diff --git a/NAMESPACE b/NAMESPACE
index 0151ef477..125750df7 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -112,7 +112,6 @@ S3method(ci,sim.merMod)
 S3method(ci,slopes)
 S3method(ci,stanfit)
 S3method(ci,stanreg)
-S3method(cwi,data.frame)
 S3method(describe_posterior,BFBayesFactor)
 S3method(describe_posterior,BGGM)
 S3method(describe_posterior,MCMCglmm)
@@ -642,7 +641,6 @@ export(contr.orthonorm)
 export(convert_bayesian_as_frequentist)
 export(convert_p_to_pd)
 export(convert_pd_to_p)
-export(cwi)
 export(density_at)
 export(describe_posterior)
 export(describe_prior)
diff --git a/R/bic_to_bf.R b/R/bic_to_bf.R
index 306b5e750..f8e6ae832 100644
--- a/R/bic_to_bf.R
+++ b/R/bic_to_bf.R
@@ -27,8 +27,8 @@ bic_to_bf <- function(bic, denominator, log = FALSE) {
   delta <- (denominator - bic) / 2
 
   if (log) {
-    return(delta)
+    delta
   } else {
-    return(exp(delta))
+    exp(delta)
   }
 }
diff --git a/R/describe_prior.R b/R/describe_prior.R
index 7f94a68ea..54b140c83 100644
--- a/R/describe_prior.R
+++ b/R/describe_prior.R
@@ -63,7 +63,7 @@ describe_prior.brmsfit <- function(model,
 
   # If the prior scale has been adjusted, it is the actual scale that was used.
   if ("Prior_Adjusted_Scale" %in% names(priors)) {
-    priors$Prior_Scale[!is.na(priors$Prior_Adjusted_Scale)] <- priors$Prior_Adjusted_Scale[!is.na(priors$Prior_Adjusted_Scale)]
+    priors$Prior_Scale[!is.na(priors$Prior_Adjusted_Scale)] <- priors$Prior_Adjusted_Scale[!is.na(priors$Prior_Adjusted_Scale)] # nolint
     priors$Prior_Adjusted_Scale <- NULL
   }
 
@@ -85,7 +85,7 @@ describe_prior.brmsfit <- function(model,
     colnames(priors)[1] <- "Cleaned_Parameter"
     out <- merge(cp, priors, by = "Cleaned_Parameter", all = TRUE)
     out <- out[!duplicated(out$Parameter), ]
-    priors <- out[intersect(colnames(out), c("Parameter", "Prior_Distribution", "Prior_df", "Prior_Location", "Prior_Scale", "Response"))]
+    priors <- out[intersect(colnames(out), c("Parameter", "Prior_Distribution", "Prior_df", "Prior_Location", "Prior_Scale", "Response"))] # nolint
   }
 
   priors
diff --git a/R/diagnostic_draws.R b/R/diagnostic_draws.R
index f1b1ddc48..30858f453 100644
--- a/R/diagnostic_draws.R
+++ b/R/diagnostic_draws.R
@@ -27,17 +27,26 @@ diagnostic_draws <- function(posterior, ...) {
 diagnostic_draws.brmsfit <- function(posterior, ...) {
   insight::check_if_installed("brms")
 
-  data <- brms::nuts_params(posterior)
-  data$idvar <- paste0(data$Chain, "_", data$Iteration)
+  nuts_parameters <- brms::nuts_params(posterior)
+  nuts_parameters$idvar <- paste0(
+    nuts_parameters$Chain,
+    "_",
+    nuts_parameters$Iteration
+  )
   out <- stats::reshape(
-    data,
+    nuts_parameters,
     v.names = "Value",
     idvar = "idvar",
     timevar = "Parameter",
     direction = "wide"
   )
   out$idvar <- NULL
-  out <- merge(out, brms::log_posterior(posterior), by = c("Chain", "Iteration"), sort = FALSE)
+  out <- merge(
+    out,
+    brms::log_posterior(posterior),
+    by = c("Chain", "Iteration"),
+    sort = FALSE
+  )
 
