Package 'primarycensored'

Helper method for custom distributions

Description

pprimarycensored() and related functions can identify which distributions are provided via the pdist and dprimary arguments when those are base R functions (e.g. punif, dexp) via the name attribute.

Usage

add_name_attribute(func, name)

Arguments

func	Function, for example the p- or d- form of a distribution function.
name	Character string, starting with "p" or "d" indicating the underlying distribution.

Details

If you need to use a non-base R implementation, but know the distribution name, you can use this helper function to set it in a way that will be detected by pprimarycensored() and related functions.

This is useful as it enables the automatic use of analytical solutions for distributions where they exist. You can check which analytical solutions are available using methods(pcens_cdf) and check distribution names using pcd_dist_name().

Value

Function, with a "name" attribute added

See Also

Utility functions for working with distributions pcd_dist_name(), pcd_distributions, pcd_primary_distributions

Examples

dist <- add_name_attribute(pnorm, "hello")
attr(dist, "name")

---

Check if a function is a valid bounded probability density function (PDF)

Description

This function tests whether a given function behaves like a valid PDF by checking if it integrates to approximately 1 over the specified range and if it takes the arguments min and max.

Usage

check_dprimary(dprimary, pwindow, dprimary_args = list(), tolerance = 0.001)

Arguments

dprimary	Function to generate the probability density function (PDF) of primary event times. This function should take a value x and a pwindow parameter, and return a probability density. It should be normalized to integrate to 1 over [0, pwindow]. Defaults to a uniform distribution over [0, pwindow]. Users can provide custom functions or use helper functions like dexpgrowth for an exponential growth distribution. See pcd_primary_distributions() for examples. The package can identify base R distributions for potential analytical solutions. For non-base R functions, users can apply add_name_attribute() to yield properly tagged functions if they wish to leverage analytical solutions.
pwindow	Primary event window
dprimary_args	[Deprecated] Use primary_args instead.
tolerance	The tolerance for the integral to be considered close to 1

Value

NULL. The function will stop execution with an error message if dprimary is not a valid PDF.

See Also

Distribution checking functions check_pdist(), check_truncation()

Examples

check_dprimary(dunif, pwindow = 1)

---

Check if a function is a valid cumulative distribution function (CDF)

Description

This function tests whether a given function behaves like a valid CDF by checking if it's monotonically increasing and bounded between 0 and 1.

Usage

check_pdist(pdist, D = Inf, ...)

Arguments

pdist	Distribution function (CDF). The package can identify base R distributions for potential analytical solutions. For non-base R functions, users can apply add_name_attribute() to yield properly tagged functions if they wish to leverage the analytical solutions.
D	Maximum delay (upper truncation point). If finite, the distribution is truncated at D. If set to Inf, no upper truncation is applied. Defaults to Inf.
...	Additional arguments to be passed to pdist

Value

NULL. The function will stop execution with an error message if pdist is not a valid CDF.

See Also

Distribution checking functions check_dprimary(), check_truncation()

Examples

check_pdist(pnorm, D = 10)

---

Check if truncation time is appropriate relative to the maximum delay

Description

This function checks if the truncation time D is appropriate relative to the maximum delay. If D is much larger than necessary, it suggests considering setting it to Inf for better efficiency with minimal accuracy cost.

Usage

check_truncation(delays, D, multiplier = 2)

Arguments

delays	A numeric vector of delay times
D	The truncation time
multiplier	The multiplier for the maximum delay to compare with D. Default is 2.

Value

Invisible NULL. Prints a message if the condition is met.

See Also

Distribution checking functions check_dprimary(), check_pdist()

Examples

check_truncation(delays = c(1, 2, 3, 4), D = 10, multiplier = 2)

---

Hazard-parameterised piecewise-constant PMF

Description

Returns the probability mass for each value in x. Converts hazards to a PMF via hazards_to_pmf() then delegates to ddiscretestep().

Usage

ddiscretehazard(x, boundaries = NULL, hazards)

Arguments

x	Numeric vector of values at which to evaluate the PMF.
boundaries	Numeric vector of length $K+1$ defining the bin edges. Must be strictly increasing. Defaults to 0:K.
hazards	Numeric vector of length $K$ (or $K-1$, in which case a trailing 1 is appended) giving the discrete-time conditional hazard for each bin. Values must be in $[0, 1]$.

Details

Outside fitting it is a deterministic wrapper around ddiscretestep(). It earns its keep as a fitting parameterisation in fitdistdoublecens() because the random walk on the logit hazard smooths the recovered PMF.

Value

Numeric vector of PMF values, the same length as x.

See Also

ddiscretestep(), hazards_to_pmf(), pmf_to_hazards(), fitdistdoublecens()

Other pdiscretehazard: discretehazard_start(), pdiscretehazard(), rdiscretehazard()

Examples

hazards <- c(0.3, 0.5, 1)
ddiscretehazard(c(1, 2, 3), boundaries = 0:3, hazards = hazards)

---

Step (piecewise-constant) PMF

Description

Returns the probability mass for each value in x. Mass pmf[i] is located at boundaries[i+1] (the right edge of bin i); all other values return 0.

