diff --git a/DESCRIPTION b/DESCRIPTION
index f095aa8..233cb32 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -25,8 +25,6 @@ Suggests:
     usethis
 Additional_repositories:
     https://stan-dev.r-universe.dev
-VignetteBuilder:
-    knitr
 Config/Needs/hexsticker: hexSticker, sysfonts
 Config/Needs/website: r-lib/pkgdown, epinowcast/enwtheme
 Config/testthat/edition: 3
diff --git a/R/check.R b/R/check.R
index 5ce8fd6..254cf6f 100644
--- a/R/check.R
+++ b/R/check.R
@@ -7,6 +7,9 @@
 #' @return NULL. The function will stop execution with an error message if
 #'         pdist is not a valid CDF.
 #' @export
+#'
+#' @family check
+#'
 #' @examples
 #' check_pdist(pnorm, D = 10)
 check_pdist <- function(pdist, D, ...) {
@@ -43,6 +46,8 @@ check_pdist <- function(pdist, D, ...) {
 #'         dprimary is not a valid PDF.
 #' @export
 #'
+#' @family check
+#'
 #' @examples
 #' check_dprimary(dunif, pwindow = 1)
 check_dprimary <- function(dprimary, pwindow, dprimary_args = list(),
diff --git a/R/dprimarycensoreddist.R b/R/dprimarycensoreddist.R
index aec9e79..ed1ddb0 100644
--- a/R/dprimarycensoreddist.R
+++ b/R/dprimarycensoreddist.R
@@ -1,5 +1,12 @@
 #' Compute the primary event censored PMF for delays
 #'
+#'
+#' This function computes the primary event censored probability mass function
+#' (PMF) for a given set of quantiles. It adjusts the PMF of the primary event
+#' distribution by accounting for the delay distribution and potential
+#' truncation at a maximum delay (D). The function allows for custom primary
+#' event distributions and delay distributions.
+#'
 #' @inheritParams pprimarycensoreddist
 #'
 #' @param x Vector of quantiles
@@ -19,6 +26,20 @@
 #'
 #' @export
 #'
+#' @details
+#' The primary event censored PMF is computed by taking the difference of the
+#' primary event censored cumulative distribution function (CDF) at two points,
+#' \eqn{d + \text{swindow}} and \eqn{d}. The primary event censored PMF,
+#' \eqn{f_{\text{cens}}(d)}, is given by:
+#' \deqn{
+#' f_{\text{cens}}(d) = F_{\text{cens}}(d + \text{swindow}) - F_{\text{cens}}(d)
+#' }
+#' where \eqn{F_{\text{cens}}} is the primary event censored CDF. For the
+#' explanation and mathematical details of the CDF, refer to the documentation
+#' of [pprimarycensoreddist()].
+#'
+#' @family primarycensoreddist
+#'
 #' @examples
 #' # Example: Weibull distribution with uniform primary events
 #' dprimarycensoreddist(c(0.1, 0.5, 1), pweibull, shape = 1.5, scale = 2.0)
diff --git a/R/expgrowth.R b/R/expgrowth.R
index 9dabc7f..7419fc5 100644
--- a/R/expgrowth.R
+++ b/R/expgrowth.R
@@ -20,35 +20,47 @@
 #' maximum of the lengths of the numerical arguments for the other functions.
 #'
 #' @details
-#' The exponential growth distribution is defined on the interval [min, max]
-#' with rate parameter r. Its probability density function (PDF) is:
+#' The exponential growth distribution is defined on the interval \[min, max\]
+#' with rate parameter (r). Its probability density function (PDF) is:
 #'
-#' f(x) = r * exp(r * (x - min)) / (exp(r * max) - exp(r * min))
+#' \deqn{f(x) = \frac{r \cdot \exp(r \cdot (x - min))}{\exp(r \cdot max) -
+#'  \exp(r \cdot min)}}
 #'
 #' The cumulative distribution function (CDF) is:
 #'
-#' F(x) = (exp(r * (x - min)) - exp(r * min)) / (exp(r * max) - exp(r * min))
+#' \deqn{F(x) = \frac{\exp(r \cdot (x - min)) - \exp(r \cdot min)}{
+#'  \exp(r \cdot max) - \exp(r \cdot min)}}
 #'
 #' For random number generation, we use the inverse transform sampling method:
-#' 1. Generate u ~ Uniform(0,1)
-#' 2. Set F(x) = u and solve for x:
-#'    x = min + (1/r) * log(u * (exp(r * max) - exp(r * min)) + exp(r * min))