   # Rename
   names(out)[names(out) == "Value.accept_stat__"] <- "Acceptance_Rate"
diff --git a/R/distribution.R b/R/distribution.R
index 97c347ddd..d89e05548 100644
--- a/R/distribution.R
+++ b/R/distribution.R
@@ -33,21 +33,21 @@ distribution <- function(type = "normal", ...) {
   )
 
   switch(match.arg(arg = type, choices = basr_r_distributions),
-    "beta" = distribution_beta(...),
-    "binom" = ,
-    "binomial" = distribution_binomial(...),
-    "cauchy" = distribution_cauchy(...),
-    "chisq" = ,
-    "chisquared" = distribution_chisquared(...),
-    "gamma" = distribution_gamma(...),
-    "gaussian" = ,
-    "normal" = distribution_normal(...),
-    "nbinom" = distribution_nbinom(...),
-    "poisson" = distribution_poisson(...),
-    "t" = ,
-    "student" = ,
-    "student_t" = distribution_student(...),
-    "uniform" = distribution_uniform(...),
+    beta = distribution_beta(...),
+    binom = ,
+    binomial = distribution_binomial(...),
+    cauchy = distribution_cauchy(...),
+    chisq = ,
+    chisquared = distribution_chisquared(...),
+    gamma = distribution_gamma(...),
+    gaussian = ,
+    normal = distribution_normal(...),
+    nbinom = distribution_nbinom(...),
+    poisson = distribution_poisson(...),
+    t = ,
+    student = ,
+    student_t = distribution_student(...),
+    uniform = distribution_uniform(...),
     distribution_custom(type = type, ...)
   )
 }
@@ -148,7 +148,7 @@ distribution_mixture_normal <- function(n, mean = c(-3, 3), sd = 1, random = FAL
   n <- round(n / length(mean))
   sd <- sd
   if (length(sd) != length(mean)) {
-    sd <- rep(sd, length.out = length(mean))
+    sd <- rep_len(sd, length(mean))
   }
 
 
diff --git a/R/effective_sample.R b/R/effective_sample.R
index fa81bd5d5..0b5c6df7c 100644
--- a/R/effective_sample.R
+++ b/R/effective_sample.R
@@ -16,8 +16,10 @@
 #'   information there is in autocorrelated chains} (*Kruschke 2015, p182-3*).
 #'
 #' @references
-#'   - Kruschke, J. (2014). Doing Bayesian data analysis: A tutorial with R, JAGS, and Stan. Academic Press.
-#'   - Bürkner, P. C. (2017). brms: An R package for Bayesian multilevel models using Stan. Journal of Statistical Software, 80(1), 1-28
+#' - Kruschke, J. (2014). Doing Bayesian data analysis: A tutorial with R, JAGS,
+#'   and Stan. Academic Press.
+#' - Bürkner, P. C. (2017). brms: An R package for Bayesian multilevel models
+#'   using Stan. Journal of Statistical Software, 80(1), 1-28
 #'
 #' @examplesIf require("rstanarm")
 #' \donttest{
@@ -82,7 +84,7 @@ effective_sample.brmsfit <- function(model,
 #' @export
 effective_sample.stanreg <- function(model,
                                      effects = c("fixed", "random", "all"),
-                                     component = c("location", "all", "conditional", "smooth_terms", "sigma", "distributional", "auxiliary"),
+                                     component = c("location", "all", "conditional", "smooth_terms", "sigma", "distributional", "auxiliary"), # nolint
                                      parameters = NULL,
                                      ...) {
   # check arguments
@@ -112,7 +114,7 @@ effective_sample.stanreg <- function(model,
 #' @export
 effective_sample.stanmvreg <- function(model,
                                        effects = c("fixed", "random", "all"),
-                                       component = c("location", "all", "conditional", "smooth_terms", "sigma", "distributional", "auxiliary"),
+                                       component = c("location", "all", "conditional", "smooth_terms", "sigma", "distributional", "auxiliary"), # nolint
                                        parameters = NULL,
                                        ...) {
   # check arguments
diff --git a/R/hdi.R b/R/hdi.R
index 0662a2c52..fa31b23bd 100644
--- a/R/hdi.R
+++ b/R/hdi.R
@@ -82,7 +82,8 @@
 #' @inherit ci return
 #'
 #' @family ci
-#' @seealso Other interval functions, such as [hdi()], [eti()], [bci()], [spi()], [si()], [cwi()].
+#' @seealso Other interval functions, such as [`hdi()`], [`eti()`], [`bci()`],
+#' [`spi()`], [`si()`].
 #'
 #' @examplesIf require("rstanarm") && require("brms") && require("emmeans") && require("BayesFactor")
 #' library(bayestestR)
diff --git a/R/model_to_priors.R b/R/model_to_priors.R
index 29634e547..20dfb9d1a 100644
--- a/R/model_to_priors.R
+++ b/R/model_to_priors.R
@@ -35,12 +35,12 @@ model_to_priors.brmsfit <- function(model, scale_multiply = 3, ...) {
 
