R/plot_magnitude.R

In esci: Estimation Statistics with Confidence Intervals

#' Plot the mean or median for a continuous variable
#'
#'
#' @description `plot_magnitude` creates a ggplot2 plot suitable for visualizing
#' the results of a study with one group and one or more continuous outcome
#' variables.  It can highlight either the mean or median of each outcome
#' variable.  This function can be passed an esci_estimate object generated by
#' [esci::estimate_magnitude()]
#'
#'
#' @inherit plot_describe details
#'
#'
#' @param estimate - An esci_estimate object generated by
#'   [esci::estimate_magnitude()]
#'
#' @param effect_size - Optional; One of 'mean' (default) or 'median'; specifies
#'   which measure of central tendency to highlight; note medians are only
#'   available if the esci_estimate object was generated from raw data
#' @param data_layout - Optional; One of 'random' (default), 'swarm', or 'none'
#'   for how raw data (if available) will be displayed
#' @param data_spread - Optional real number > 0 specifying width raw data (if
#'   available) should take on the graph; default is 0.25; default spacing
#'   between two groups on the graph is 1
#' @param error_layout - Optional; One of 'halfeye', 'eye', 'gradient' or 'none'
#'   for how expected sampling error of the measure of central tendency should
#'   be displayed.  Currently, only applies if 'mean' is selected as measure of
#'   central tendency
#' @param error_scale - Optional real number > 0 specifying width of the expected
#'   sampling error visualization; default is 0.3
#' @param error_nudge - Optional amount by which error distribution should be
#'   offset; default is 0.35
#' @param error_normalize - Optional; One of 'groups' (default), 'all', or
#'   'panels' specifying how width of expected sampling error distributions
#'   should be calculated.
#' @param rope - Optional two-item vector specifying a region of practical
#'   equivalence (ROPE) to be highlighted on the plot.  For a point null
#'   hypothesis, pass the same value (e.g. c(0, 0) to test a point null of
#'   exactly 0); for an interval null pass ascending values (e.g. c(-1, 1))
#' @param ggtheme - Optional ggplot2 theme object to control overall styling;
#'   defaults to [ggplot2::theme_classic()]
#'
#'
#' @inherit plot_describe return
#'
#'
#' @inherit estimate_magnitude examples
#'
#'
#' @export
plot_magnitude <- function(
  estimate,
  effect_size = c("mean", "median"),
  data_layout = c("random", "swarm", "none"),
  data_spread = 0.25,
  error_layout = c("halfeye", "eye", "gradient", "none"),
  error_scale = 0.3,
  error_nudge = 0.35,
  error_normalize = c("groups", "all", "panels"),
  rope = c(NA, NA),
  ggtheme = NULL
) {

  # Input checks ---------------------------------------------------------------
  warnings <- NULL
  x_value <- ta_LL <- ta_UL <- NULL

  esci_assert_type(estimate, "is.estimate")
  effect_size <- match.arg(effect_size)
  if (effect_size == "median" & is.null(estimate$es_median)) {
    stop("effect_size parameter is 'median' but no median-based effect size available to plot")
  }
  data_layout <- match.arg(data_layout)
  error_layout <- match.arg(error_layout)
  error_normalize <- match.arg(error_normalize)
  if (is.null(data_spread) | !is.numeric(data_spread) | data_spread < 0) {
    warnings <- c(
      warnings,
      glue::glue(
        "data_spread = {data_spread} but this is invalid; replaced with 0.25"
      )
    )
    data_spread <- 0.25
  }
  if (is.null(error_scale) | !is.numeric(error_scale) | error_scale < 0) {
    warnings <- c(
      warnings,
      glue::glue(
        "error_scale = {error_scale} but this is invalid; replaced with 0.3"
      )
    )
    error_scale = 0.3
  }
  if (is.null(error_nudge) | !is.numeric(error_nudge) | error_nudge < 0) {
    warnings <- c(
      warnings,
      glue::glue(
        "error_nudge = {error_nudge} but this is invalid; replaced with 0.25"
      )
    )
    error_nudge <- 0.25
  }
  if(is.null(ggtheme)) { ggtheme <- ggplot2::theme_classic()}