+#' 1. Generate \eqn{u \sim \text{Uniform}(0,1)}
+#' 2. Set \eqn{F(x) = u} and solve for \eqn{x}:
+#'    \deqn{
+#'    x = min + \frac{1}{r} \cdot \log(u \cdot (\exp(r \cdot max) -
+#'   \exp(r \cdot min)) + \exp(r \cdot min))
+#'    }
 #'
 #' This method works because of the probability integral transform theorem,
-#' which states that if X is a continuous random variable with CDF F(x),
-#' then Y = F(X) follows a Uniform(0,1) distribution. Conversely, if U is
-#' a Uniform(0,1) random variable, then F^(-1)(U) has the same distribution
-#' as X, where F^(-1) is the inverse of the CDF.
+#' which states that if \eqn{X} is a continuous random variable with CDF
+#' \eqn{F(x)}, then \eqn{Y = F(X)} follows a \eqn{\text{Uniform}(0,1)}
+#' distribution. Conversely, if \eqn{U} is a \eqn{\text{Uniform}(0,1)} random
+#' variable, then \eqn{F^{-1}(U)} has the same distribution as \eqn{X}, where
+#' \eqn{F^{-1}} is the inverse of the CDF.
 #'
-#' In our case, we generate u from Uniform(0,1), then solve F(x) = u for x
-#' to get a sample from our exponential growth distribution. The formula
-#' for x is derived by algebraically solving the equation:
+#' In our case, we generate \eqn{u} from \eqn{\text{Uniform}(0,1)}, then solve
+#' \eqn{F(x) = u} for \eqn{x} to get a sample from our exponential growth
+#' distribution. The formula for \eqn{x} is derived by algebraically solving
+#' the equation:
 #'
-#' u = (exp(r * (x - min)) - exp(r * min)) / (exp(r * max) - exp(r * min))
+#' \deqn{
+#' u = \frac{\exp(r \cdot (x - min)) - \exp(r \cdot min)}{\exp(r \cdot max) -
+#'  \exp(r \cdot min)}
+#' }
 #'
-#' When r is very close to 0 (|r| < 1e-10), the distribution approximates
-#' a uniform distribution on [min, max], and we use a simpler method to
-#' generate samples directly from this uniform distribution.
+#' When \eqn{r} is very close to 0 (\eqn{|r| < 1e-10}), the distribution
+#' approximates a uniform distribution on \[min, max\], and we use a simpler
+#' method to generate samples directly from this uniform distribution.
+#'
+#' @family primaryeventdistributions
 #'
 #' @examples
 #' x <- seq(0, 1, by = 0.1)
diff --git a/R/pcd-stan-tools.R b/R/pcd-stan-tools.R
index ceccddb..29d0387 100644
--- a/R/pcd-stan-tools.R
+++ b/R/pcd-stan-tools.R
@@ -1,6 +1,9 @@
 #' Get the path to the Stan code
 #'
 #' @return A character string with the path to the Stan code
+#'
+#' @family stantools
+#'
 #' @export
 pcd_stan_path <- function() {
   system.file("stan", package = "primarycensoreddist")
@@ -52,6 +55,9 @@ pcd_stan_path <- function() {
 #' the Stan files.
 #'
 #' @export
+#'
+#' @family stantools
+#'
 #' @examples
 #' \dontrun{
 #' stan_functions <- pcd_stan_functions()
@@ -90,6 +96,9 @@ pcd_stan_functions <- function(
 #' write_to_file is TRUE. Defaults to "pcd_stan_functions.stan".
 #'
 #' @return A character string containing the requested Stan functions
+#'
+#' @family stantools
+#'
 #' @export
 pcd_load_stan_functions <- function(
     functions = NULL, stan_path = primarycensoreddist::pcd_stan_path(),
diff --git a/R/pprimarycensoreddist.R b/R/pprimarycensoreddist.R
index 9bec118..0194885 100644
--- a/R/pprimarycensoreddist.R
+++ b/R/pprimarycensoreddist.R
@@ -1,5 +1,11 @@
 #' Compute the primary event censored CDF for delays
 #'
+#' This function computes the primary event censored cumulative distribution
+#' function (CDF) for a given set of quantiles. It adjusts the CDF of the
+#' primary event distribution by accounting for the delay distribution and
+#' potential truncation at a maximum delay (D). The function allows for
+#' custom primary event distributions and delay distributions.
