#' Missing Data

#'

#' @description `r lifecycle::badge("stable")`

#'

#' Substitute missing data with a string or factor level.

#'

#' @param x (`factor` or `character` vector)\cr values for which any missing values should be substituted.

#' @param label (`character`)\cr string that missing data should be replaced with.

#'

#' @return `x` with any `NA` values substituted by `label`.

#'

#' @examples

#' explicit_na(c(NA, "a", "b"))

#' is.na(explicit_na(c(NA, "a", "b")))

#'

#' explicit_na(factor(c(NA, "a", "b")))

#' is.na(explicit_na(factor(c(NA, "a", "b"))))

#'

#' explicit_na(sas_na(c("a", "")))

#'

#' @export

explicit_na <- function(x, label = "<Missing>") {

  } else {

  }

}

#' Convert Strings to `NA`

#'

#' @description `r lifecycle::badge("stable")`

#'

#' SAS imports missing data as empty strings or strings with whitespaces only. This helper function can be used to

#' convert these values to `NA`s.

#'

#' @inheritParams explicit_na

#' @param empty (`logical`)\cr if `TRUE` empty strings get replaced by `NA`.

#' @param whitespaces (`logical`)\cr if `TRUE` then strings made from whitespaces only get replaced with `NA`.

#'

#' @return `x` with `""` and/or whitespace-only values substituted by `NA`, depending on the values of

#'   `empty` and `whitespaces`.

#'

#' @examples

#' sas_na(c("1", "", " ", "   ", "b"))

#' sas_na(factor(c("", " ", "b")))

#'

#' is.na(sas_na(c("1", "", " ", "   ", "b")))

#'

#' @export

sas_na <- function(x, empty = TRUE, whitespaces = TRUE) {

  } else {

  }

}

#' Cox Proportional Hazards Regression

#'

#' @description `r lifecycle::badge("stable")`

#'

#' Fits a Cox regression model and estimates hazard ratio to describe the effect size in a survival analysis.

#'

#' @inheritParams argument_convention

#'

#' @details Cox models are the most commonly used methods to estimate the magnitude of

#'   the effect in survival analysis. It assumes proportional hazards: the ratio

#'   of the hazards between groups (e.g., two arms) is constant over time.

#'   This ratio is referred to as the "hazard ratio" (HR) and is one of the

#'   most commonly reported metrics to describe the effect size in survival

#'   analysis (NEST Team, 2020).

#'

#' @seealso [fit_coxreg] for relevant fitting functions, [h_cox_regression] for relevant

#'   helper functions, and [tidy_coxreg] for custom tidy methods.

#'

#' @examples

#' library(survival)

#'

#' # Testing dataset [survival::bladder].

#' set.seed(1, kind = "Mersenne-Twister")

#' dta_bladder <- with(

#'   data = bladder[bladder$enum < 5, ],

#'   tibble::tibble(

#'     TIME = stop,

#'     STATUS = event,

#'     ARM = as.factor(rx),

#'     COVAR1 = as.factor(enum) %>% formatters::with_label("A Covariate Label"),

#'     COVAR2 = factor(

#'       sample(as.factor(enum)),

#'       levels = 1:4, labels = c("F", "F", "M", "M")

#'     ) %>% formatters::with_label("Sex (F/M)")

#'   )

#' )

#' dta_bladder$AGE <- sample(20:60, size = nrow(dta_bladder), replace = TRUE)

#' dta_bladder$STUDYID <- factor("X")

#'

#' plot(

#'   survfit(Surv(TIME, STATUS) ~ ARM + COVAR1, data = dta_bladder),

#'   lty = 2:4,

#'   xlab = "Months",

#'   col = c("blue1", "blue2", "blue3", "blue4", "red1", "red2", "red3", "red4")

#' )

#'

#' @name cox_regression

NULL

#' @describeIn cox_regression Statistics function that transforms results tabulated

#'   from [fit_coxreg_univar()] or [fit_coxreg_multivar()] into a list.

#'

#' @param model_df (`data.frame`)\cr contains the resulting model fit from a [fit_coxreg]

#'   function with tidying applied via [broom::tidy()].

#' @param .stats (`character`)\cr the name of statistics to be reported among:

#'   * `n`: number of observations (univariate only)

#'   * `hr`: hazard ratio

#'   * `ci`: confidence interval

#'   * `pval`: p-value of the treatment effect

#'   * `pval_inter`: p-value of the interaction effect between the treatment and the covariate (univariate only)

#' @param .which_vars (`character`)\cr which rows should statistics be returned for from the given model.