  # Data prep --------------------------------------
  conf_level <- estimate$properties$conf_level

  # Raw data
  plot_raw <- !is.null(estimate$raw_data) & data_layout != "none"
  nudge <- if(plot_raw) error_nudge else 0

  # Group data
  if (effect_size == "mean") {
    gdata <- estimate$es_mean
  } else {
    gdata <- estimate$es_median
  }
  gdata$type <- as.factor("summary")
  gdata$x_label <- gdata$outcome_variable_name
  gdata$y_value <- gdata$effect_size
  gdata$x_value <- seq(from = 1, to = nrow(gdata), by = 1)
  gdata$nudge <- nudge
  if (nrow(gdata[gdata$SE <= 0, ]) > 0) {
    gdata[gdata$SE <= 0, ]$SE <- .Machine$double.xmin
  }

  # Raw data
  if (plot_raw) {
    rdata <- estimate$raw_data
    rdata$type <- as.factor("raw")
    rdata$x_label <- rdata$grouping_variable
    rdata$y_value <- rdata$outcome_variable
    rdata$x_value <- gdata[match(rdata$x_label, gdata$x_label), "x_value"]
    rdata$nudge <- 0
    nudge <- error_nudge
  } else {
    nudge <- 0
  }


  # Initialize null information
  plot_null <- FALSE
  interval_null <- FALSE
  null_symbol <- if (effect_size == "mean") "mu" else "eta"

  if (length(rope) == 1) rope[[2]] = rope[[1]]

  if (!is.na(rope[[1]])) {
    plot_null <- TRUE
    null_label <- paste(
      "H[0]: ",
      null_symbol,
      " == ",
      rope[[1]],
      sep = ""
    )
  }

  if (!is.na(rope[[1]]) & !is.na(rope[[2]])) {
    if (rope[[1]] != rope[[2]]) {
      plot_null <- TRUE
      interval_null <- TRUE
      null_label <- glue::glue(
        "{rope[[1]]}*' < '*{null_symbol}*' < '*{rope[[2]]}"
      )
    }
  }


  # Build plot ------------------------------------
  # Base plot
  myplot <- ggplot2::ggplot() + ggtheme


  # 90% CI
  if (interval_null) {
    alpha <- 1 - estimate$properties$conf_level
    conf_level <- c(
      1 - (alpha*2),
      conf_level
    )


    myplot <- myplot + ggplot2::geom_segment(
      data = gdata,
      aes(
        x = x_value + nudge,
        xend = x_value + nudge,
        y = ta_LL,
        yend = ta_UL
      ),
      colour = "black",
      size = 2
    )

    myplot <- esci_plot_layers(myplot, "ta_CI")
  }

  # Group data
  error_glue <-esci_plot_group_data(effect_size)
  error_call <- esci_plot_error_layouts(error_layout)
  error_expression <- parse(text = glue::glue(error_glue))
  myplot <- try(eval(error_expression))
  myplot <- esci_plot_layers(myplot, "group_data")

  # Raw data
  if (plot_raw) {
    raw_expression <- esci_plot_raw_data(myplot, data_layout, data_spread)
    myplot <- try(eval(raw_expression))
  }

  # Plot nulls
  if (plot_null & !interval_null) {
    myplot <- myplot + ggplot2::geom_hline(
      yintercept = rope[[1]],
      colour = "red",
      size = 1,
      linetype = "solid"
    )
    myplot <- esci_plot_layers(myplot, "null_line")

    myplot <- myplot + ggplot2::annotate(
      geom = "text",
      label = null_label,
      y = rope[[1]],
      x = Inf,
      vjust = 0,
      hjust = "inward",
      parse = TRUE
    )
  }

  if (plot_null & interval_null) {
    myplot <- myplot + ggplot2::geom_hline(
      yintercept = rope[[2]] - ((rope[[2]] - rope[[1]])/2),
      colour = "red",
      size = 1,
      linetype = "solid"
    )
    myplot <- esci_plot_layers(myplot, "null_line")

    myplot <- myplot + ggplot2::geom_rect(
      ggplot2::aes(
        ymin = rope[[1]],
        ymax = rope[[2]],
        xmin = -Inf,
        xmax = Inf
      ),
      alpha = 0.12,
      fill = "red"
    )
    myplot <- esci_plot_layers(myplot, "null_interval")

  }


  # Customize plot -------------------------------
  # Default aesthetics
  myplot <- esci_plot_simple_aesthetics(myplot, use_ggdist = (effect_size == "mean"))