Usage

ddiscretestep(x, boundaries = NULL, pmf)

Arguments

x	Numeric vector of values at which to evaluate the PMF.
boundaries	Numeric vector of length $K+1$ defining the bin edges. Must be strictly increasing. Defaults to 0:K (unit-width daily bins) where K is inferred from length(pmf).
pmf	Numeric vector of length $K$ giving the probability mass for each bin. Must be non-negative and sum to approximately 1; if either condition is violated the function returns a vector of .Machine$double.eps rather than 0 (a soft simplex penalty that keeps log-density finite inside fitting closures).

Details

Like pdiscretestep, this function applies a soft simplex penalty: if pmf contains negative entries or fails to sum to 1 (within $10^{-8}$), the function returns near-zero density (.Machine$double.eps) rather than erroring. This makes it safe to call from inside fitdistrplus::fitdist() closures driven by fitdistdoublecens.

Value

Numeric vector of PMF values, the same length as x.

See Also

Other pdiscretestep: hazards_to_pmf(), pdiscretestep(), pmf_to_hazards(), rdiscretestep()

Examples

ddiscretestep(c(0, 1, 2, 3), boundaries = 0:3, pmf = c(0.2, 0.5, 0.3))

---

Start values for the logit-hazard parameterisation

Description

Builds a named list of starting values for fitdistdoublecens() with distr = "discretehazard". The free parameters are the logit intercept alpha, the log random-walk (or random-effect) scale log_sigma, and the K - 1 innovations eps_1, ..., eps_{K-1}. The final bin hazard is pinned to 1 inside the parameterisation so the implied PMF sums to 1.

Usage

discretehazard_start(K, alpha = -2, log_sigma = log(1), eps = 0)

Arguments

K	Integer, number of bins in the hazard parameterisation.
alpha	Numeric, start value for the logit intercept.
log_sigma	Numeric, start value for the log scale.
eps	Numeric, scalar or length-K - 1 vector of start values for the innovations eps_1, ..., eps_{K-1}.

Value

Named list suitable for the start argument of fitdistdoublecens().

See Also

Other pdiscretehazard: ddiscretehazard(), pdiscretehazard(), rdiscretehazard()

Examples

discretehazard_start(K = 5)

---

Compute the primary event censored PMF for delays

Description

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) and minimum delay (L). The function allows for custom primary event distributions and delay distributions.

Usage

dprimarycensored(
  x,
  pdist,
  pwindow = 1,
  swindow = 1,
  L = -Inf,
  D = Inf,
  dprimary = dunif,
  primary_args = NULL,
  pprimary = NULL,
  dprimary_args = NULL,
  log = FALSE,
  ...,
  check = TRUE
)

dpcens(
  x,
  pdist,
  pwindow = 1,
  swindow = 1,
  L = -Inf,
  D = Inf,
  dprimary = dunif,
  primary_args = NULL,
  pprimary = NULL,
  dprimary_args = NULL,
  log = FALSE,
  ...,
  check = TRUE
)

Arguments

x	Vector of quantiles
pdist	Distribution function (CDF). The package can identify base R distributions for potential analytical solutions. For non-base R functions, users can apply add_name_attribute() to yield properly tagged functions if they wish to leverage the analytical solutions.
pwindow	Primary event window
swindow	Secondary event window (default: 1)
L	Minimum delay (lower truncation point). Defaults to -Inf, meaning no left truncation. For any finite value of L the distribution is left-truncated at L.
D	Maximum delay (upper truncation point). If finite, the distribution is truncated at D. If set to Inf, no upper truncation is applied. Defaults to Inf.
dprimary	Function to generate the probability density function (PDF) of primary event times. This function should take a value x and a pwindow parameter, and return a probability density. It should be normalized to integrate to 1 over [0, pwindow]. Defaults to a uniform distribution over [0, pwindow]. Users can provide custom functions or use helper functions like dexpgrowth for an exponential growth distribution. See pcd_primary_distributions() for examples. The package can identify base R distributions for potential analytical solutions. For non-base R functions, users can apply add_name_attribute() to yield properly tagged functions if they wish to leverage analytical solutions.
primary_args	List of additional arguments to be passed to dprimary (and the matching primary CDF). For example, when using dexpgrowth, you would pass list(min = 0, max = pwindow, r = 0.2) to set the minimum, maximum, and rate parameters. Replaces the deprecated dprimary_args; defaults to NULL.
pprimary	Optional CDF for the primary event distribution. May be a function or a character string naming a primary distribution in pcd_primary_distributions. Defaults to NULL, in which case the primary CDF is looked up automatically from the registry using the "name" attribute of dprimary. When both dprimary and pprimary carry a "name" attribute (or are base R functions whose name can be inferred), the two names must agree on everything other than the leading d/p prefix; mismatches such as dunif + pexpgrowth raise an error. Supplying pprimary explicitly is mainly useful when using a custom primary distribution whose CDF is not in the registry.
dprimary_args	[Deprecated] Use primary_args instead.
log	Logical; if TRUE, probabilities p are given as log(p)
...	Additional arguments to be passed to the distribution function
check	Logical; if TRUE (the default) pdist is validated with check_pdist() and dprimary with check_dprimary(). Set to FALSE to skip both when they have already been validated. check_pdist() evaluates pdist at four points drawn with stats::runif(), so skipping it avoids that cost and leaves the random number stream untouched. Must be given by its full name, as it follows ....