#'   Defaults to "all". Other options include "var_main" for main effects, "inter" for interaction effects,

#'   and "multi_lvl" for multivariate model covariate level rows. When `.which_vars` is "all" specific

#'   variables can be selected by specifying `.var_nms`.

#' @param .var_nms (`character`)\cr the `term` value of rows in `df` for which `.stats` should be returned. Typically

#'   this is the name of a variable. If using variable labels, `var` should be a vector of both the desired

#'   variable name and the variable label in that order to see all `.stats` related to that variable. When `.which_vars`

#'   is "var_main" `.var_nms` should be only the variable name.

#'

#' @return

#' * `s_coxreg()` returns the selected statistic for from the Cox regression model for the selected variable(s).

#'

#' @examples

#' # s_coxreg

#'

#' # Univariate

#' u1_variables <- list(

#'   time = "TIME", event = "STATUS", arm = "ARM", covariates = c("COVAR1", "COVAR2")

#' )

#' univar_model <- fit_coxreg_univar(variables = u1_variables, data = dta_bladder)

#' df1 <- broom::tidy(univar_model)

#' s_coxreg(model_df = df1, .stats = "hr")

#'

#' # Univariate with interactions

#' univar_model_inter <- fit_coxreg_univar(

#'   variables = u1_variables, control = control_coxreg(interaction = TRUE), data = dta_bladder

#' )

#' df1_inter <- broom::tidy(univar_model_inter)

#' s_coxreg(model_df = df1_inter, .stats = "hr", .which_vars = "inter", .var_nms = "COVAR1")

#'

#' # Univariate without treatment arm - only "COVAR2" covariate effects

#' u2_variables <- list(time = "TIME", event = "STATUS", covariates = c("COVAR1", "COVAR2"))

#' univar_covs_model <- fit_coxreg_univar(variables = u2_variables, data = dta_bladder)

#' df1_covs <- broom::tidy(univar_covs_model)

#' s_coxreg(model_df = df1_covs, .stats = "hr", .var_nms = c("COVAR2", "Sex (F/M)"))

#'

#' # Multivariate.

#' m1_variables <- list(

#'   time = "TIME", event = "STATUS", arm = "ARM", covariates = c("COVAR1", "COVAR2")

#' )

#' multivar_model <- fit_coxreg_multivar(variables = m1_variables, data = dta_bladder)

#' df2 <- broom::tidy(multivar_model)

#' s_coxreg(model_df = df2, .stats = "pval", .which_vars = "var_main", .var_nms = "COVAR1")

#' s_coxreg(

#'   model_df = df2, .stats = "pval", .which_vars = "multi_lvl",

#'   .var_nms = c("COVAR1", "A Covariate Label")

#' )

#'

#' # Multivariate without treatment arm - only "COVAR1" main effect

#' m2_variables <- list(time = "TIME", event = "STATUS", covariates = c("COVAR1", "COVAR2"))

#' multivar_covs_model <- fit_coxreg_multivar(variables = m2_variables, data = dta_bladder)

#' df2_covs <- broom::tidy(multivar_covs_model)

#' s_coxreg(model_df = df2_covs, .stats = "hr")

#'

#' @export

s_coxreg <- function(model_df, .stats, .which_vars = "all", .var_nms = NULL) {

  # We need a list with names corresponding to the stats to display of equal length to the list of stats.

  }

    }

  )

}

#' @describeIn cox_regression Analysis function which is used as `afun` in [rtables::analyze()]

#'   and `cfun` in [rtables::summarize_row_groups()] within `summarize_coxreg()`.

#'

#' @param eff (`flag`)\cr whether treatment effect should be calculated. Defaults to `FALSE`.

#' @param var_main (`flag`)\cr whether main effects should be calculated. Defaults to `FALSE`.

#' @param na_level (`string`)\cr custom string to replace all `NA` values with. Defaults to `""`.

#' @param cache_env (`environment`)\cr an environment object used to cache the regression model in order to

#'   avoid repeatedly fitting the same model for every row in the table. Defaults to `NULL` (no caching).

#'

#' @return

#' * `a_coxreg()` returns formatted [rtables::CellValue()].

