Update documentation with new lifecycle badges

Resolves #60

DESCRIPTION

@@ -51,5 +51,5 @@ URL: https://pkg.mitchelloharawild.com/distributional/, https://github.com/mitch
 BugReports: https://github.com/mitchelloharawild/distributional/issues
 Encoding: UTF-8
 Language: en-GB
-Roxygen: list(markdown = TRUE, roclets=c('rd', 'collate', 'namespace'))
+Roxygen: list(markdown = TRUE)
 RoxygenNote: 7.2.1

NAMESPACE

@@ -514,6 +514,7 @@ importFrom(ggplot2,waiver)
 importFrom(grDevices,col2rgb)
 importFrom(grDevices,rgb)
 importFrom(lifecycle,deprecate_soft)
+importFrom(lifecycle,deprecated)
 importFrom(stats,density)
 importFrom(stats,family)
 importFrom(stats,median)

R/dist_bernoulli.R

@@ -1,6 +1,7 @@
 #' The Bernoulli distribution
 #'
-#' \lifecycle{stable}
+#' @description
+#' `r lifecycle::badge('stable')`
 #'
 #' Bernoulli distributions are used to represent events like coin flips
 #' when there is single trial that is either successful or unsuccessful.

R/dist_beta.R

@@ -1,6 +1,7 @@
 #' The Beta distribution
 #'
-#' \lifecycle{maturing}
+#' @description
+#' `r lifecycle::badge('stable')`
 #'
 #' @param shape1,shape2 The non-negative shape parameters of the Beta distribution.
 #'

R/dist_binomial.R

@@ -1,6 +1,7 @@
 #' The Binomial distribution
 #'
-#' \lifecycle{stable}
+#' @description
+#' `r lifecycle::badge('stable')`
 #'
 #' Binomial distributions are used to represent situations can that can
 #' be thought as the result of \eqn{n} Bernoulli experiments (here the

R/dist_burr.R

@@ -1,6 +1,7 @@
 #' The Burr distribution
 #'
-#' \lifecycle{stable}
+#' @description
+#' `r lifecycle::badge('stable')`
 #'
 #' @inheritParams actuar::dburr
 #'

R/dist_categorical.R

@@ -1,6 +1,7 @@
 #' The Categorical distribution
 #'
-#' \lifecycle{stable}
+#' @description
+#' `r lifecycle::badge('stable')`
 #'
 #' Categorical distributions are used to represent events with multiple
 #' outcomes, such as what number appears on the roll of a dice. This is also

R/dist_cauchy.R

@@ -1,6 +1,7 @@
 #' The Cauchy distribution
 #'
-#' \lifecycle{maturing}
+#' @description
+#' `r lifecycle::badge('stable')`
 #'
 #' The Cauchy distribution is the student's t distribution with one degree of
 #' freedom. The Cauchy distribution does not have a well defined mean or

R/dist_chisq.R

@@ -1,6 +1,7 @@
 #' The (non-central) Chi-Squared Distribution
 #'
-#' \lifecycle{stable}
+#' @description
+#' `r lifecycle::badge('stable')`
 #'
 #' Chi-square distributions show up often in frequentist settings
 #' as the sampling distribution of test statistics, especially

R/dist_degenerate.R

@@ -1,6 +1,7 @@
 #' The degenerate distribution
 #'
-#' \lifecycle{stable}
+#' @description
+#' `r lifecycle::badge('stable')`
 #'
 #' The degenerate distribution takes a single value which is certain to be
 #' observed. It takes a single parameter, which is the value that is observed

R/dist_exponential.R

@@ -1,6 +1,7 @@
 #' The Exponential Distribution
 #'
-#' \lifecycle{stable}
+#' @description
+#' `r lifecycle::badge('stable')`
 #'
 #' @inheritParams stats::dexp
 #'

R/dist_f.R

@@ -1,6 +1,7 @@
 #' The F Distribution
 #'
-#' \lifecycle{stable}
+#' @description
+#' `r lifecycle::badge('stable')`
 #'
 #' @inheritParams stats::df
 #'