Details

The primary event censored PMF is computed by taking the difference of the primary event censored cumulative distribution function (CDF) at two points, $d + \text{swindow}$ and $d$. The primary event censored PMF, $f_{\text{cens}}(d)$, is given by:

$f_{\text{cens}}(d) = F_{\text{cens}}(d + \text{swindow}) - F_{\text{cens}}(d)$

where $F_{\text{cens}}$ is the primary event censored CDF.

The function first computes the CDFs for all unique points (including both $d$ and $d + \text{swindow}$) using pprimarycensored(). It then creates a lookup table for these CDFs to efficiently calculate the PMF for each input value. For delays less than L, the function returns 0.

When the secondary censoring interval extends past the upper truncation point ($d + \text{swindow} > D$) but the lower endpoint satisfies $d < D$, the upper endpoint is internally clipped to $D$ before evaluating the CDF. The likelihood for such an observation is $P(X \in [d, \min(d + \text{swindow}, D)] \mid L \le X \le D)$, which equals the usual interval probability when $d + \text{swindow} \le D$. This avoids erroring when an observation's secondary window straddles the truncation point (relevant for non-parametric delays such as pdiscretestep()).

Observations with $d \ge D$ are rejected with an error: under the truncation $X \le D$, no event with latent value $d \ge D$ is observable, and accepting such inputs would otherwise yield a 0/0 likelihood.

The PMF is normalised to ensure it sums to 1 over the range [L, D\). This normalization uses:

$f_{\text{cens,norm}}(d) = \frac{f_{\text{cens}}(d)}{ F_{\text{cens}}(D) - F_{\text{cens}}(L)}$

where $f_{\text{cens,norm}}(d)$ is the normalized PMF. For the explanation and mathematical details of the CDF, refer to the documentation of pprimarycensored().

Value

Vector of primary event censored PMFs, normalized over [L, D] if truncation is applied

See Also

Primary event censored distribution functions pprimarycensored(), qprimarycensored(), rprimarycensored()

Examples

# Example: Weibull distribution with uniform primary events
dprimarycensored(c(0.1, 0.5, 1), pweibull, shape = 1.5, scale = 2.0)

# Example: Weibull distribution with exponential growth primary events
dprimarycensored(
  c(0.1, 0.5, 1), pweibull,
  dprimary = dexpgrowth,
  primary_args = list(r = 0.2), shape = 1.5, scale = 2.0
)

# Example: Left-truncated distribution (e.g., for generation intervals)
dprimarycensored(1:9, pweibull, L = 1, D = 10, shape = 1.5, scale = 2.0)

---

Exponential growth distribution functions

Description

Density, distribution function, and random generation for the exponential growth distribution.

Usage

dexpgrowth(x, min = 0, max = 1, r, log = FALSE)

pexpgrowth(q, min = 0, max = 1, r, lower.tail = TRUE, log.p = FALSE)

rexpgrowth(n, min = 0, max = 1, r)

Arguments

x, q	Vector of quantiles.
min	Minimum value of the distribution range. Default is 0.
max	Maximum value of the distribution range. Default is 1.
r	Rate parameter for the exponential growth.
log, log.p	Logical; if TRUE, probabilities p are given as log(p).
lower.tail	Logical; if TRUE (default), probabilities are P[X <= x], otherwise, P[X > x].
n	Number of observations. If length(n) > 1, the length is taken to be the number required.

Details

The exponential growth distribution is defined on the interval [min, max] with rate parameter (r). Its probability density function (PDF) is:

$f(x) = \frac{r \cdot \exp(r \cdot x)}{\exp(r \cdot max) - \exp(r \cdot min)}$

The cumulative distribution function (CDF) is:

$F(x) = \frac{\exp(r \cdot x) - \exp(r \cdot min)}{ \exp(r \cdot max) - \exp(r \cdot min)}$

For random number generation, we use the inverse transform sampling method:

1. Generate $u \sim \text{Uniform}(0,1)$

2. Set $F(x) = u$ and solve for $x$:

   $x = \frac{1}{r} \cdot \log(u \cdot \exp(r \cdot max) + (1 - u) \cdot \exp(r \cdot min))$

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 $\text{Uniform}(0,1)$ distribution. Conversely, if $U$ is a $\text{Uniform}(0,1)$ 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 $\text{Uniform}(0,1)$, 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:

$u = \frac{\exp(r \cdot x) - \exp(r \cdot min)}{\exp(r \cdot max) - \exp(r \cdot min)}$

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.

Value

dexpgrowth gives the density, pexpgrowth gives the distribution function, and rexpgrowth generates random deviates.

The length of the result is determined by n for rexpgrowth, and is the maximum of the lengths of the numerical arguments for the other functions.