#'

#' @examples

#' tern:::a_coxreg(

#'   df = dta_bladder,

#'   labelstr = "Label 1",

#'   variables = u1_variables,

#'   .spl_context = list(value = "COVAR1"),

#'   .stats = "n",

#'   .formats = "xx"

#' )

#'

#' tern:::a_coxreg(

#'   df = dta_bladder,

#'   labelstr = "",

#'   variables = u1_variables,

#'   .spl_context = list(value = "COVAR2"),

#'   .stats = "pval",

#'   .formats = "xx.xxxx"

#' )

#'

#' @keywords internal

a_coxreg <- function(df,

                     labelstr,

                     eff = FALSE,

                     var_main = FALSE,

                     multivar = FALSE,

                     variables,

                     at = list(),

                     control = control_coxreg(),

                     .spl_context,

                     .stats,

                     .formats,

                     na_level = "",

                     cache_env = NULL) {

  } else {

  }

    } else {

    }

  } else {

  }

  }

    } else { # treatment effect

    }

  } else {

    }

  }

  } else {

  }

  )

}

#' @describeIn cox_regression Layout-creating function which creates a Cox regression summary table

#'   layout. This function is a wrapper for several `rtables` layouting functions. This function

#'   is a wrapper for [rtables::analyze_colvars()] and [rtables::summarize_row_groups()].

#'

#' @inheritParams fit_coxreg_univar

#' @param multivar (`flag`)\cr Defaults to `FALSE`. If `TRUE` multivariate Cox regression will run, otherwise

#'   univariate Cox regression will run.

#' @param common_var (`character`)\cr the name of a factor variable in the dataset which takes the same value

#'   for all rows. This should be created during pre-processing if no such variable currently exists.

#' @param .section_div (`character`)\cr string which should be repeated as a section divider between sections.

#'   Defaults to `NA` for no section divider. If a vector of two strings are given, the first will be used between

#'   treatment and covariate sections and the second between different covariates.

#'

#' @return

#' * `summarize_coxreg()` returns a layout object suitable for passing to further layouting functions,

#'   or to [rtables::build_table()]. Adding this function to an `rtable` layout will add a Cox regression table

#'   containing the chosen statistics to the table layout.

#'

#' @seealso [fit_coxreg_univar()] and [fit_coxreg_multivar()] which also take the `variables`, `data`,

#'   `at` (univariate only), and `control` arguments but return unformatted univariate and multivariate

#'   Cox regression models, respectively.

#'

#' @examples

#' # summarize_coxreg

#'

#' result_univar <- basic_table() %>%

#'   summarize_coxreg(variables = u1_variables) %>%

#'   build_table(dta_bladder)

#' result_univar

#'

#' result_multivar <- basic_table() %>%

#'   summarize_coxreg(

#'     variables = m1_variables,

#'     multivar = TRUE,

#'   ) %>%

#'   build_table(dta_bladder)

#' result_multivar

#'

#' result_univar_covs <- basic_table() %>%

#'   summarize_coxreg(

#'     variables = u2_variables,

#'   ) %>%

#'   build_table(dta_bladder)

#' result_univar_covs

#'

#' result_multivar_covs <- basic_table() %>%

#'   summarize_coxreg(

#'     variables = m2_variables,

#'     multivar = TRUE,

#'     varlabels = c("Covariate 1", "Covariate 2") # custom labels

#'   ) %>%

#'   build_table(dta_bladder)

#' result_multivar_covs

#'

#' @export

summarize_coxreg <- function(lyt,

                             variables,

                             control = control_coxreg(),

                             at = list(),

                             multivar = FALSE,

                             common_var = "STUDYID",

                             .stats = c("n", "hr", "ci", "pval", "pval_inter"),

                             .formats = c(

                               n = "xx", hr = "xx.xx", ci = "(xx.xx, xx.xx)",

                               pval = "x.xxxx | (<0.0001)", pval_inter = "x.xxxx | (<0.0001)"

                             ),

                             varlabels = NULL,

                             .indent_mods = NULL,

                             na_level = "",

                             .section_div = NA_character_) {

    ))

  }

  }

  } else {

  }

  )

      )

    )

      ) %>%

        )

      )

        )

    }

  }

      ) %>%

        )

      )

        )

    }

  }

}

#' Pairwise Formula Special Term

#'

#' @description `r lifecycle::badge("deprecated")`

#'

#' The special term `pairwise` indicate that the model should be fitted individually for

#' every tested level in comparison to the reference level.

#'

#' @param x the variable for which pairwise result is expected.

#'

#' @return Variable "paired".