+#'
 #' @param q Vector of quantiles
 #'
 #' @param pdist Distribution function (CDF)
@@ -33,6 +39,29 @@
 #'
 #' @export
 #'
+#' @details
+#' The primary event censored CDF is computed by integrating the product of
+#' the primary event distribution function (CDF) and the delay distribution
+#' function (PDF) over the primary event window. The integration is adjusted
+#' for truncation if a finite maximum delay (D) is specified.
+#'
+#' The primary event censored CDF, \eqn{F_{\text{cens}}(q)}, is given by:
+#' \deqn{
+#' F_{\text{cens}}(q) = \int_{0}^{pwindow} F(q - p) \cdot f_{\text{primary}}(p)
+#' \, dp
+#' }
+#' where \eqn{F} is the CDF of the primary event distribution,
+#' \eqn{f_{\text{primary}}} is the PDF of the primary event times, and
+#' \eqn{pwindow} is the primary event window.
+#'
+#' If the maximum delay \eqn{D} is finite, the CDF is normalized by \eqn{F(D)}:
+#' \deqn{
+#' F_{\text{cens}}(q) = \int_{0}^{pwindow} \frac{F(q - p)}{F(D - p)} \cdot
+#' f_{\text{primary}}(p) \, dp
+#' }
+#'
+#' @family primarycensoreddist
+#'
 #' @examples
 #' # Example: Lognormal distribution with uniform primary events
 #' pprimarycensoreddist(c(0.1, 0.5, 1), plnorm, meanlog = 0, sdlog = 1)
diff --git a/R/rprimarycensoreddist.R b/R/rprimarycensoreddist.R
index 1f20ab1..b3defdb 100644
--- a/R/rprimarycensoreddist.R
+++ b/R/rprimarycensoreddist.R
@@ -1,14 +1,20 @@
 #' Generate random samples from a primary event censored distribution
 #'
+#' This function generates random samples from a primary event censored
+#' distribution. It adjusts the distribution by accounting for the primary
+#' event distribution and potential truncation at a maximum delay (D). The
+#' function allows for custom primary event distributions and delay
+#' distributions.
+#'
 #' @inheritParams dprimarycensoreddist
 #'
 #' @param rdist Function to generate random samples from the delay distribution
-#' for example \code{rlnorm} for lognormal distribution.
+#' for example [stats::rlnorm()] for lognormal distribution.
 #'
 #' @param n Number of random samples to generate.
 #'
 #' @param rprimary Function to generate random samples from the primary
-#' distribution (default is \code{runif}).
+#' distribution (default is [stats::runif()]).
 #'
 #' @param rprimary_args List of additional arguments to be passed to rprimary.
 #'
@@ -26,6 +32,20 @@
 #'
 #' @export
 #'
+#' @details
+#' The primary event censored random samples are generated by first
+#' oversampling to account for potential truncation. Primary event times are
+#' generated from the specified primary event distribution, and delays are
+#' generated from the specified delay distribution. The total delays are
+#' calculated by adding the primary event times and the delays. These total
+#' delays are then rounded to the nearest secondary event window (swindow).
+#' Truncation is applied to ensure that the delays are within the specified
+#' range \[0, D\]. If the number of valid samples is less than the desired
+#' number of samples (n), additional samples are generated until the required
+#' number of valid samples is obtained.
+#'
+#' @family primarycensoreddist
+#'
 #' @examples
 #' # Example: Lognormal distribution with uniform primary events
 #' rprimarycensoreddist(10, rlnorm, meanlog = 0, sdlog = 1)
diff --git a/_pkgdown.yml b/_pkgdown.yml
index 9ba5148..1286612 100644
--- a/_pkgdown.yml
+++ b/_pkgdown.yml
@@ -1,13 +1,40 @@
 url: https://primarycensoreddist.epinowcast.org/
 template:
   package: enwtheme
-  twitter:
-    creator: "@seabbs"
-    card: summary_large_image
-  opengraph:
-    twitter:
-      creator: "@seabbs"
-      card: summary_large_image
 