Examples

x <- seq(0, 1, by = 0.1)
probs <- dexpgrowth(x, r = 0.2)
cumprobs <- pexpgrowth(x, r = 0.2)
samples <- rexpgrowth(100, r = 0.2)

---

Fit a distribution to doubly censored data

Description

This function wraps the custom approach for fitting distributions to doubly censored data using fitdistrplus and primarycensored. It handles primary censoring (when the primary event time is not known exactly), secondary censoring (when the secondary event time is interval-censored), and truncation (when events are only observed within a delay range [L, D]).

Usage

fitdistdoublecens(
  censdata,
  distr,
  left = "left",
  right = "right",
  pwindow = "pwindow",
  L = "L",
  D = "D",
  dprimary = dunif,
  primary_args = NULL,
  pprimary = NULL,
  dprimary_args = NULL,
  truncation_check_multiplier = 2,
  prior = NULL,
  hazard_model = c("rw", "re"),
  check = TRUE,
  ...
)

Arguments

censdata	A data frame with columns 'left' and 'right' representing the lower and upper bounds of the censored observations. Unlike fitdistrplus::fitdistcens() NA is not supported for either the upper or lower bounds.
distr	A character string naming the distribution to be fitted. Special values "discretestep" and "discretehazard" select the non-parametric step-distribution fitting; see Details.
left	Column name for lower bound of observed values (default: "left").
right	Column name for upper bound of observed values (default: "right").
pwindow	Column name for primary window (default: "pwindow").
L	Column name for minimum delay (lower truncation point). For any finite L the distribution is left-truncated at L; use L = -Inf for no left truncation. This is useful for modelling generation intervals where day 0 is excluded, particularly when used in renewal models. (default: "L"). If the column is not present in censdata, L = -Inf is assumed.
D	Column name for maximum delay (upper truncation point). If finite, the distribution is truncated at D. If set to Inf, no upper truncation is applied. (default: "D"). Observations whose secondary censoring interval straddles D (⁠left < D <= right⁠) are accepted: the upper endpoint is internally clipped to D and the likelihood becomes ⁠P(X in [left, min(right, D)] | L <= X <= D)⁠. This is a no-op for the standard parametric case where right <= D. Observations with left >= D are rejected because under truncation at D no event with latent value ⁠>= D⁠ is observable.
dprimary	Function to generate the probability density function (PDF) of primary event times. This function should take a value x and a pwindow parameter, and return a probability density. It should be normalized to integrate to 1 over [0, pwindow]. Defaults to a uniform distribution over [0, pwindow]. Users can provide custom functions or use helper functions like dexpgrowth for an exponential growth distribution. See pcd_primary_distributions() for examples. The package can identify base R distributions for potential analytical solutions. For non-base R functions, users can apply add_name_attribute() to yield properly tagged functions if they wish to leverage analytical solutions.
primary_args	List of additional arguments to be passed to dprimary (and the matching primary CDF). For example, when using dexpgrowth, you would pass list(min = 0, max = pwindow, r = 0.2) to set the minimum, maximum, and rate parameters. Replaces the deprecated dprimary_args; defaults to NULL.
pprimary	Optional CDF for the primary event distribution. May be a function or a character string naming a primary distribution in pcd_primary_distributions. Defaults to NULL, in which case the primary CDF is looked up automatically from the registry using the "name" attribute of dprimary. When both dprimary and pprimary carry a "name" attribute (or are base R functions whose name can be inferred), the two names must agree on everything other than the leading d/p prefix; mismatches such as dunif + pexpgrowth raise an error. Supplying pprimary explicitly is mainly useful when using a custom primary distribution whose CDF is not in the registry.
dprimary_args	[Deprecated] Use primary_args instead.
truncation_check_multiplier	Numeric multiplier to use for checking if the truncation time D is appropriate relative to the maximum delay. Set to NULL to skip the check. Default is 2.
prior	Optional list of prior settings used by the dist function's fit_penalty attribute (currently only "discretehazard"). Each element is itself a list with mean and sd entries. Defaults are used for any component not supplied. See pdiscretehazard() for the default values.
hazard_model	One of "rw" (default) or "re". Only consulted when distr = "discretehazard". "rw" selects the random-walk transform logit(h_i) = alpha + sigma * cumsum(eps); "re" selects the IID logit random-effect transform logit(h_i) = alpha + sigma * eps_i. See Details.
check	Logical; if TRUE (the default) pdist is validated with check_pdist() and dprimary with check_dprimary(). Neither changes across a fit, so validation runs on the first likelihood evaluation only rather than on every one. Set to FALSE to skip it entirely. For non-parametric distributions, start is required and determines the number of bins; pass boundaries here to override the default 0:K unit-width bins.
...	Additional arguments to be passed to fitdistrplus::fitdist().

Details

How distribution functions are resolved

The distr argument names a distribution. The function looks up the density and CDF functions by prepending d and p to the name (e.g. distr = "gamma" resolves to dgamma() and pgamma()). Custom distributions can be used as long as the corresponding ⁠d<distr>()⁠ and ⁠p<distr>()⁠ functions are defined.

Non-parametric distributions

Two non-parametric distributions are supported. They share a common fitting machinery: the dist function carries a vector_param attribute ("pmf" for pdiscretestep()/ddiscretestep(), "hazards" for pdiscretehazard()/ddiscretehazard()) that drives this function to build a closure mapping flat scalar parameters into the underlying vector argument.