R/dist_gamma.R

@@ -1,6 +1,7 @@
 #' The Gamma distribution
 #'
-#' \lifecycle{stable}
+#' @description
+#' `r lifecycle::badge('stable')`
 #'
 #' Several important distributions are special cases of the Gamma
 #' distribution. When the shape parameter is `1`, the Gamma is an

R/dist_geometric.R

@@ -1,11 +1,13 @@
 #' The Geometric Distribution
 #'
+#' @description
+#' `r lifecycle::badge('stable')`
+#'
 #' The Geometric distribution can be thought of as a generalization
 #' of the [dist_bernoulli()] distribution where we ask: "if I keep flipping a
 #' coin with probability `p` of heads, what is the probability I need
 #' \eqn{k} flips before I get my first heads?" The Geometric
 #' distribution is a special case of Negative Binomial distribution.
-#' \lifecycle{stable}
 #'
 #' @inheritParams stats::dgeom
 #'

R/dist_gumbel.R

@@ -1,6 +1,7 @@
 #' The Gumbel distribution
 #'
-#' \lifecycle{stable}
+#' @description
+#' `r lifecycle::badge('stable')`
 #'
 #' The Gumbel distribution is a special case of the Generalized Extreme Value
 #' distribution, obtained when the GEV shape parameter \eqn{\xi} is equal to 0.

R/dist_hypergeometric.R

@@ -1,6 +1,7 @@
 #' The Hypergeometric distribution
 #'
-#' \lifecycle{stable}
+#' @description
+#' `r lifecycle::badge('stable')`
 #'
 #' To understand the HyperGeometric distribution, consider a set of
 #' \eqn{r} objects, of which \eqn{m} are of the type I and

R/dist_inverse_exponential.R

@@ -1,6 +1,7 @@
 #' The Inverse Exponential distribution
 #'
-#' \lifecycle{stable}
+#' @description
+#' `r lifecycle::badge('stable')`
 #'
 #' @inheritParams actuar::dinvexp
 #'

R/dist_inverse_gamma.R

@@ -1,6 +1,7 @@
 #' The Inverse Gamma distribution
 #'
-#' \lifecycle{stable}
+#' @description
+#' `r lifecycle::badge('stable')`
 #'
 #' @inheritParams actuar::dinvgamma
 #'

R/dist_inverse_gaussian.R

@@ -1,6 +1,7 @@
 #' The Inverse Gaussian distribution
 #'
-#' \lifecycle{stable}
+#' @description
+#' `r lifecycle::badge('stable')`
 #'
 #' @inheritParams actuar::dinvgauss
 #'

R/dist_logarithmic.R

@@ -1,6 +1,7 @@
 #' The Logarithmic distribution
 #'
-#' \lifecycle{stable}
+#' @description
+#' `r lifecycle::badge('stable')`
 #'
 #' @inheritParams actuar::dlogarithmic
 #'

R/dist_logistic.R

@@ -1,6 +1,7 @@
 #' The Logistic distribution
 #'
-#' \lifecycle{stable}
+#' @description
+#' `r lifecycle::badge('stable')`
 #'
 #' A continuous distribution on the real line. For binary outcomes
 #' the model given by \eqn{P(Y = 1 | X) = F(X \beta)} where

R/dist_lognormal.R

@@ -1,6 +1,7 @@
 #' The log-normal distribution
 #'
-#' \lifecycle{stable}
+#' @description
+#' `r lifecycle::badge('stable')`
 #'
 #' The log-normal distribution is a commonly used transformation of the Normal
 #' distribution. If \eqn{X} follows a log-normal distribution, then \eqn{\ln{X}}

R/dist_missing.R

@@ -1,6 +1,7 @@
 #' Missing distribution
 #'
-#' \lifecycle{experimental}
+#' @description
+#' `r lifecycle::badge('maturing')`
 #'
 #' A placeholder distribution for handling missing values in a vector of
 #' distributions.

R/dist_multinomial.R

@@ -1,6 +1,7 @@
 #' The Multinomial distribution
 #'
-#' \lifecycle{maturing}
+#' @description
+#' `r lifecycle::badge('stable')`
 #'
 #' The multinomial distribution is a generalization of the binomial
 #' distribution to multiple categories. It is perhaps easiest to think

R/dist_multivariate_normal.R

@@ -1,6 +1,7 @@
 #' The multivariate normal distribution
 #'
-#' \lifecycle{maturing}
+#' @description
+#' `r lifecycle::badge('stable')`
 #'
 #' @param mu A list of numeric vectors for the distribution's mean.
 #' @param sigma A list of matrices for the distribution's variance-covariance matrix.