   for (p in priors_params$Parameter) {
     if (p %in% params$Parameter) {
-      subset <- params[params$Parameter == p, ]
+      param_subset <- params[params$Parameter == p, ]
       priors$prior[priors_params$Parameter == p] <- paste0(
         "normal(",
-        insight::format_value(subset$Mean),
+        insight::format_value(param_subset$Mean),
         ", ",
-        insight::format_value(subset$SD * scale_multiply),
+        insight::format_value(param_subset$SD * scale_multiply),
         ")"
       )
     }
diff --git a/R/overlap.R b/R/overlap.R
index 36faf6899..8fdb29737 100644
--- a/R/overlap.R
+++ b/R/overlap.R
@@ -1,6 +1,8 @@
 #' Overlap Coefficient
 #'
-#' A method to calculate the overlap coefficient between two empirical distributions (that can be used as a measure of similarity between two samples).
+#' A method to calculate the overlap coefficient between two empirical
+#' distributions (that can be used as a measure of similarity between two
+#' samples).
 #'
 #' @param x Vector of x values.
 #' @param y Vector of x values.
@@ -17,32 +19,56 @@
 #' overlap(x, y)
 #' plot(overlap(x, y))
 #' @export
-overlap <- function(x, y, method_density = "kernel", method_auc = "trapezoid", precision = 2^10, extend = TRUE, extend_scale = 0.1, ...) {
+overlap <- function(x,
+                    y,
+                    method_density = "kernel",
+                    method_auc = "trapezoid",
+                    precision = 2^10,
+                    extend = TRUE,
+                    extend_scale = 0.1,
+                    ...) {
   # Generate densities
-  dx <- estimate_density(x, method = method_density, precision = precision, extend = extend, extend_scale = extend_scale, ...)
-  dy <- estimate_density(y, method = method_density, precision = precision, extend = extend, extend_scale = extend_scale, ...)
+  dx <- estimate_density(
+    x,
+    method = method_density,
+    precision = precision,
+    extend = extend,
+    extend_scale = extend_scale,
+    ...
+  )
+  dy <- estimate_density(
+    y,
+    method = method_density,
+    precision = precision,
+    extend = extend,
+    extend_scale = extend_scale,
+    ...
+  )
 
   # Create density estimation functions
   fx <- stats::approxfun(dx$x, dx$y, method = "linear", rule = 2)
   fy <- stats::approxfun(dy$x, dy$y, method = "linear", rule = 2)
 
   x_axis <- seq(min(c(dx$x, dy$x)), max(c(dx$x, dy$x)), length.out = precision)
-  data <- data.frame(x = x_axis, y1 = fx(x_axis), y2 = fy(x_axis))
-
+  approx_data <- data.frame(x = x_axis, y1 = fx(x_axis), y2 = fy(x_axis))
 
 
   # calculate intersection densities
-  data$intersection <- pmin(data$y1, data$y2)
-  data$exclusion <- pmax(data$y1, data$y2)
+  approx_data$intersection <- pmin(approx_data$y1, approx_data$y2)
+  approx_data$exclusion <- pmax(approx_data$y1, approx_data$y2)
 
   # integrate areas under curves
-  area_intersection <- area_under_curve(data$x, data$intersection, method = method_auc)
+  area_intersection <- area_under_curve(
+    approx_data$x,
+    approx_data$intersection,
+    method = method_auc
+  )
   # area_exclusion <- area_under_curve(data$x, data$exclusion, method = method_auc)
 
