use consistent end-of-line sequence

Author: simonpcouch

DESCRIPTION

@@ -2,7 +2,7 @@ Type: Package
 Package: infer
 Title: Tidy Statistical Inference
 Version: 1.0.4.9000
-Authors@R:c(
+Authors@R: c(
     person("Andrew", "Bray", , "abray@reed.edu", role = "aut"),
     person("Chester", "Ismay", , "chester.ismay@gmail.com", role = "aut",
            comment = c(ORCID = "0000-0003-2820-2547")),
@@ -65,5 +65,5 @@ Config/Needs/website: tidyverse/tidytemplate
 Encoding: UTF-8
 LazyData: true
 Roxygen: list(markdown = TRUE)
-RoxygenNote: 7.2.1
+RoxygenNote: 7.2.3
 Config/testthat/edition: 3

R/assume.R

@@ -1,76 +1,76 @@
 #' Define a theoretical distribution
-#' 
-#' @description 
-#' 
+#'
+#' @description
+#'
 #' This function allows the user to define a null distribution based on
 #' theoretical methods. In many infer pipelines, `assume()` can be
 #' used in place of [generate()] and [calculate()] to create a null
-#' distribution. Rather than outputting a data frame containing a 
-#' distribution of test statistics calculated from resamples of the observed 
+#' distribution. Rather than outputting a data frame containing a
+#' distribution of test statistics calculated from resamples of the observed
 #' data, `assume()` outputs a more abstract type of object just containing
 #' the distributional details supplied in the `distribution` and `df` arguments.
 #' However, `assume()` output can be passed to [visualize()], [get_p_value()],
 #' and [get_confidence_interval()] in the same way that simulation-based
 #' distributions can.
-#' 
+#'
 #' To define a theoretical null distribution (for use in hypothesis testing),
 #' be sure to provide a null hypothesis via [hypothesize()]. To define a
-#' theoretical sampling distribution (for use in confidence intervals), 
+#' theoretical sampling distribution (for use in confidence intervals),
 #' provide the output of [specify()]. Sampling distributions (only
 #' implemented for `t` and `z`) lie on the scale of the data, and will be
 #' recentered and rescaled to match the corresponding `stat` given in
 #' [calculate()] to calculate the observed statistic.
-#' 
+#'
 #' @param x The output of [specify()] or [hypothesize()], giving the
 #'   observed data, variable(s) of interest, and (optionally) null hypothesis.
 #' @param distribution The distribution in question, as a string. One of
 #'   `"F"`, `"Chisq"`, `"t"`, or `"z"`.
 #' @param df Optional. The degrees of freedom parameter(s) for the `distribution`
 #'   supplied, as a numeric vector. For `distribution = "F"`, this should have
-#'   length two (e.g. `c(10, 3)`). For `distribution = "Chisq"` or 
-#'   `distribution = "t"`, this should have length one. For 
-#'   `distribution = "z"`, this argument is not required. The package 
-#'   will supply a message if the supplied `df` argument is different from 
+#'   length two (e.g. `c(10, 3)`). For `distribution = "Chisq"` or
+#'   `distribution = "t"`, this should have length one. For
+#'   `distribution = "z"`, this argument is not required. The package
+#'   will supply a message if the supplied `df` argument is different from
 #'   recognized values. See the Details section below for more information.
 #' @param ... Currently ignored.
-#'  
+#'
 #' @return An infer theoretical distribution that can be passed to helpers
-#'   like [visualize()], [get_p_value()], and [get_confidence_interval()]. 
-#' 
-#' @details 
-#' 
+#'   like [visualize()], [get_p_value()], and [get_confidence_interval()].
+#'
+#' @details
+#'
 #' Note that the assumption being expressed here, for use in theory-based
-#' inference, only extends to _distributional_ assumptions: the null 
-#' distribution in question and its parameters. Statistical inference with 
-#' infer, whether carried out via simulation (i.e. based on pipelines 
-#' using [generate()] and [calculate()]) or theory (i.e. with `assume()`), 
+#' inference, only extends to _distributional_ assumptions: the null
+#' distribution in question and its parameters. Statistical inference with
+#' infer, whether carried out via simulation (i.e. based on pipelines
+#' using [generate()] and [calculate()]) or theory (i.e. with `assume()`),
 #' always involves the condition that observations are independent of
 #' each other.
-#' 
-#' `infer` only supports theoretical tests on one or two means via the 
+#'
+#' `infer` only supports theoretical tests on one or two means via the
 #' `t` distribution and one or two proportions via the `z`.
-#' 
-#' For tests comparing two means, if `n1` is the group size for one level of 
+#'
+#' For tests comparing two means, if `n1` is the group size for one level of
 #' the explanatory variable, and `n2` is that for the other level, `infer`
-#' will recognize the following degrees of freedom (`df`) arguments: 
-#' 
+#' will recognize the following degrees of freedom (`df`) arguments:
+#'
 #' * `min(n1 - 1, n2 - 1)`
 #' * `n1 + n2 - 2`
 #' * The `"parameter"` entry of the analogous `stats::t.test()` call
 #' * The `"parameter"` entry of the analogous `stats::t.test()` call with `var.equal = TRUE`
-#' 
-#' By default, the package will use the `"parameter"` entry of the analogous 
+#'
+#' By default, the package will use the `"parameter"` entry of the analogous
 #' `stats::t.test()` call with `var.equal = FALSE` (the default).
-#' 
-#' @examples   
+#'
+#' @examples
 #' # construct theoretical distributions ---------------------------------
-#' 