#'

#' @details Let's `ARM` being a factor with level A, B, C; let's be B the reference level,

#'   a model calling the formula including `pairwise(ARM)` will result in two models:

#'   * A model including only levels A and B, and effect of A estimated in reference to B.

#'   * A model including only levels C and B, the effect of C estimated in reference to B.

#'

#' @export

pairwise <- function(x) {

}

#' Univariate Formula Special Term

#'

#' @description `r lifecycle::badge("stable")`

#'

#' The special term `univariate` indicate that the model should be fitted individually for

#' every variable included in univariate.

#'

#' @param x A vector of variable name separated by commas.

#'

#' @return When used within a model formula, produces univariate models for each variable provided.

#'

#' @details

#' If provided alongside with pairwise specification, the model

#' `y ~ ARM + univariate(SEX, AGE, RACE)` lead to the study and comparison of the models

#' + `y ~ ARM`

#' + `y ~ ARM + SEX`

#' + `y ~ ARM + AGE`

#' + `y ~ ARM + RACE`

#'

#' @export

univariate <- function(x) {

}

# Get the right-hand-term of a formula

rht <- function(x) {

}

#' Hazard Ratio Estimation in Interactions

#'

#' This function estimates the hazard ratios between arms when an interaction variable is given with

#' specific values.

#'

#' @param variable,given Names of two variable in interaction. We seek the estimation of the levels of `variable`

#'   given the levels of `given`.

#' @param lvl_var,lvl_given corresponding levels has given by `levels`.

#' @param mmat A name numeric filled with 0 used as template to obtain the design matrix.

#' @param coef Numeric of estimated coefficients.

#' @param vcov Variance-covariance matrix of underlying model.

#' @param conf_level Single numeric for the confidence level of estimate intervals.

#'

#' @details Given the cox regression investigating the effect of Arm (A, B, C; reference A)

#'   and Sex (F, M; reference Female). The model is abbreviated: y ~ Arm + Sex + Arm x Sex.

#'   The cox regression estimates the coefficients along with a variance-covariance matrix for:

#'

#'   - b1 (arm b), b2 (arm c)

#'   - b3 (sex m)

#'   - b4 (arm b: sex m), b5 (arm c: sex m)

#'

#'   Given that I want an estimation of the Hazard Ratio for arm C/sex M, the estimation

#'   will be given in reference to arm A/Sex M by exp(b2 + b3 + b5)/ exp(b3) = exp(b2 + b5),

#'   therefore the interaction coefficient is given by b2 + b5 while the standard error is obtained

#'   as $1.96 * sqrt(Var b2 + Var b5 + 2 * covariance (b2,b5))$ for a confidence level of 0.95.

#'

#' @return A list of matrix (one per level of variable) with rows corresponding to the combinations of

#'   `variable` and `given`, with columns:

#'   * `coef_hat`: Estimation of the coefficient.

#'   * `coef_se`: Standard error of the estimation.

#'   * `hr`: Hazard ratio.

#'   * `lcl, ucl`: Lower/upper confidence limit of the hazard ratio.

#'

#' @seealso [s_cox_multivariate()].

#'

#' @examples

#' library(dplyr)

#' library(survival)

#'

#' ADSL <- tern_ex_adsl %>%

#'   filter(SEX %in% c("F", "M"))

#'

#' adtte <- tern_ex_adtte %>% filter(PARAMCD == "PFS")

#' adtte$ARMCD <- droplevels(adtte$ARMCD)

#' adtte$SEX <- droplevels(adtte$SEX)

#'

#' mod <- coxph(

#'   formula = Surv(time = AVAL, event = 1 - CNSR) ~ (SEX + ARMCD)^2,

#'   data = adtte

#' )

#'

#' mmat <- stats::model.matrix(mod)[1, ]

#' mmat[!mmat == 0] <- 0

#'

#' # Internal function - estimate_coef

#' \dontrun{

#' estimate_coef(

#'   variable = "ARMCD", given = "SEX", lvl_var = "ARM A", lvl_given = "M",

#'   coef = stats::coef(mod), mmat = mmat, vcov = stats::vcov(mod), conf_level = .95

#' )

#' }

#'

#' @keywords internal

estimate_coef <- function(variable, given,

                          lvl_var, lvl_given,

                          coef,

                          mmat,

                          vcov,

                          conf_level = 0.95) {

    }

  )

    }

  )

  })

  })

  )

}

#' `tryCatch` around `car::Anova`

#'

#' Captures warnings when executing [car::Anova].