 
   # compute overlap coefficient
   overlap <- area_intersection
-  attr(overlap, "data") <- data
+  attr(overlap, "data") <- approx_data
 
   class(overlap) <- c("overlap", class(overlap))
   overlap
@@ -59,7 +85,7 @@ print.overlap <- function(x, ...) {
 #' @export
 plot.overlap <- function(x, ...) {
   # Can be improved through see
-  data <- attributes(x)$data
-  graphics::plot(data$x, data$exclusion, type = "l")
-  graphics::polygon(data$x, data$intersection, col = "red")
+  plot_data <- attributes(x)$data
+  graphics::plot(plot_data$x, plot_data$exclusion, type = "l")
+  graphics::polygon(plot_data$x, plot_data$intersection, col = "red")
 }
diff --git a/R/p_significance.R b/R/p_significance.R
index 12a692c1b..c38a3842a 100644
--- a/R/p_significance.R
+++ b/R/p_significance.R
@@ -285,7 +285,7 @@ p_significance.predictions <- p_significance.slopes
 p_significance.stanreg <- function(x,
                                    threshold = "default",
                                    effects = c("fixed", "random", "all"),
-                                   component = c("location", "all", "conditional", "smooth_terms", "sigma", "distributional", "auxiliary"),
+                                   component = c("location", "all", "conditional", "smooth_terms", "sigma", "distributional", "auxiliary"), # nolint
                                    parameters = NULL,
                                    verbose = TRUE,
                                    ...) {
diff --git a/R/cwi.R b/WIP/cwi.R
similarity index 100%
rename from R/cwi.R
rename to WIP/cwi.R
diff --git a/man/bayestestR-package.Rd b/man/bayestestR-package.Rd
index 3552e5bdb..c61f4e45c 100644
--- a/man/bayestestR-package.Rd
+++ b/man/bayestestR-package.Rd
@@ -33,15 +33,15 @@ Useful links:
 
 }
 \author{
-\strong{Maintainer}: Dominique Makowski \email{dom.makowski@gmail.com} (\href{https://orcid.org/0000-0001-5375-9967}{ORCID}) (@Dom_Makowski)
+\strong{Maintainer}: Dominique Makowski \email{dom.makowski@gmail.com} (\href{https://orcid.org/0000-0001-5375-9967}{ORCID})
 
 Authors:
 \itemize{
-  \item Daniel Lüdecke \email{d.luedecke@uke.de} (\href{https://orcid.org/0000-0002-8895-3206}{ORCID}) (@strengejacke)
-  \item Mattan S. Ben-Shachar \email{matanshm@post.bgu.ac.il} (\href{https://orcid.org/0000-0002-4287-4801}{ORCID}) (@mattansb)
-  \item Indrajeet Patil \email{patilindrajeet.science@gmail.com} (\href{https://orcid.org/0000-0003-1995-6531}{ORCID}) (@patilindrajeets)
+  \item Daniel Lüdecke \email{d.luedecke@uke.de} (\href{https://orcid.org/0000-0002-8895-3206}{ORCID})
+  \item Mattan S. Ben-Shachar \email{matanshm@post.bgu.ac.il} (\href{https://orcid.org/0000-0002-4287-4801}{ORCID})
+  \item Indrajeet Patil \email{patilindrajeet.science@gmail.com} (\href{https://orcid.org/0000-0003-1995-6531}{ORCID})
   \item Micah K. Wilson \email{micah.k.wilson@curtin.edu.au} (\href{https://orcid.org/0000-0003-4143-7308}{ORCID})
-  \item Brenton M. Wiernik \email{brenton@wiernik.org} (\href{https://orcid.org/0000-0001-9560-6336}{ORCID}) (@bmwiernik)
+  \item Brenton M. Wiernik \email{brenton@wiernik.org} (\href{https://orcid.org/0000-0001-9560-6336}{ORCID})
 }
 