• distr = "discretestep": free parameters ⁠p1, ..., p_{K-1}⁠ (in ⁠[0, 1]⁠); the last bin probability is ⁠1 - sum(p1, ..., p_{K-1})⁠. See pdiscretestep() for parameterisation details and the soft simplex penalty applied when probabilities are infeasible.

• distr = "discretehazard": free parameters alpha, log_sigma, ⁠eps_1, ..., eps_{K-1}⁠. The hazard form parameterises the same family of step distributions as "discretestep", but its free parameters drive either a Gaussian random walk on the logit hazard (hazard_model = "rw", the default, logit(h_i) = alpha + sigma * cumsum(eps)) or an IID logit random-effect transform (hazard_model = "re", logit(h_i) = alpha + sigma * eps_i with eps_i ~ N(0, 1)). The smoothing of the random walk regularises the recovered PMF against over-fitting in sparse data and replaces the simplex constraint with an unconstrained optimisation; the random-effect variant models hazards as independent draws around alpha rather than a smoothed trajectory. See pdiscretehazard() for full parameterisation details and the MAP-equivalent prior penalty applied during fitting; pass prior to override the default prior settings.

For non-parametric distributions K is implied by length(start): K = length(start) + 1 for "discretestep" and K = length(start) - 1 for "discretehazard". start is therefore required.

Value

An object of class "fitdist" as returned by fitdistrplus::fitdist.

See Also

pdiscretestep() pdiscretehazard()

Modelling wrappers for external fitting packages pcd_as_stan_data(), pcd_cmdstan_model()

Examples

# Example with normal distribution
set.seed(123)
n <- 1000
true_mean <- 5
true_sd <- 2
pwindow <- 2
swindow <- 2
D <- 10
samples <- rprimarycensored(
  n, rnorm,
  mean = true_mean, sd = true_sd,
  pwindow = pwindow, swindow = swindow, D = D
)

delay_data <- data.frame(
  left = samples,
  right = samples + swindow,
  pwindow = rep(pwindow, n),
  D = rep(D, n)
)

fit_norm <- fitdistdoublecens(
  delay_data,
  distr = "norm",
  start = list(mean = 0, sd = 1)
)

summary(fit_norm)


# Example with discretestep (non-parametric PMF) distribution
set.seed(42)
true_pmf <- c(0.1, 0.3, 0.4, 0.15, 0.05)
step_samples <- rprimarycensored(
  500, rdiscretestep,
  boundaries = 0:5, pmf = true_pmf,
  pwindow = 1, swindow = 1, D = 6
)
step_data <- data.frame(
  left = step_samples,
  right = step_samples + 1,
  pwindow = rep(1, 500),
  D = rep(6, 500)
)
fit_step <- fitdistdoublecens(
  step_data,
  distr = "discretestep",
  boundaries = 0:5,
  start = as.list(setNames(rep(0.2, 4), paste0("p", 1:4)))
)

# Example with discretehazard (logit-hazard random walk) distribution
fit_haz <- fitdistdoublecens(
  step_data,
  distr = "discretehazard",
  boundaries = 0:5,
  start = c(
    list(alpha = -2, log_sigma = log(0.5)),
    as.list(setNames(rep(0, 4), paste0("eps_", 1:4)))
  )
)

---

Convert discrete-time hazards to a PMF

Description

Given a vector of discrete-time hazards $h_1, \ldots, h_K$, computes the corresponding PMF:

$pmf_i = h_i \prod_{j < i} (1 - h_j)$

Usage

hazards_to_pmf(hazards)

Arguments

hazards

Numeric vector of hazards in $[0, 1]$. The last entry must equal 1 (within $10^{-8}$), or a vector of length $K-1$ may be supplied and the trailing 1 will be appended.

Details

The last hazard must equal 1 (to ensure the PMF sums to 1). If a vector of length $K-1$ is supplied (all hazards except the final exit hazard), 1 is appended automatically.

Value

Numeric vector of PMF values.

See Also

Other pdiscretestep: ddiscretestep(), pdiscretestep(), pmf_to_hazards(), rdiscretestep()

Examples

hazards_to_pmf(c(0.2, 0.3, 1))

---

S3 class for primary event censored distribution computation

Description

S3 class for primary event censored distribution computation

Usage

new_pcens(
  pdist,
  dprimary,
  primary_args = NULL,
  pprimary = NULL,
  dprimary_args = NULL,
  ...
)

Arguments

pdist	Distribution function (CDF). The package can identify base R distributions for potential analytical solutions. For non-base R functions, users can apply add_name_attribute() to yield properly tagged functions if they wish to leverage the analytical solutions.
dprimary	Function to generate the probability density function (PDF) of primary event times. This function should take a value x and a pwindow parameter, and return a probability density. It should be normalized to integrate to 1 over [0, pwindow]. Defaults to a uniform distribution over [0, pwindow]. Users can provide custom functions or use helper functions like dexpgrowth for an exponential growth distribution. See pcd_primary_distributions() for examples. The package can identify base R distributions for potential analytical solutions. For non-base R functions, users can apply add_name_attribute() to yield properly tagged functions if they wish to leverage analytical solutions.
primary_args	List of additional arguments to be passed to dprimary (and the looked-up pprimary). Replaces the deprecated dprimary_args.
pprimary	CDF of the primary event distribution. May be a function or a character string naming a primary distribution in pcd_primary_distributions. When NULL (the default), it is looked up automatically from the registry using the "name" attribute of dprimary. When both dprimary and pprimary carry a name, the two must agree on everything other than the leading d/p prefix; mismatches such as dunif + pexpgrowth raise an error.
dprimary_args	[Deprecated] Use primary_args instead.
...	Additional arguments to be passed to pdist