#'

#' @inheritParams car::Anova

#'

#' @return A list with item `aov` for the result of the model and `error_text` for the captured warnings.

#'

#' @examples

#' # `car::Anova` on cox regression model including strata and expected

#' # a likelihood ratio test triggers a warning as only Wald method is

#' # accepted.

#'

#' library(survival)

#'

#' mod <- coxph(

#'   formula = Surv(time = futime, event = fustat) ~ factor(rx) + strata(ecog.ps),

#'   data = ovarian

#' )

#'

#' # Internal function - try_car_anova

#' \dontrun{

#' with_wald <- try_car_anova(mod = mod, test.statistic = "Wald")

#' with_lr <- try_car_anova(mod = mod, test.statistic = "LR")

#' }

#'

#' @keywords internal

try_car_anova <- function(mod,

                          test.statistic) { # nolint

          ),

        )

      },

        # If a warning is detected it is handled as "w".

        # A warning is sometimes expected, then, we want to restart

        # the execution while ignoring the warning.

      }

    ),

    }

  )

}

#' Fit the Cox Regression Model and Anova

#'

#' The functions allows to derive from the [survival::coxph()] results the effect p.values using [car::Anova()].

#' This last package introduces more flexibility to get the effect p.values.

#'

#' @inheritParams t_coxreg

#'

#' @return A list with items `mod` (results of [survival::coxph()]), `msum` (result of `summary`) and

#'   `aov` (result of [car::Anova()]).

#'

#' @noRd

fit_n_aov <- function(formula,

                      data = data,

                      conf_level = conf_level,

                      pval_method = c("wald", "likelihood"),

                      ...) {

    # We expect some warnings due to coxph which fails strict programming.

  })

    )

  )

}

# argument_checks

check_formula <- function(formula) {

  }

}

check_covariate_formulas <- function(covariates) {

  }

}

name_covariate_names <- function(covariates) {

}

check_increments <- function(increments, covariates) {

            )

          )

        }

      }

    )

  }

}

#' Multivariate Cox Model - Summarized Results

#'

#' Analyses based on multivariate Cox model are usually not performed for the Controlled Substance Reporting or

#' regulatory documents but serve exploratory purposes only (e.g., for publication). In practice, the model usually

#' includes only the main effects (without interaction terms). It produces the hazard ratio estimates for each of the

#' covariates included in the model.

#' The analysis follows the same principles (e.g., stratified vs. unstratified analysis and tie handling) as the

#' usual Cox model analysis. Since there is usually no pre-specified hypothesis testing for such analysis,

#' the p.values need to be interpreted with caution. (**Statistical Analysis of Clinical Trials Data with R**,

#' `NEST's bookdown`)

#'

#' @param formula (`formula`)\cr A formula corresponding to the investigated [survival::Surv()] survival model

#'   including covariates.

#' @param data (`data.frame`)\cr A data frame which includes the variable in formula and covariates.

#' @param conf_level (`proportion`)\cr The confidence level for the hazard ratio interval estimations. Default is 0.95.

#' @param pval_method (`character`)\cr The method used for the estimation of p-values, should be one of

#'   "wald" (default) or "likelihood".

#' @param ... Optional parameters passed to [survival::coxph()]. Can include `ties`, a character string specifying the

#'   method for tie handling, one of `exact` (default), `efron`, `breslow`.

#'

#' @return A `list` with elements `mod`, `msum`, `aov`, and `coef_inter`.

#'

#' @details The output is limited to single effect terms. Work in ongoing for estimation of interaction terms

#'   but is out of scope as defined by the  Global Data Standards Repository

#'   (**`GDS_Standard_TLG_Specs_Tables_2.doc`**).

#'

#' @seealso [estimate_coef()].

#'

#' @examples

#' library(dplyr)

#'

#' adtte <- tern_ex_adtte

#' adtte_f <- subset(adtte, PARAMCD == "OS") # _f: filtered

#' adtte_f <- filter(

#'   adtte_f,

#'   PARAMCD == "OS" &

#'     SEX %in% c("F", "M") &

#'     RACE %in% c("ASIAN", "BLACK OR AFRICAN AMERICAN", "WHITE")

#' )

#' adtte_f$SEX <- droplevels(adtte_f$SEX)

#' adtte_f$RACE <- droplevels(adtte_f$RACE)

#'

#' # Internal function - s_cox_multivariate

#' \dontrun{

#' s_cox_multivariate(

#'   formula = Surv(time = AVAL, event = 1 - CNSR) ~ (ARMCD + RACE + AGE)^2, data = adtte_f

#' )

#' }

#'

#' @keywords internal

s_cox_multivariate <- function(formula, data,

                               conf_level = 0.95,

                               pval_method = c("wald", "likelihood"),

                               ...) {

      }

    }

  )

  # Results directly exported from environment(fit_n_aov) to environment(s_function_draft)

    ...