 Other contributors:
diff --git a/man/bci.Rd b/man/bci.Rd
index 172d51655..3e2f66a42 100644
--- a/man/bci.Rd
+++ b/man/bci.Rd
@@ -174,7 +174,6 @@ Statistical Science. 11(3): 189–212. 10.1214/ss/1032280214
 \seealso{
 Other ci: 
 \code{\link{ci}()},
-\code{\link{cwi}()},
 \code{\link{eti}()},
 \code{\link{hdi}()},
 \code{\link{si}()},
diff --git a/man/ci.Rd b/man/ci.Rd
index 1ef0d7948..92c7a9ae4 100644
--- a/man/ci.Rd
+++ b/man/ci.Rd
@@ -155,7 +155,6 @@ intervals"? BMJ 2019;l5381. 10.1136/bmj.l5381
 \seealso{
 Other ci: 
 \code{\link{bci}()},
-\code{\link{cwi}()},
 \code{\link{eti}()},
 \code{\link{hdi}()},
 \code{\link{si}()},
diff --git a/man/cwi.Rd b/man/cwi.Rd
deleted file mode 100644
index 6b3db0ae0..000000000
--- a/man/cwi.Rd
+++ /dev/null
@@ -1,95 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/cwi.R
-\name{cwi}
-\alias{cwi}
-\alias{cwi.data.frame}
-\title{Curvewise Intervals (CWI)}
-\usage{
-cwi(x, ...)
-
-\method{cwi}{data.frame}(x, ci = 0.95, ...)
-}
-\arguments{
-\item{x}{Vector representing a posterior distribution, or a data frame of such
-vectors. Can also be a Bayesian model. \strong{bayestestR} supports a wide range
-of models (see, for example, \code{methods("hdi")}) and not all of those are
-documented in the 'Usage' section, because methods for other classes mostly
-resemble the arguments of the \code{.numeric} or \code{.data.frame}methods.}
-
-\item{...}{Currently not used.}
-
-\item{ci}{Value or vector of probability of the (credible) interval - CI
-(between 0 and 1) to be estimated. Default to \code{.95} (\verb{95\%}).}
-}
-\value{
-A data frame with following columns:
-\itemize{
-\item \code{Parameter} The model parameter(s), if \code{x} is a model-object. If \code{x} is a
-vector, this column is missing.
-\item \code{CI} The probability of the credible interval.
-\item \code{CI_low}, \code{CI_high} The lower and upper credible interval limits for the parameters.
-}
-}
-\description{
-Compute the \strong{Curvewise interval (CWI)} (also called the "simultaneous interval" or "joint interval") of posterior distributions using \code{ggdist::curve_interval()}.
-Whereas the more typical "pointwise intervals" contain xx\% of the posterior for a single parameter,
-joint/curvewise intervals contain xx\% of the posterior distribution for \strong{all} parameters.
-}
-\details{
-Applied model predictions, pointwise intervals contain xx\% of the predicted response values \strong{conditional} on specific predictor values.
-In contrast, curvewise intervals contain xx\% of the predicted response values across all predictor values.
-Put another way, curvewise intervals contain xx\% of the full \strong{prediction lines} from the model.
-
-For more details, see the \href{https://mjskay.github.io/ggdist/articles/lineribbon.html#curve-boxplots-aka-lineribbons-with-joint-intervals-or-curvewise-intervals-}{\emph{ggdist} documentation on curvewise intervals}.
-}
-\examples{
-\donttest{
-library(bayestestR)
-
-if (require("ggplot2") && require("rstanarm") && require("ggdist")) {
-  # Generate data =============================================
-  k <- 11 # number of curves (iterations)
-  n <- 201 # number of rows
-  data <- data.frame(x = seq(-15, 15, length.out = n))
-
-  # Simulate iterations as new columns
-  for (i in 1:k) {
-    data[paste0("iter_", i)] <- dnorm(data$x, seq(-5, 5, length.out = k)[i], 3)
-  }
-
-  # Note: first, we need to transpose the data to have iters as rows
-  iters <- datawizard::data_transpose(data[paste0("iter_", 1:k)])
-
-  # Compute Median
-  data$Median <- point_estimate(iters)[["Median"]]
-
-  # Compute Credible Intervals ================================
-
-  # Compute ETI (default type of CI)
-  data[c("ETI_low", "ETI_high")] <- eti(iters, ci = 0.5)[c("CI_low", "CI_high")]
-
-  # Compute CWI
-  # ggdist::curve_interval(reshape_iterations(data), iter_value .width = 0.5)
-
-  # Visualization =============================================
-  ggplot(data, aes(x = x, y = Median)) +
-    geom_ribbon(aes(ymin = ETI_low, ymax = ETI_high), fill = "red", alpha = 0.3) +
-    geom_line(linewidth = 1) +
-    geom_line(
-      data = reshape_iterations(data),
-      aes(y = iter_value, group = iter_group),
-      alpha = 0.3
-    )
-}
-}
-}
-\seealso{
-Other ci: 
-\code{\link{bci}()},
-\code{\link{ci}()},
-\code{\link{eti}()},
-\code{\link{hdi}()},
-\code{\link{si}()},
-\code{\link{spi}()}
-}
-\concept{ci}
diff --git a/man/effective_sample.Rd b/man/effective_sample.Rd
index 50c8aac01..7ca6d5745 100644
--- a/man/effective_sample.Rd
+++ b/man/effective_sample.Rd
@@ -70,7 +70,9 @@ effective_sample(model)
 }
 \references{
 \itemize{
-\item Kruschke, J. (2014). Doing Bayesian data analysis: A tutorial with R, JAGS, and Stan. Academic Press.
-\item Bürkner, P. C. (2017). brms: An R package for Bayesian multilevel models using Stan. Journal of Statistical Software, 80(1), 1-28
+\item Kruschke, J. (2014). Doing Bayesian data analysis: A tutorial with R, JAGS,
+and Stan. Academic Press.
+\item Bürkner, P. C. (2017). brms: An R package for Bayesian multilevel models
+using Stan. Journal of Statistical Software, 80(1), 1-28
 }
 }
diff --git a/man/eti.Rd b/man/eti.Rd
index 4b116beb7..b81a35a60 100644
--- a/man/eti.Rd
+++ b/man/eti.Rd
@@ -171,7 +171,6 @@ eti(bf, ci = c(0.80, 0.89, 0.95))
 Other ci: 
 \code{\link{bci}()},
 \code{\link{ci}()},
-\code{\link{cwi}()},
 \code{\link{hdi}()},
 \code{\link{si}()},
 \code{\link{spi}()}
diff --git a/man/hdi.Rd b/man/hdi.Rd
index 927d4f3da..62ffc2e67 100644
--- a/man/hdi.Rd
+++ b/man/hdi.Rd
@@ -177,12 +177,12 @@ examples in R and Stan. Chapman and Hall/CRC.
 }
 }
 \seealso{
-Other interval functions, such as \code{\link[=hdi]{hdi()}}, \code{\link[=eti]{eti()}}, \code{\link[=bci]{bci()}}, \code{\link[=spi]{spi()}}, \code{\link[=si]{si()}}, \code{\link[=cwi]{cwi()}}.
+Other interval functions, such as \code{\link[=hdi]{hdi()}}, \code{\link[=eti]{eti()}}, \code{\link[=bci]{bci()}},
+\code{\link[=spi]{spi()}}, \code{\link[=si]{si()}}.
 