 development:
   mode: auto
+
+navbar:
+  structure:
+    left: [intro, reference, stanreference, articles]
+    right: [search, github, lightswitch]
+  components:
+    stanreference:
+      text: Stan reference
+      href: https://primarycensoreddist.epinowcast.org/stan/
+    articles:
+      text: Articles
+      menu:
+
+authors:
+  Sam Abbott:
+    href: "https://www.samabbott.co.uk/"
+
+reference:
+- title: Primary event censored distribution functions
+  desc: Primary event censored distribution functions
+  contents:
+  - has_concept("primarycensoreddist")
+- title: Primary event distributions
+  desc: Primary event distributions
+  contents:
+  - has_concept("primaryeventdistributions")
+- title: Distribution checking functions
+  desc: Distribution checking functions
+  contents:
+  - has_concept("check")
+- title: Tools for working with package Stan functions
+  desc: Tools for working with package Stan functions
+  contents:
+  - has_concept("stantools")
\ No newline at end of file
diff --git a/inst/WORDLIST b/inst/WORDLIST
index d99c703..86141fc 100644
--- a/inst/WORDLIST
+++ b/inst/WORDLIST
@@ -7,13 +7,16 @@ Lifecycle
 PMF
 PMFs
 SamuelBrand
+cens
 dprimary
 modeling
 pcd
 pdist
+primaryeventdistributions
 pwindow
 rprimary
 seabbs
 stan
-u
+stantools
+swindow
 zsusswein
diff --git a/man/check_dprimary.Rd b/man/check_dprimary.Rd
index 893f521..bf6b0f2 100644
--- a/man/check_dprimary.Rd
+++ b/man/check_dprimary.Rd
@@ -35,3 +35,8 @@ checking if it integrates to approximately 1 over the specified range.
 \examples{
 check_dprimary(dunif, pwindow = 1)
 }
+\seealso{
+Distribution checking functions
+\code{\link{check_pdist}()}
+}
+\concept{check}
diff --git a/man/check_pdist.Rd b/man/check_pdist.Rd
index 89c52b8..903a086 100644
--- a/man/check_pdist.Rd
+++ b/man/check_pdist.Rd
@@ -25,3 +25,8 @@ checking if it's monotonically increasing and bounded between 0 and 1.
 \examples{
 check_pdist(pnorm, D = 10)
 }
+\seealso{
+Distribution checking functions
+\code{\link{check_dprimary}()}
+}
+\concept{check}
diff --git a/man/dprimarycensoreddist.Rd b/man/dprimarycensoreddist.Rd
index 87f26a3..f6dd398 100644
--- a/man/dprimarycensoreddist.Rd
+++ b/man/dprimarycensoreddist.Rd
@@ -63,7 +63,23 @@ Vector of primary event censored PMFs, normalized by D if finite
 (truncation adjustment)
 }
 \description{
-Compute the primary event censored PMF for delays
+This function computes the primary event censored probability mass function
+(PMF) for a given set of quantiles. It adjusts the PMF of the primary event
+distribution by accounting for the delay distribution and potential
+truncation at a maximum delay (D). The function allows for custom primary
+event distributions and delay distributions.
+}
+\details{
+The primary event censored PMF is computed by taking the difference of the
+primary event censored cumulative distribution function (CDF) at two points,
+\eqn{d + \text{swindow}} and \eqn{d}. The primary event censored PMF,
+\eqn{f_{\text{cens}}(d)}, is given by:
+\deqn{
+f_{\text{cens}}(d) = F_{\text{cens}}(d + \text{swindow}) - F_{\text{cens}}(d)
+}
+where \eqn{F_{\text{cens}}} is the primary event censored CDF. For the
+explanation and mathematical details of the CDF, refer to the documentation
+of \code{\link[=pprimarycensoreddist]{pprimarycensoreddist()}}.
 }
 \examples{
 # Example: Weibull distribution with uniform primary events
@@ -76,3 +92,9 @@ dprimarycensoreddist(
   dprimary_args = list(r = 0.2), shape = 1.5, scale = 2.0
 )
 }
+\seealso{
+Primary event censored distribution functions
+\code{\link{pprimarycensoreddist}()},
+\code{\link{rprimarycensoreddist}()}
+}
+\concept{primarycensoreddist}
diff --git a/man/expgrowth.Rd b/man/expgrowth.Rd
index a3ae23f..498bfd1 100644
--- a/man/expgrowth.Rd
+++ b/man/expgrowth.Rd
@@ -42,37 +42,47 @@ Density, distribution function, and random generation for the exponential
 growth distribution.
 }
 \details{
-The exponential growth distribution is defined on the interval \link{min, max}
-with rate parameter r. Its probability density function (PDF) is:
+The exponential growth distribution is defined on the interval [min, max]
+with rate parameter (r). Its probability density function (PDF) is:
 