Value

An object with class hierarchy c("pcens_{pdist_name}_{dprimary_name}", "pcens_{pdist_name}", "pcens"). This contains the primary event distribution, the delay distribution, the delay distribution arguments, the primary event CDF (if available), and any additional arguments. It can be used with the pcens_cdf() function to compute the primary event censored CDF.

See Also

Low level primary event censored distribution objects and methods pcens_cdf(), pcens_cdf.default(), pcens_cdf.pcens_pdiscretehazard(), pcens_cdf.pcens_pdiscretestep(), pcens_cdf.pcens_pgamma_dunif(), pcens_cdf.pcens_pgengamma.orig_dunif(), pcens_cdf.pcens_pgengamma_dunif(), pcens_cdf.pcens_plnorm_dunif(), pcens_cdf.pcens_pweibull_dunif(), pcens_quantile(), pcens_quantile.default()

Examples

new_pcens(
  pdist = pgamma, dprimary = dunif,
  primary_args = list(min = 0, max = 1),
  shape = 1, scale = 1
)

---

Prepare data for primarycensored Stan model

Description

This function takes in delay data and prepares it for use with the primarycensored Stan model.

Usage

pcd_as_stan_data(
  data,
  delay = "delay",
  delay_upper = "delay_upper",
  n = "n",
  pwindow = "pwindow",
  start_relative_obs_time = "start_relative_obs_time",
  relative_obs_time = "relative_obs_time",
  dist_id,
  primary_id,
  param_bounds,
  primary_param_bounds,
  priors,
  primary_priors,
  compute_log_lik = FALSE,
  use_reduce_sum = FALSE,
  truncation_check_multiplier = 2,
  dist_options = NULL
)

Arguments

data	A data frame containing the delay data.
delay	Column name for observed delays (default: "delay")
delay_upper	Column name for upper bound of delays (default: "delay_upper")
n	Column name for count of observations (default: "n")
pwindow	Column name for primary window (default: "pwindow")
start_relative_obs_time	Column name for start of relative observation time, used as the lower truncation point L. Values may be any finite real number (including negatives) or -Inf to indicate no lower truncation. If the column is not present in data, L = -Inf is assumed for all observations. (default: "start_relative_obs_time")
relative_obs_time	Column name for relative observation time, used as the upper truncation point D. Values may be any finite real number (including negatives, paired with a smaller start_relative_obs_time) or +Inf to indicate no upper truncation. (default: "relative_obs_time")
dist_id	Integer identifying the delay distribution: You can use pcd_stan_dist_id() to get the dist ID for a distribution or look at the pcd_distributions data set.
primary_id	Integer identifying the primary distribution: You can use pcd_stan_dist_id() to get the primary dist ID for a distribution (make sure to select the "primary" type) or look at the pcd_primary_distributions data set.
param_bounds	A list with elements lower and upper, each a numeric vector specifying bounds for the delay distribution parameters.
primary_param_bounds	A list with elements lower and upper, each a numeric vector specifying bounds for the primary distribution parameters.
priors	A list with elements location and scale, each a numeric vector specifying priors for the delay distribution parameters.
primary_priors	A list with elements location and scale, each a numeric vector specifying priors for the primary distribution parameters.
compute_log_lik	Logical; compute log likelihood? (default: FALSE)
use_reduce_sum	Logical; use reduce_sum for performance? (default: FALSE)
truncation_check_multiplier	Numeric multiplier to use for checking if the truncation time D is appropriate relative to the maximum delay for each unique D value. Set to NULL to skip the check. Default is 2.
dist_options	Optional list carrying the shape of a non-parametric delay. When dist_id is one of 26 (step CDF, Dirichlet prior on the PMF), 27 (step CDF, random walk on the logit hazards) or 28 (step CDF, IID logit random effects on the hazards), this list must be supplied and must contain: K: integer, the number of bins. boundaries: numeric vector of length K + 1. Priors and param_bounds follow the same channel as the parametric path: pass them through priors and param_bounds. The semantics of priors depend on dist_id: dist_id = 26: priors$scale is the length-K Dirichlet concentration vector; priors$location is unused. dist_id = 27 or 28: priors$location = c(alpha_mean, log_sigma_mean) and priors$scale = c(alpha_sd, log_sigma_sd). param_bounds is unused for the non-parametric paths; pass list(lower = numeric(0), upper = numeric(0)). If priors is empty when a non-parametric dist_id is given, sensible defaults are applied (Dirichlet(1, ..., 1) for dist_id = 26; N(0, 5) on alpha and N(0, 1) on log_sigma for 27 and 28).