 Other ci: 
 \code{\link{bci}()},
 \code{\link{ci}()},
-\code{\link{cwi}()},
 \code{\link{eti}()},
 \code{\link{si}()},
 \code{\link{spi}()}
diff --git a/man/overlap.Rd b/man/overlap.Rd
index d3a170c29..86448f776 100644
--- a/man/overlap.Rd
+++ b/man/overlap.Rd
@@ -34,7 +34,9 @@ means that the x axis will be extended by \code{1/10} of the range of the data.}
 \item{...}{Currently not used.}
 }
 \description{
-A method to calculate the overlap coefficient between two empirical distributions (that can be used as a measure of similarity between two samples).
+A method to calculate the overlap coefficient between two empirical
+distributions (that can be used as a measure of similarity between two
+samples).
 }
 \examples{
 library(bayestestR)
diff --git a/man/si.Rd b/man/si.Rd
index 0ca1492af..a066e987c 100644
--- a/man/si.Rd
+++ b/man/si.Rd
@@ -226,7 +226,6 @@ The Support Interval. \doi{10.31234/osf.io/zwnxb}
 Other ci: 
 \code{\link{bci}()},
 \code{\link{ci}()},
-\code{\link{cwi}()},
 \code{\link{eti}()},
 \code{\link{hdi}()},
 \code{\link{spi}()}
diff --git a/man/spi.Rd b/man/spi.Rd
index 6212d9343..b1bf0b560 100644
--- a/man/spi.Rd
+++ b/man/spi.Rd
@@ -127,7 +127,6 @@ Liu, Y., Gelman, A., & Zheng, T. (2015). Simulation-efficient shortest probabili
 Other ci: 
 \code{\link{bci}()},
 \code{\link{ci}()},
-\code{\link{cwi}()},
 \code{\link{eti}()},
 \code{\link{hdi}()},
 \code{\link{si}()}