  # X axis
  myplot <- myplot + ggplot2::scale_x_continuous(
    breaks = gdata$x_value + (gdata$nudge*.5),
    labels = gdata$x_label
  )
  myplot <- myplot + ggplot2::coord_cartesian(
    xlim = c(min(gdata$x_value)-0.5, max(gdata$x_value)+0.75)
  )

  # Labels -----------------------------
  vnames <- paste(estimate$es_mean$outcome_variable_name, collapse = ", ")
  esize <- paste(toupper(substr(effect_size, 1, 1)), substr(effect_size, 2, nchar(effect_size)), sep = "")
  ylab <- glue::glue("{vnames}\n{if (plot_raw) 'Data, ' else ''}{esize} and {glue::glue_collapse(conf_level*100, sep = '%, ')}% Confidence Interval")
  xlab <- NULL
  myplot <- myplot + ggplot2::xlab(xlab) + ggplot2::ylab(ylab)


  myplot <- myplot + ggplot2::theme(
    axis.text.y = ggtext::element_markdown(),
    axis.title.y = ggtext::element_markdown(),
    axis.text.x = ggtext::element_markdown(),
    axis.title.x = ggtext::element_markdown()
  )

  # Attach warnings and return    -------------------
  myplot$warnings <- c(myplot$warnings, warnings)

  return(myplot)

}


#' Plot an estimated Pearson's r value
#'
#'
#' @description
#' `plot_correlation` creates a ggplot2 plot suitable for visualizing an
#' estimate correlation between two continuous variables (Pearson's *r*). This
#' function can be passed an esci_estimate object generated by
#' [esci::estimate_r()]
#'
#'
#' @inherit plot_describe details
#'
#'
#' @param estimate - An esci_estimate object generated by
#'   [esci::estimate_r()]
#'
#' @param error_layout - Optional; One of 'halfeye', 'eye', 'gradient' or 'none'
#'   for how expected sampling error of the measure of central tendency should
#'   be displayed.  **Caution - the displayed error distributions do not seem
#'   correct yet**
#' @param error_scale - Optional real number > 0 specifying width of the expected
#'   sampling error visualization; default is 0.3
#' @param error_normalize - Optional; One of 'groups' (default), 'all', or
#'   'panels' specifying how width of expected sampling error distributions
#'   should be calculated.
#' @param rope - Optional two-item vector specifying a region of practical
#'   equivalence (ROPE) to be highlighted on the plot.  For a point null
#'   hypothesis, pass the same value (e.g. c(0, 0) to test a point null of
#'   exactly 0); for an interval null pass ascending values (e.g. c(-1, 1))
#' @param ggtheme - Optional ggplot2 theme object to control overall styling;
#'   defaults to [ggplot2::theme_classic()]
#'
#'
#' @inherit plot_describe return
#'
#'
#' @inherit estimate_r examples
#'
#'
#' @export
plot_correlation <- function(
  estimate,
  error_layout = c("halfeye", "eye", "gradient", "none"),
  error_scale = 0.3,
  error_normalize = c("groups", "all", "panels"),
  rope = c(NA, NA),
  ggtheme = NULL
) {

  # Input checks ---------------------------------------------------------------
  x_value <- ta_LL <- ta_UL <- NULL
  warnings <- NULL

  esci_assert_type(estimate, "is.estimate")
  error_layout <- match.arg(error_layout)
  error_normalize <- match.arg(error_normalize)
  if (is.null(error_scale) | !is.numeric(error_scale) | error_scale < 0) {
    warnings <- c(
      warnings,
      glue::glue(
        "error_scale = {error_scale} but this is invalid; replaced with 0.3"
      )
    )
    error_scale = 0.3
  }
  if(is.null(ggtheme)) { ggtheme <- ggplot2::theme_classic()}

  # Data prep --------------------------------------
  conf_level <- estimate$properties$conf_level
  effect_size <- "r"
  nudge <- 0

  # Initialize null information
  plot_null <- FALSE
  interval_null <- FALSE
  null_symbol <- "rho"

  if (length(rope) == 1) rope[[2]] = rope[[1]]

  if (!is.na(rope[[1]])) {
    plot_null <- TRUE
    null_label <- paste(
      "H[0]: ",
      null_symbol,
      " == ",
      rope[[1]],
      sep = ""
    )
  }

  if (!is.na(rope[[1]]) & !is.na(rope[[2]])) {
    if (rope[[1]] != rope[[2]]) {
      plot_null <- TRUE
      interval_null <- TRUE
      null_label <- glue::glue(
        "{rope[[1]]}*' < '*{null_symbol}*' < '*{rope[[2]]}"
      )
    }
  }

  gdata <- estimate$es_r
  gdata$type <- as.factor("summary")
  gdata$x_label <- gdata$effect
  gdata$y_value <- gdata$effect_size
  gdata$x_value <- seq(from = 1, to = nrow(gdata), by = 1)
  gdata$nudge <- nudge