+#'
 #' # F distribution
 #' # with the `partyid` explanatory variable
-#' gss %>% 
-#'   specify(age ~ partyid) %>% 
+#' gss %>%
+#'   specify(age ~ partyid) %>%
 #'   assume(distribution = "F")
-#' 
+#'
 #' # Chi-squared goodness of fit distribution
 #' # on the `finrela` variable
 #' gss %>%
@@ -83,68 +83,68 @@
 #'                     "far above average" = 1/6,
 #'                     "DK" = 1/6)) %>%
 #'   assume("Chisq")
-#' 
+#'
 #' # Chi-squared test of independence
 #' # on the `finrela` and `sex` variables
 #' gss %>%
 #'   specify(formula = finrela ~ sex) %>%
 #'   assume(distribution = "Chisq")
-#' 
+#'
 #' # T distribution
-#' gss %>% 
+#' gss %>%
 #'   specify(age ~ college) %>%
 #'   assume("t")
-#' 
+#'
 #' # Z distribution
 #' gss %>%
 #'   specify(response = sex, success = "female") %>%
 #'   assume("z")
-#' 
+#'
 #' \dontrun{
 #' # each of these distributions can be passed to infer helper
 #' # functions alongside observed statistics!
-#' 
+#'
 #' # for example, a 1-sample t-test -------------------------------------
-#' 
-#' # calculate the observed statistic 
+#'
+#' # calculate the observed statistic
 #' obs_stat <- gss %>%
 #'   specify(response = hours) %>%
 #'   hypothesize(null = "point", mu = 40) %>%
 #'   calculate(stat = "t")
-#' 
+#'
 #' # construct a null distribution
 #' null_dist <- gss %>%
 #'   specify(response = hours) %>%
 #'   assume("t")
-#' 
+#'
 #' # juxtapose them visually
-#' visualize(null_dist) + 
+#' visualize(null_dist) +
 #'   shade_p_value(obs_stat, direction = "both")
-#'   
+#'
 #' # calculate a p-value
 #' get_p_value(null_dist, obs_stat, direction = "both")
-#' 
+#'
 #' # or, an F test ------------------------------------------------------
-#' 
-#' # calculate the observed statistic 
-#' obs_stat <- gss %>% 
+#'
+#' # calculate the observed statistic
+#' obs_stat <- gss %>%
 #'   specify(age ~ partyid) %>%
 #'   hypothesize(null = "independence") %>%
 #'   calculate(stat = "F")
-#' 
+#'
 #' # construct a null distribution
-#' null_dist <- gss %>% 
+#' null_dist <- gss %>%
 #'   specify(age ~ partyid) %>%
 #'   assume(distribution = "F")
-#' 
+#'
 #' # juxtapose them visually
-#' visualize(null_dist) + 
+#' visualize(null_dist) +
 #'   shade_p_value(obs_stat, direction = "both")
-#'   
+#'
 #' # calculate a p-value
 #' get_p_value(null_dist, obs_stat, direction = "both")
 #' }
-#'     
+#'
 #' @export
 assume <- function(x, distribution, df = NULL, ...) {
   if (!inherits(x, "infer")) {
@@ -153,11 +153,11 @@ assume <- function(x, distribution, df = NULL, ...) {
       "likely `specify()` or `hypothesize()`."
     )
   }
-  
+
   # check that `distribution` aligns with what is expected from
   # `x` and that `distribution` and `df` are consistent with each other
   df <- check_distribution(x, distribution, df, ...)
-  
+
   structure(
     glue_null(
       "{distribution_desc(distribution)} distribution{df_desc(df)}.",
@@ -184,19 +184,19 @@ assume <- function(x, distribution, df = NULL, ...) {
 # check that the distribution is well-specified
 check_distribution <- function(x, distribution, df, ...) {
   dist <- tolower(distribution)
-  
+
   if (!dist %in% c("f", "chisq", "t", "z")) {
     stop_glue(
       'The distribution argument must be one of "Chisq", "F", "t", or "z".'
     )
   }
-  
+
   if ((dist == "f" && attr(x, "theory_type") != "ANOVA") ||
-      (dist == "chisq" && !attr(x, "theory_type") %in% c("Chi-square test of indep", 
+      (dist == "chisq" && !attr(x, "theory_type") %in% c("Chi-square test of indep",
                                                          "Chi-square Goodness of Fit")) ||
-      (dist == "t" && !attr(x, "theory_type") %in% c("One sample t", 
+      (dist == "t" && !attr(x, "theory_type") %in% c("One sample t",
                                                      "Two sample t")) ||
-      (dist == "z" && !attr(x, "theory_type") %in% c("One sample prop z", 
+      (dist == "z" && !attr(x, "theory_type") %in% c("One sample prop z",
                                                      "Two sample props z"))) {
     if (has_explanatory(x)) {
       msg_tail <- glue_null(
@@ -206,33 +206,33 @@ check_distribution <- function(x, distribution, df, ...) {
     } else {
       msg_tail <- "no explanatory variable."
     }
-    
+
     stop_glue(
       'The supplied distribution "{distribution}" is not well-defined for a ',
       "{get_stat_type_desc(attr(x, 'type_desc_response'))} response ",
       "variable ({response_name(x)}) and ", msg_tail
     )
   }
-  
+
   if (!is.numeric(df) && !is.null(df)) {
     stop_glue(
       "`assume()` expects the `df` argument to be a numeric vector, ",
       "but you supplied a {list(class(df))} object."
     )
   }
-  
+
   if (length(list(...)) != 0) {
     plural <- length(list(...)) != 1
     dots <- list(...)
-    
+
     stop_glue(
       "`assume()` ignores the dots `...` argument, though the ",
       "argument{if (plural) 's' else ''} `{list(dots)}` ",
       "{if (plural) 'were' else 'was'} supplied. Did you forget to ",
       "concatenate the `df` argument with `c()`?"
     )
   }
-  
+
   if (dist_df_length(distribution) != length(df) && !is.null(df)) {
     plural <- length(df) != 1
     stop_glue(
@@ -242,9 +242,9 @@ check_distribution <- function(x, distribution, df, ...) {
       '{if (plural) "were" else "was"} supplied.'
     )
   }
-  
+
   df <- determine_df(x, dist, df)
-  
+
   return(df)
 }
 