-f(x) = r * exp(r * (x - min)) / (exp(r * max) - exp(r * min))
+\deqn{f(x) = \frac{r \cdot \exp(r \cdot (x - min))}{\exp(r \cdot max) -
+ \exp(r \cdot min)}}
 
 The cumulative distribution function (CDF) is:
 
-F(x) = (exp(r * (x - min)) - exp(r * min)) / (exp(r * max) - exp(r * min))
+\deqn{F(x) = \frac{\exp(r \cdot (x - min)) - \exp(r \cdot min)}{
+ \exp(r \cdot max) - \exp(r \cdot min)}}
 
 For random number generation, we use the inverse transform sampling method:
 \enumerate{
-\item Generate u ~ Uniform(0,1)
-\item Set F(x) = u and solve for x:
-x = min + (1/r) * log(u * (exp(r * max) - exp(r * min)) + exp(r * min))
+\item Generate \eqn{u \sim \text{Uniform}(0,1)}
+\item Set \eqn{F(x) = u} and solve for \eqn{x}:
+\deqn{
+   x = min + \frac{1}{r} \cdot \log(u \cdot (\exp(r \cdot max) -
+  \exp(r \cdot min)) + \exp(r \cdot min))
+   }
 }
 
 This method works because of the probability integral transform theorem,
-which states that if X is a continuous random variable with CDF F(x),
-then Y = F(X) follows a Uniform(0,1) distribution. Conversely, if U is
-a Uniform(0,1) random variable, then F^(-1)(U) has the same distribution
-as X, where F^(-1) is the inverse of the CDF.
-
-In our case, we generate u from Uniform(0,1), then solve F(x) = u for x
-to get a sample from our exponential growth distribution. The formula
-for x is derived by algebraically solving the equation:
-
-u = (exp(r * (x - min)) - exp(r * min)) / (exp(r * max) - exp(r * min))
+which states that if \eqn{X} is a continuous random variable with CDF
+\eqn{F(x)}, then \eqn{Y = F(X)} follows a \eqn{\text{Uniform}(0,1)}
+distribution. Conversely, if \eqn{U} is a \eqn{\text{Uniform}(0,1)} random
+variable, then \eqn{F^{-1}(U)} has the same distribution as \eqn{X}, where
+\eqn{F^{-1}} is the inverse of the CDF.
+
+In our case, we generate \eqn{u} from \eqn{\text{Uniform}(0,1)}, then solve
+\eqn{F(x) = u} for \eqn{x} to get a sample from our exponential growth
+distribution. The formula for \eqn{x} is derived by algebraically solving
+the equation:
+
+\deqn{
+u = \frac{\exp(r \cdot (x - min)) - \exp(r \cdot min)}{\exp(r \cdot max) -
+ \exp(r \cdot min)}
+}
 