  # Build plot ------------------------------------
  # Base plot
  myplot <- ggplot2::ggplot() + ggtheme


  # Plot nulls
  if (plot_null & !interval_null) {
    myplot <- myplot + ggplot2::geom_hline(
      yintercept = rope[[1]],
      colour = "red",
      size = 1,
      linetype = "solid"
    )
    myplot <- esci_plot_layers(myplot, "null_line")

    myplot <- myplot + ggplot2::annotate(
      geom = "text",
      label = null_label,
      y = rope[[1]],
      x = Inf,
      vjust = 0,
      hjust = "inward",
      parse = TRUE
    )
  }

  if (plot_null & interval_null) {
    myplot <- myplot + ggplot2::geom_hline(
      yintercept = rope[[2]] - ((rope[[2]] - rope[[1]])/2),
      colour = "red",
      size = 1,
      linetype = "solid"
    )
    myplot <- esci_plot_layers(myplot, "null_line")

    myplot <- myplot + ggplot2::geom_rect(
      ggplot2::aes(
        ymin = rope[[1]],
        ymax = rope[[2]],
        xmin = -Inf,
        xmax = Inf
      ),
      alpha = 0.12,
      fill = "red"
    )
    myplot <- esci_plot_layers(myplot, "null_interval")

  }

  # 90% CI
  if (interval_null) {
    alpha <- 1 - estimate$properties$conf_level
    conf_level <- c(
      1 - (alpha*2),
      conf_level
    )


    myplot <- myplot + ggplot2::geom_segment(
      data = gdata,
      aes(
        x = x_value + nudge,
        xend = x_value + nudge,
        y = ta_LL,
        yend = ta_UL
      ),
      colour = "black",
      size = 2
    )

    myplot <- esci_plot_layers(myplot, "ta_CI")
  }


  # Group data
  error_glue <- esci_plot_group_data(effect_size = effect_size)
  error_call <- esci_plot_error_layouts(error_layout)
  error_expression <- parse(text = glue::glue(error_glue))
  myplot <- try(eval(error_expression))


  # Customize plot ------------------------------
  # Default look
  myplot <- esci_plot_simple_aesthetics(myplot, use_ggdist = TRUE)

  # X axis
  myplot <- myplot + ggplot2::scale_x_continuous(
    breaks = gdata$x_value + gdata$nudge,
    labels = gdata$x_label,
  )

  myplot <- myplot + ggplot2::coord_cartesian(
    xlim = c(min(gdata$x_value)-0.5, max(gdata$x_value)+0.5)
  )

  #Labels
  ylab <- glue::glue("Pearson's *r* and {conf_level*100}% Confidence Interval")
  xlab <- NULL
  myplot <- myplot + ggplot2::xlab(xlab) + ggplot2::ylab(ylab)

  myplot <- myplot + ggplot2::theme(
    axis.title.x = ggtext::element_markdown(),
    axis.title.y = ggtext::element_markdown(),
    axis.text.x = ggtext::element_markdown()
  )


  # Limits
  myplot <- myplot + ylim(-1, 1)

  # Attach warnings and return    -------------------
  myplot$warnings <- c(myplot$warnings, warnings)

  return(myplot)