Value

A list containing the data formatted for use with pcd_cmdstan_model()

See Also

Modelling wrappers for external fitting packages fitdistdoublecens(), pcd_cmdstan_model()

Examples

data <- data.frame(
  delay = c(1, 2, 3),
  delay_upper = c(2, 3, 4),
  n = c(10, 20, 15),
  pwindow = c(1, 1, 2),
  relative_obs_time = c(10, 10, 10)
)
stan_data <- pcd_as_stan_data(
  data,
  dist_id = 1,
  primary_id = 1,
  param_bounds = list(lower = c(0, 0), upper = c(10, 10)),
  primary_param_bounds = list(lower = numeric(0), upper = numeric(0)),
  priors = list(location = c(1, 1), scale = c(1, 1)),
  primary_priors = list(location = numeric(0), scale = numeric(0))
)

---

Create a CmdStanModel with primarycensored Stan functions

Description

This function creates a CmdStanModel object using the Stan model and functions from primarycensored and optionally includes additional user-specified Stan files.

Usage

pcd_cmdstan_model(include_paths = primarycensored::pcd_stan_path(), ...)

Arguments

include_paths	Character vector of paths to include for Stan compilation. Defaults to the result of pcd_stan_path().
...	Additional arguments passed to cmdstanr::cmdstan_model().

Details

The underlying Stan model (pcens_model.stan) supports various features:

• Multiple probability distributions for modeling delays

• Primary and secondary censoring

• Truncation

• Optional use of reduce_sum for improved performance (via within chain parallelism).

• Flexible prior specifications

• Optional computation of log-likelihood for model comparison

Value

A CmdStanModel object.

See Also

Modelling wrappers for external fitting packages fitdistdoublecens(), pcd_as_stan_data()

Examples

if (!is.null(cmdstanr::cmdstan_version(error_on_NA = FALSE))) {
  model <- pcd_cmdstan_model(compile = FALSE)
  model
}

---

Get distribution function cdf or pdf name

Description

Get distribution function cdf or pdf name

Usage

pcd_dist_name(name, type = c("delay", "primary"))

Arguments

name	String. Distribution name or alias
type	String. "delay" or "primary" corresponding to the type of distribution to use as the look up. If delay then pcd_distributions() is used, if primary then pcd_primary_distributions() is used.

Value

String distribution function name or NA if no base R implementation

See Also

Utility functions for working with distributions add_name_attribute(), pcd_distributions, pcd_primary_distributions

Examples

pcd_dist_name("lnorm")
pcd_dist_name("lognormal")
pcd_dist_name("gamma")
pcd_dist_name("weibull")
pcd_dist_name("exp")
pcd_dist_name("unif", type = "primary")
pcd_dist_name("expgrowth", type = "primary")

---

Supported delay distributions

Description

A dataset containing information about the supported delay distributions in primarycensored. Includes both distributions with base R implementations and those only available in Stan. Distributions beyond these are not supported in the stan code but any user functions can be used in the R code.

Usage

pcd_distributions

Format

A data.frame with 28 rows and 4 columns:

name

Distribution name

pdist

R distribution function name (e.g. plnorm), NA if there is no base R implementation

aliases

Alternative names/identifiers

stan_id

Stan distribution ID used in the stan code

See Also

Utility functions for working with distributions add_name_attribute(), pcd_dist_name(), pcd_primary_distributions

---

Load Stan functions as a string

Description

Load Stan functions as a string

Usage

pcd_load_stan_functions(
  functions = NULL,
  stan_path = primarycensored::pcd_stan_path(),
  wrap_in_block = FALSE,
  write_to_file = FALSE,
  output_file = "pcd_functions.stan",
  dependencies = FALSE
)

Arguments

functions	Character vector of function names to load. Defaults to all functions.
stan_path	Character string, the path to the Stan code. Defaults to the path to the Stan code in the primarycensored package.
wrap_in_block	Logical, whether to wrap the functions in a ⁠functions{}⁠ block. Default is FALSE.
write_to_file	Logical, whether to write the output to a file. Default is FALSE.
output_file	Character string, the path to write the output file if write_to_file is TRUE. Defaults to "pcd_functions.stan".
dependencies	Logical, whether to include all functions that the requested functions depend on. When TRUE, recursively finds and includes all dependencies in the correct order (dependencies before the functions that use them). Default is FALSE.

Value

A character string containing the requested Stan functions

See Also

Tools for working with package Stan functions pcd_stan_dist_id(), pcd_stan_files(), pcd_stan_function_deps(), pcd_stan_functions(), pcd_stan_path()

---

Supported primary event distributions

Description

A dataset containing information about the supported primary event distributions in primarycensored. Distributions beyond these are not supported in the stan code but any user functions can be used in the R code.

Usage

pcd_primary_distributions

Format

A data.frame with 2 rows and 4 columns:

name

Distribution name

dprimary

R density function name

aliases

Alternative names/identifiers

stan_id

Stan distribution ID used in the stan code

See Also

Utility functions for working with distributions add_name_attribute(), pcd_dist_name(), pcd_distributions

---

Get distribution stan ID by name

Description

Get distribution stan ID by name

Usage

pcd_stan_dist_id(name, type = c("delay", "primary"))

Arguments

name	String. Distribution name or alias
type	String. "delay" or "primary" corresponding to the type of distribution to use as the look up. If delay then pcd_distributions() is used, if primary then pcd_primary_distributions() is used.