-When r is very close to 0 (|r| < 1e-10), the distribution approximates
-a uniform distribution on \link{min, max}, and we use a simpler method to
-generate samples directly from this uniform distribution.
+When \eqn{r} is very close to 0 (\eqn{|r| < 1e-10}), the distribution
+approximates a uniform distribution on [min, max], and we use a simpler
+method to generate samples directly from this uniform distribution.
 }
 \examples{
 x <- seq(0, 1, by = 0.1)
@@ -81,3 +91,4 @@ cumprobs <- pexpgrowth(x, r = 0.2)
 samples <- rexpgrowth(100, r = 0.2)
 
 }
+\concept{primaryeventdistributions}
diff --git a/man/pcd_load_stan_functions.Rd b/man/pcd_load_stan_functions.Rd
index d4c256f..1e4e374 100644
--- a/man/pcd_load_stan_functions.Rd
+++ b/man/pcd_load_stan_functions.Rd
@@ -34,3 +34,9 @@ A character string containing the requested Stan functions
 \description{
 Load Stan functions as a string
 }
+\seealso{
+Tools for working with package Stan functions
+\code{\link{pcd_stan_functions}()},
+\code{\link{pcd_stan_path}()}
+}
+\concept{stantools}
diff --git a/man/pcd_stan_functions.Rd b/man/pcd_stan_functions.Rd
index 795d68f..e1cf02c 100644
--- a/man/pcd_stan_functions.Rd
+++ b/man/pcd_stan_functions.Rd
@@ -25,3 +25,9 @@ stan_functions <- pcd_stan_functions()
 print(stan_functions)
 }
 }
+\seealso{
+Tools for working with package Stan functions
+\code{\link{pcd_load_stan_functions}()},
+\code{\link{pcd_stan_path}()}
+}
+\concept{stantools}
diff --git a/man/pcd_stan_path.Rd b/man/pcd_stan_path.Rd
index 7505a38..86b1b9e 100644
--- a/man/pcd_stan_path.Rd
+++ b/man/pcd_stan_path.Rd
@@ -12,3 +12,9 @@ A character string with the path to the Stan code
 \description{
 Get the path to the Stan code
 }
+\seealso{
+Tools for working with package Stan functions
+\code{\link{pcd_load_stan_functions}()},
+\code{\link{pcd_stan_functions}()}
+}
+\concept{stantools}
diff --git a/man/pprimarycensoreddist.Rd b/man/pprimarycensoreddist.Rd
index e7fcd3a..8f6aa4d 100644
--- a/man/pprimarycensoreddist.Rd
+++ b/man/pprimarycensoreddist.Rd
@@ -55,7 +55,32 @@ Vector of primary event censored CDFs, normalized by D if finite
 (truncation adjustment)
 }
 \description{
-Compute the primary event censored CDF for delays
+This function computes the primary event censored cumulative distribution
+function (CDF) for a given set of quantiles. It adjusts the CDF of the
+primary event distribution by accounting for the delay distribution and
+potential truncation at a maximum delay (D). The function allows for
+custom primary event distributions and delay distributions.
+}
+\details{
+The primary event censored CDF is computed by integrating the product of
+the primary event distribution function (CDF) and the delay distribution
+function (PDF) over the primary event window. The integration is adjusted
+for truncation if a finite maximum delay (D) is specified.
+
+The primary event censored CDF, \eqn{F_{\text{cens}}(q)}, is given by:
+\deqn{
+F_{\text{cens}}(q) = \int_{0}^{pwindow} F(q - p) \cdot f_{\text{primary}}(p)
+\, dp
+}
+where \eqn{F} is the CDF of the primary event distribution,
+\eqn{f_{\text{primary}}} is the PDF of the primary event times, and
+\eqn{pwindow} is the primary event window.
+
+If the maximum delay \eqn{D} is finite, the CDF is normalized by \eqn{F(D)}:
+\deqn{
+F_{\text{cens}}(q) = \int_{0}^{pwindow} \frac{F(q - p)}{F(D - p)} \cdot
+f_{\text{primary}}(p) \, dp
+}
 }
 \examples{
 # Example: Lognormal distribution with uniform primary events
@@ -68,3 +93,9 @@ pprimarycensoreddist(
   dprimary_args = list(r = 0.2), meanlog = 0, sdlog = 1
 )
 }
+\seealso{
+Primary event censored distribution functions
+\code{\link{dprimarycensoreddist}()},
+\code{\link{rprimarycensoreddist}()}
+}
+\concept{primarycensoreddist}
diff --git a/man/roxygen/meta.R b/man/roxygen/meta.R
new file mode 100644
index 0000000..a06abc8
--- /dev/null
+++ b/man/roxygen/meta.R
@@ -0,0 +1,8 @@
+list( # nolint
+  rd_family_title = list( # nolint
+    primarycensoreddist = "Primary event censored distribution functions",
+    primaryeventdistributions = "Primary event distributions",
+    check = "Distribution checking functions",
+    stantools = "Tools for working with package Stan functions"
+  )
+)
\ No newline at end of file
diff --git a/man/rprimarycensoreddist.Rd b/man/rprimarycensoreddist.Rd
index 64a1a46..bfe1455 100644
--- a/man/rprimarycensoreddist.Rd
+++ b/man/rprimarycensoreddist.Rd
@@ -33,7 +33,7 @@ rpcens(
 \item{n}{Number of random samples to generate.}
 