}


#' Plot an estimated proportion
#'
#'
#' @description
#' `plot_proportion` creates a ggplot2 plot suitable for visualizing an
#' estimated proportion from a categorical variable. This function can be passed
#' an esci_estimate object generated by [esci::estimate_proportion()]
#'
#'
#' @inherit plot_describe details
#'
#'
#' @param estimate - An esci_estimate object generated by
#'   [esci::estimate_proportion()]
#' @param error_layout - Optional; One of 'halfeye', 'eye', 'gradient' or 'none'
#'   for how expected sampling error of the measure of central tendency should
#'   be displayed.  **Caution - the displayed error distributions do not seem
#'   correct yet**
#' @param error_scale - Optional real number > 0 specifying width of the expected
#'   sampling error visualization; default is 0.3
#' @param error_normalize - Optional; One of 'groups' (default), 'all', or
#'   'panels' specifying how width of expected sampling error distributions
#'   should be calculated.
#' @param rope - Optional two-item vector specifying a region of practical
#'   equivalence (ROPE) to be highlighted on the plot.  For a point null
#'   hypothesis, pass the same value (e.g. c(0, 0) to test a point null of
#'   exactly 0); for an interval null pass ascending values (e.g. c(-1, 1))
#' @param plot_possible - Boolean; defaults to FALSE; TRUE to plot lines at each
#'   discrete proportion possible given the sample size (e.g for a proportion
#'   with 10 total cases, would draw lines at 0, .1, .2, etc.)
#' @param ggtheme - Optional ggplot2 theme object to control overall styling;
#'   defaults to [ggplot2::theme_classic()]
#'
#'
#' @inherit plot_describe return
#'
#'
#' @inherit estimate_proportion examples
#'
#'
#' @export
plot_proportion <- function(
  estimate,
  error_layout = c("halfeye", "eye", "gradient", "none"),
  error_scale = 0.3,
  error_normalize = c("groups", "all", "panels"),
  rope = c(NA, NA),
  plot_possible = FALSE,
  ggtheme = NULL
) {

  # Input checks ---------------------------------------------------------------
  warnings <- NULL
  y <- type <- upr <- lwr <- predicted <- x_value <- ta_LL <- ta_UL <- possible <- NULL

  esci_assert_type(estimate, "is.estimate")
  error_layout <- match.arg(error_layout)
  error_normalize <- match.arg(error_normalize)
  if (is.null(error_scale) | !is.numeric(error_scale) | error_scale < 0) {
    warnings <- c(
      warnings,
      glue::glue(
        "error_scale = {error_scale} but this is invalid; replaced with 0.3"
      )
    )
    error_scale = 0.3
  }
  if(is.null(ggtheme)) { ggtheme <- ggplot2::theme_classic()}


  # Data prep --------------------------------------
  conf_level <- estimate$properties$conf_level
  nudge <- 0
  effect_size <- "P"

  # Initialize null information
  plot_null <- FALSE
  interval_null <- FALSE
  null_symbol <- "Pi"

  if (length(rope) == 1) rope[[2]] = rope[[1]]

  if (!is.na(rope[[1]])) {
    plot_null <- TRUE
    null_label <- paste(
      "H[0]: ",
      null_symbol,
      " == ",
      rope[[1]],
      sep = ""
    )
  }

  if (!is.na(rope[[1]]) & !is.na(rope[[2]])) {
    if (rope[[1]] != rope[[2]]) {
      plot_null <- TRUE
      interval_null <- TRUE
      null_label <- glue::glue(
        "{rope[[1]]}*' < '*{null_symbol}*' < '*{rope[[2]]}"
      )
    }
  }

  clevel <- estimate$overview$outcome_variable_level[[1]]

  gdata <- estimate$overview
  gdata <- gdata[!is.na(gdata$P), ]
  gdata <- gdata[!is.null(gdata$P), ]

  if (plot_null & !is.null(clevel)) {
    gdata <- gdata[gdata$outcome_variable_level == clevel, ]
  }

  gdata$type <- as.factor("summary")
  gdata$x_label <- gdata$outcome_variable_level
  gdata$y_value <- gdata$P
  gdata$x_value <- seq(from = 1, to = nrow(gdata), by = 1)
  gdata$nudge <- nudge
  gdata$LL <- gdata$P_LL
  gdata$UL <- gdata$P_UL


  # Build plot ------------------------------------
  # Base plot
  myplot <- ggplot2::ggplot() + ggtheme


  # Plot nulls
  if (plot_null & !interval_null) {
    myplot <- myplot + ggplot2::geom_hline(
      yintercept = rope[[1]],
      colour = "red",
      size = 1,
      linetype = "solid"
    )
    myplot <- esci_plot_layers(myplot, "null_line")

    myplot <- myplot + ggplot2::annotate(
      geom = "text",
      label = null_label,
      y = rope[[1]],
      x = Inf,
      vjust = 0,
      hjust = "inward",
      parse = TRUE
    )
  }

  if (plot_null & interval_null) {
    myplot <- myplot + ggplot2::geom_hline(
      yintercept = rope[[2]] - ((rope[[2]] - rope[[1]])/2),
      colour = "red",
      size = 1,
      linetype = "solid"
    )
    myplot <- esci_plot_layers(myplot, "null_line")

    myplot <- myplot + ggplot2::geom_rect(
      ggplot2::aes(
        ymin = rope[[1]],
        ymax = rope[[2]],
        xmin = -Inf,
        xmax = Inf
      ),
      alpha = 0.12,
      fill = "red"
    )
    myplot <- esci_plot_layers(myplot, "null_interval")