R/dist_negative_binomial.R

@@ -1,6 +1,7 @@
 #' The Negative Binomial distribution
 #'
-#' \lifecycle{stable}
+#' @description
+#' `r lifecycle::badge('stable')`
 #'
 #' A generalization of the geometric distribution. It is the number
 #' of failures in a sequence of i.i.d. Bernoulli trials before

R/dist_normal.R

@@ -1,6 +1,7 @@
 #' The Normal distribution
 #'
-#' \lifecycle{stable}
+#' @description
+#' `r lifecycle::badge('stable')`
 #'
 #' The Normal distribution is ubiquitous in statistics, partially because
 #' of the central limit theorem, which states that sums of i.i.d. random

R/dist_pareto.R

@@ -1,6 +1,7 @@
 #' The Pareto distribution
 #'
-#' \lifecycle{questioning}
+#' @description
+#' `r lifecycle::badge('stable')`
 #'
 #' @inheritParams actuar::dpareto
 #'

R/dist_percentile.R

@@ -1,6 +1,7 @@
 #' Percentile distribution
 #'
-#' \lifecycle{maturing}
+#' @description
+#' `r lifecycle::badge('stable')`
 #'
 #' @param x A list of values
 #' @param percentile A list of percentiles

R/dist_poisson.R

@@ -1,6 +1,7 @@
 #' The Poisson Distribution
 #'
-#' \lifecycle{stable}
+#' @description
+#' `r lifecycle::badge('stable')`
 #'
 #' Poisson distributions are frequently used to model counts.
 #'

R/dist_poisson_inverse_gaussian.R

@@ -1,6 +1,7 @@
 #' The Poisson-Inverse Gaussian distribution
 #'
-#' \lifecycle{stable}
+#' @description
+#' `r lifecycle::badge('stable')`
 #'
 #' @inheritParams actuar::dpoisinvgauss
 #'

R/dist_sample.R

@@ -1,6 +1,7 @@
 #' Sampling distribution
 #'
-#' \lifecycle{stable}
+#' @description
+#' `r lifecycle::badge('stable')`
 #'
 #' @param x A list of sampled values.
 #'

R/dist_student_t.R

@@ -1,6 +1,7 @@
 #' The (non-central) location-scale Student t Distribution
 #'
-#' \lifecycle{stable}
+#' @description
+#' `r lifecycle::badge('stable')`
 #'
 #' The Student's T distribution is closely related to the [Normal()]
 #' distribution, but has heavier tails. As \eqn{\nu} increases to \eqn{\infty},

R/dist_studentized_range.R

@@ -1,6 +1,7 @@
 #' The Studentized Range distribution
 #'
-#' \lifecycle{stable}
+#' @description
+#' `r lifecycle::badge('stable')`
 #'
 #' Tukey's studentized range distribution, used for Tukey's
 #' honestly significant differences test in ANOVA.

R/dist_uniform.R

@@ -1,6 +1,7 @@
 #' The Uniform distribution
 #'
-#' \lifecycle{stable}
+#' @description
+#' `r lifecycle::badge('stable')`
 #'
 #' A distribution with constant density on an interval.
 #'

R/dist_weibull.R

@@ -1,6 +1,7 @@
 #' The Weibull distribution
 #'
-#' \lifecycle{stable}
+#' @description
+#' `r lifecycle::badge('stable')`
 #'
 #' Generalization of the gamma distribution. Often used in survival and
 #' time-to-event analyses.

R/dist_wrap.R

@@ -1,6 +1,7 @@
 #' Create a distribution from p/d/q/r style functions
 #'
-#' \lifecycle{experimental}
+#' @description
+#' `r lifecycle::badge('maturing')`
 #'
 #' If a distribution is not yet supported, you can vectorise p/d/q/r functions
 #' using this function. `dist_wrap()` stores the distributions parameters, and