 \item{rdist}{Function to generate random samples from the delay distribution
-for example \code{rlnorm} for lognormal distribution.}
+for example \code{\link[stats:Lognormal]{stats::rlnorm()}} for lognormal distribution.}
 
 \item{pwindow}{Primary event window}
 
@@ -43,7 +43,7 @@ for example \code{rlnorm} for lognormal distribution.}
 truncated at D. If set to Inf, no truncation is applied. Defaults to Inf.}
 
 \item{rprimary}{Function to generate random samples from the primary
-distribution (default is \code{runif}).}
+distribution (default is \code{\link[stats:Uniform]{stats::runif()}}).}
 
 \item{rprimary_args}{List of additional arguments to be passed to rprimary.}
 
@@ -57,7 +57,23 @@ Vector of random samples from the primary event censored
 distribution
 }
 \description{
-Generate random samples from a primary event censored distribution
+This function generates random samples from a primary event censored
+distribution. It adjusts the distribution by accounting for the primary
+event distribution and potential truncation at a maximum delay (D). The
+function allows for custom primary event distributions and delay
+distributions.
+}
+\details{
+The primary event censored random samples are generated by first
+oversampling to account for potential truncation. Primary event times are
+generated from the specified primary event distribution, and delays are
+generated from the specified delay distribution. The total delays are
+calculated by adding the primary event times and the delays. These total
+delays are then rounded to the nearest secondary event window (swindow).
+Truncation is applied to ensure that the delays are within the specified
+range [0, D]. If the number of valid samples is less than the desired
+number of samples (n), additional samples are generated until the required
+number of valid samples is obtained.
 }
 \examples{
 # Example: Lognormal distribution with uniform primary events
@@ -70,3 +86,9 @@ rprimarycensoreddist(
   meanlog = 0, sdlog = 1
 )
 }
+\seealso{
+Primary event censored distribution functions
+\code{\link{dprimarycensoreddist}()},
+\code{\link{pprimarycensoreddist}()}
+}
+\concept{primarycensoreddist}