++++--------
 2 files changed, 18 insertions(+), 16 deletions(-)

diff --git a/R/xtab.R b/R/xtab.R
index 6db7ca69f..3479846e3 100644
--- a/R/xtab.R
+++ b/R/xtab.R
@@ -1,8 +1,9 @@
 #' Effect size for contingency tables
 #'
-#' Compute Cramer's *V*, phi (\eqn{\phi}), Cohen's *w*, Fei (\eqn{XXX}), Pearson's
-#' contingency coefficient, Odds ratios, Risk ratios, Cohen's *h* and Cohen's
-#' *g* for contingency tables or goodness-of-fit. See details.
+#' Compute Cramer's *V*, phi (\eqn{\phi}), Cohen's *w*,
+#' \ifelse{latex}{\eqn{Fei}}{פ (Fei)}, Pearson's contingency coefficient, Odds
+#' ratios, Risk ratios, Cohen's *h* and Cohen's *g* for contingency tables or
+#' goodness-of-fit. See details.
 #'
 #' @inheritParams stats::chisq.test
 #' @param ci Confidence Interval (CI) level
@@ -29,11 +30,11 @@
 #' can also be used, but since it is not bounded at 1 (can be larger) its
 #' interpretation is more difficult.
 #' \cr \cr
-#' For goodness-of-fit in 1D tables Cohen's *W*, Fei (\eqn{XXX}) or Pearson's *C*
-#' can be used. Cohen's *w* has no upper bound (can be arbitrarily large,
-#' depending on the expected distribution). Fei is an adjusted Cohen's *w*,
-#' accounting for the expected distribution, making it bounded between 0-1.
-#' Pearson's *C* is also bounded between 0-1.
+#' For goodness-of-fit in 1D tables Cohen's *W*, \ifelse{latex}{\eqn{Fei}}{פ (Fei)}
+#' or Pearson's *C* can be used. Cohen's *w* has no upper bound (can be
+#' arbitrarily large, depending on the expected distribution). *Fei* is an
+#' adjusted Cohen's *w*, accounting for the expected distribution, making it
+#' bounded between 0-1. Pearson's *C* is also bounded between 0-1.
 #' \cr \cr
 #' To summarize, for correlation-like effect sizes, we recommend:
 #'
diff --git a/man/phi.Rd b/man/phi.Rd
index fbf47dafa..8e00c0bbb 100644
--- a/man/phi.Rd
+++ b/man/phi.Rd
@@ -83,9 +83,10 @@ the suffix \verb{_adjusted}), \code{Cohens_w}, \code{Fei}, \code{Odds_ratio}, \c
 (\code{CI_low} and \code{CI_high}).
 }
 \description{
-Compute Cramer's \emph{V}, phi (\eqn{\phi}), Cohen's \emph{w}, Fei (\eqn{XXX}), Pearson's
-contingency coefficient, Odds ratios, Risk ratios, Cohen's \emph{h} and Cohen's
-\emph{g} for contingency tables or goodness-of-fit. See details.
+Compute Cramer's \emph{V}, phi (\eqn{\phi}), Cohen's \emph{w},
+\ifelse{latex}{\eqn{Fei}}{פ (Fei)}, Pearson's contingency coefficient, Odds
+ratios, Risk ratios, Cohen's \emph{h} and Cohen's \emph{g} for contingency tables or
+goodness-of-fit. See details.
 }
 \details{
 \subsection{Correlation-like Effect Sizes}{
@@ -99,11 +100,11 @@ Pearson's \emph{C} should be used, as they are bounded between 0-1. Cohen's \emp
 can also be used, but since it is not bounded at 1 (can be larger) its
 interpretation is more difficult.
 \cr \cr
-For goodness-of-fit in 1D tables Cohen's \emph{W}, Fei (\eqn{XXX}) or Pearson's \emph{C}
-can be used. Cohen's \emph{w} has no upper bound (can be arbitrarily large,
-depending on the expected distribution). Fei is an adjusted Cohen's \emph{w},
-accounting for the expected distribution, making it bounded between 0-1.
-Pearson's \emph{C} is also bounded between 0-1.
+For goodness-of-fit in 1D tables Cohen's \emph{W}, \ifelse{latex}{\eqn{Fei}}{פ (Fei)}
+or Pearson's \emph{C} can be used. Cohen's \emph{w} has no upper bound (can be
+arbitrarily large, depending on the expected distribution). \emph{Fei} is an
+adjusted Cohen's \emph{w}, accounting for the expected distribution, making it
+bounded between 0-1. Pearson's \emph{C} is also bounded between 0-1.
 \cr \cr
 To summarize, for correlation-like effect sizes, we recommend:
 \itemize{