  }


  # 90% CI
  if (interval_null) {
    alpha <- 1 - estimate$properties$conf_level
    conf_level <- c(
      1 - (alpha*2),
      conf_level
    )


    myplot <- myplot + ggplot2::geom_segment(
      data = gdata,
      aes(
        x = x_value + nudge,
        xend = x_value + nudge,
        y = ta_LL,
        yend = ta_UL
      ),
      colour = "black",
      size = 2
    )

    myplot <- esci_plot_layers(myplot, "ta_CI")
  }


  # Group data
  error_glue <- esci_plot_group_data(effect_size = effect_size)
  error_call <- esci_plot_error_layouts(error_layout)
  error_expression <- parse(text = glue::glue(error_glue))
  myplot <- try(eval(error_expression))


  # Discrete lines

  if (plot_possible) {
    if (is.null(estimate$es_proportion)) {
      tn <- estimate$overview$n[[1]]
    } else {
      tn <- estimate$es_proportion$n[[1]]
    }

    if (tn <= 1000) {
      mydf <- data.frame(possible = seq(from = 0, to = tn)/tn)
      myplot <- myplot + ggplot2::geom_hline(data = mydf, ggplot2::aes(yintercept = possible), linetype = 'solid', color = 'gray70', size = 0.25)
    }

  }

  # Customize ----------------------------
  # Default look
  myplot <- esci_plot_simple_aesthetics(myplot, use_ggdist = FALSE)

  # X axis
  myplot <- myplot + ggplot2::scale_x_continuous(
    breaks = gdata$x_value + gdata$nudge,
    labels = gdata$x_label
  )

  myplot <- myplot + ggplot2::coord_cartesian(
    xlim = c(min(gdata$x_value)-0.5, max(gdata$x_value)+0.5)
  )


  # Labels
  ylab <- glue::glue("Proportion and {glue::glue_collapse(conf_level*100, sep = '%, ')}% Confidence Interval")

  xlab <- gdata$outcome_variable_name[[1]]
  myplot <- myplot + ggplot2::xlab(xlab) + ggplot2::ylab(ylab)

  myplot <- myplot + ggplot2::theme(
    axis.text.y = ggtext::element_markdown(),
    axis.title.y = ggtext::element_markdown(),
    axis.text.x = ggtext::element_markdown(),
    axis.title.x = ggtext::element_markdown()
  )

  # Limits
  ylow <- min(0, rope[[1]], na.rm = TRUE)
  yhigh <- max(1, rope[[2]], na.rm = TRUE)
  myplot <- myplot + ylim(c(ylow, yhigh))

  return(myplot)

}



esci_trans_r_to_z <- function(r) {
  return ( log((1 + r)/(1 - r))/2 )
}

esci_trans_rse_to_sez <- function(n) {
  return(sqrt(1/((n - 3))))
}

esci_trans_z_to_r <- function(x) {
  return ( (exp(2*x) - 1)/(exp(2*x) + 1) )
}

esci_trans_P <- function(x) {
  x[which(x < 0)] <- 0
  x[which(x > 1)] <- 1
  return(x)
}

esci_trans_identity <- function(x) {
  return(x)
}

rtrans <- function(x) {
  return ( log((1 + x)/(1 - x))/2 )
}

rback <- function(x) {
  return ( (exp(2*x) - 1)/(exp(2*x) + 1) )
}


# dist_P <- function(mu = 0, sigma = 1, f, n){
#   mu <- vctrs::vec_cast(mu, double())
#   sigma <- vec_cast::vec_cast(sigma, double())
#   if(any(sigma[!is.na(sigma)] < 0)){
#     abort("Standard deviation of a normal distribution must be non-negative")
#   }
#   distributional::new_dist(mu = mu, sigma = sigma, f = f, n = n, class = "dist_normal")
# }