  )

      )

    }

      }

    )

  }

}

#' Confidence Intervals for a Difference of Binomials

#'

#' @description `r lifecycle::badge("experimental")`

#'

#' Several confidence intervals for the difference between proportions.

#'

#' @param grp (`factor`)\cr vector assigning observations to one out of two groups

#'   (e.g. reference and treatment group).

#'

#' @name desctools_binom

NULL

#' Recycle List of Parameters

#'

#' This function recycles all supplied elements to the maximal dimension.

#'

#' @param ... (`any`)\cr Elements to recycle.

#'

#' @return A `list`.

#'

#' @keywords internal

#' @noRd

h_recycle <- function(...) {

}

#' @describeIn desctools_binom Several confidence intervals for the difference between proportions.

#'

#' @return A `matrix` of 3 values:

#'   * `est`: estimate of proportion difference.

#'   * `lwr.ci`: estimate of lower end of the confidence interval.

#'   * `upr.ci`: estimate of upper end of the confidence interval.

#'

#' @examples

#' # Internal function - desctools_binom

#' \dontrun{

#' set.seed(2)

#' rsp <- sample(c(TRUE, FALSE), replace = TRUE, size = 20)

#' grp <- factor(c(rep("A", 10), rep("B", 10)))

#' tbl <- table(grp, factor(rsp, levels = c(TRUE, FALSE)))

#' desctools_binom(

#'   tbl[1], sum(tbl[1], tbl[3]), tbl[2], sum(tbl[2], tbl[4]),

#'   conf.level = 0.90, method = "waldcc"

#' )

#' }

#'

#' @keywords internal

desctools_binom <- function(x1, n1, x2, n2, conf.level = 0.95, sides = c( # nolint

                              "two.sided",

                              "left", "right"

                            ), method = c(

                              "ac", "wald", "waldcc", "score",

                              "scorecc", "mn", "mee", "blj", "ha", "hal", "jp"

                            )) {

  }

  }

    }

      },

      },

      },

      },

        )

        )

      },

        )

        )

      },

          } else {

          }

        }

          )

        }

      },

      },

      },

            }

            } else {

            }

          }

        }

          } else {

          }

        }

      },

      },

      },

      },

    )

    )

    }

  }

  )

    )

  }))

  } else {

  } else {

  } else {

  } else {

}

#' @describeIn desctools_binom Compute confidence intervals for binomial proportions.

#'

#' @param x (`count`)\cr number of successes

#' @param n (`count`)\cr number of trials

#' @param conf.level (`proportion`)\cr confidence level, defaults to 0.95.

#' @param sides (`character`)\cr side of the confidence interval to compute. Must be one of "two-sided" (default),

#'   "left", or "right".

#' @param method (`character`)\cr method to use. Can be one out of: "wald", "wilson", "wilsoncc", "agresti-coull",

#'   "jeffreys", "modified wilson", "modified jeffreys", "clopper-pearson", "arcsine", "logit", "witting", "pratt",

#'   "midp", "lik", and "blaker".

#'

#' @return A `matrix` with 3 columns containing:

#'   * `est`: estimate of proportion difference.

#'   * `lwr.ci`: lower end of the confidence interval.

#'   * `upr.ci`: upper end of the confidence interval.

#'

#' @keywords internal

desctools_binomci <- function(x,

                              n,

                              conf.level = 0.95, # nolint

                              sides = c("two.sided", "left", "right"),

                              method = c(

                                "wilson", "wald", "waldcc", "agresti-coull",

                                "jeffreys", "modified wilson", "wilsoncc", "modified jeffreys",

                                "clopper-pearson", "arcsine", "logit", "witting", "pratt",

                                "midp", "lik", "blaker"

                              ),

                              rand = 123,

                              tol = 1e-05) {

  }

  }

    }

    }

    }

    }

    }

    )),

    },

    },

    },

    },

    },

      } else {

        )

      }

      } else {

      }

    },

      } else {

      }

      } else {

      }

    },

      } else {

        } else {

          )

        }

      }

      } else {

        } else {

        }

      }

    },

    },

    },

    },