@@ -286,12 +286,12 @@ df_desc <- function(df) {
     ""
   } else {
     plural <- length(df) != 1
-    
+
     paste0(
-      ' with ', 
-      if (plural) {paste0(round(df), collapse = " and ")} else {round(df)}, 
-      ' degree', 
-      if (!plural && df == 1) {''} else {'s'}, 
+      ' with ',
+      if (plural) {paste0(round(df), collapse = " and ")} else {round(df)},
+      ' degree',
+      if (!plural && df == 1) {''} else {'s'},
       ' of freedom')
   }
 }
@@ -317,18 +317,18 @@ determine_df <- function(x, dist, df) {
       "for `df` is correct (see `?assume` for recognized values) or ",
       "supply `df = NULL` to `assume()`."
     )
-    
+
     return(df)
   }
-  
+
   if (is.null(df)) {
     df <- acceptable_dfs(x)
   }
-  
+
   if (attr(x, "theory_type") == "Two sample t") {
     df <- df[1]
   }
-  
+
   df
 }
 
@@ -339,16 +339,16 @@ acceptable_dfs <- function(x) {
       # t.test param with var.equal = FALSE
       unname(
         unlist(
-          attr(x, "distr_param") <- 
-            stats::t.test(response_variable(x) ~ 
+          attr(x, "distr_param") <-
+            stats::t.test(response_variable(x) ~
                           explanatory_variable(x))[["parameter"]]
         )
       ),
       # t.test param with var.equal = TRUE
       unname(
         unlist(
-          attr(x, "distr_param") <- 
-            stats::t.test(response_variable(x) ~ 
+          attr(x, "distr_param") <-
+            stats::t.test(response_variable(x) ~
                           explanatory_variable(x),
                           var.equal = TRUE)[["parameter"]]
         )
@@ -368,7 +368,7 @@ acceptable_dfs <- function(x) {
     )
   } else {
     c(
-      unname(unlist(attr(x, "distr_param"))), 
+      unname(unlist(attr(x, "distr_param"))),
       unname(unlist(attr(x, "distr_param2")))
     )
   }

R/deprecated.R

@@ -1,6 +1,6 @@
 #' Deprecated functions and objects
 #'
-#' These functions and objects should no longer be used. They will be removed 
+#' These functions and objects should no longer be used. They will be removed
 #' in a future release of \code{infer}.
 #' @param x See the non-deprecated function.
 #' @param level See the non-deprecated function.