Value

Numeric distribution ID

See Also

Tools for working with package Stan functions pcd_load_stan_functions(), pcd_stan_files(), pcd_stan_function_deps(), pcd_stan_functions(), pcd_stan_path()

Examples

pcd_stan_dist_id("lnorm")
pcd_stan_dist_id("lognormal")
pcd_stan_dist_id("gamma")
pcd_stan_dist_id("weibull")
pcd_stan_dist_id("exp")
pcd_stan_dist_id("unif", type = "primary")

---

Get Stan files containing specified functions

Description

This function retrieves Stan files from a specified directory, optionally filtering for files that contain specific functions.

Usage

pcd_stan_files(functions = NULL, stan_path = primarycensored::pcd_stan_path())

Arguments

functions	Character vector of function names to search for. If NULL, all Stan files are returned.
stan_path	Character string specifying the path to the directory containing Stan files. Defaults to the Stan path of the primarycensored package.

Value

A character vector of file paths to Stan files.

See Also

Tools for working with package Stan functions pcd_load_stan_functions(), pcd_stan_dist_id(), pcd_stan_function_deps(), pcd_stan_functions(), pcd_stan_path()

---

Get dependencies for a Stan function

Description

Returns all Stan functions that the specified function depends on, in topological order (dependencies before the functions that use them).

Usage

pcd_stan_function_deps(
  function_name,
  stan_path = primarycensored::pcd_stan_path()
)

Arguments

function_name	Character string, the name of the Stan function.
stan_path	Character string specifying the path to the directory containing Stan files. Defaults to the Stan path of the primarycensored package.

Value

A character vector of function names that the specified function depends on, ordered so that dependencies come before functions that use them. The requested function itself is included as the last element.

See Also

Tools for working with package Stan functions pcd_load_stan_functions(), pcd_stan_dist_id(), pcd_stan_files(), pcd_stan_functions(), pcd_stan_path()

Examples

# See what primarycensored_lpmf depends on
pcd_stan_function_deps("primarycensored_lpmf")

# A function with no dependencies
pcd_stan_function_deps("expgrowth_pdf")

---

Get Stan function names from Stan files

Description

This function reads all Stan files in the specified directory and extracts the names of all functions defined in those files.

Usage

pcd_stan_functions(stan_path = primarycensored::pcd_stan_path())

Arguments

stan_path

Character string specifying the path to the directory containing Stan files. Defaults to the Stan path of the primarycensored package.

Value

A character vector containing unique names of all functions found in the Stan files.

See Also

Tools for working with package Stan functions pcd_load_stan_functions(), pcd_stan_dist_id(), pcd_stan_files(), pcd_stan_function_deps(), pcd_stan_path()

---

Get the path to the Stan code

Description

Get the path to the Stan code

Usage

pcd_stan_path()

Value

A character string with the path to the Stan code

See Also

Tools for working with package Stan functions pcd_load_stan_functions(), pcd_stan_dist_id(), pcd_stan_files(), pcd_stan_function_deps(), pcd_stan_functions()

---

Compute primary event censored CDF

Description

This function dispatches to either analytical solutions (if available) or numerical integration via the default method. To see which combinations have analytical solutions implemented, use methods(pcens_cdf). For example, pcens_cdf.gamma_unif indicates an analytical solution exists for gamma delay with uniform primary event distributions.

Usage

pcens_cdf(object, q, pwindow, use_numeric = FALSE)

Arguments

object	A primarycensored object as created by new_pcens().
q	Vector of quantiles
pwindow	Primary event window
use_numeric	Logical, if TRUE forces use of numeric integration even for distributions with analytical solutions. This is primarily useful for testing purposes or for settings where the analytical solution breaks down.

Value

Vector of computed primary event censored CDFs

See Also

Low level primary event censored distribution objects and methods new_pcens(), pcens_cdf.default(), pcens_cdf.pcens_pdiscretehazard(), pcens_cdf.pcens_pdiscretestep(), pcens_cdf.pcens_pgamma_dunif(), pcens_cdf.pcens_pgengamma.orig_dunif(), pcens_cdf.pcens_pgengamma_dunif(), pcens_cdf.pcens_plnorm_dunif(), pcens_cdf.pcens_pweibull_dunif(), pcens_quantile(), pcens_quantile.default()

---

Default method for computing primary event censored CDF

Description

This method serves as a fallback for combinations of delay and primary event distributions that don't have specific implementations. It uses a numeric integration method.

Usage

## Default S3 method:
pcens_cdf(object, q, pwindow, use_numeric = FALSE)

Arguments

object	A primarycensored object as created by new_pcens().
q	Vector of quantiles
pwindow	Primary event window
use_numeric	Logical, if TRUE forces use of numeric integration even for distributions with analytical solutions. This is primarily useful for testing purposes or for settings where the analytical solution breaks down.

Details

This method implements the numerical integration approach for computing the primary event censored CDF. It uses the same mathematical formulation as described in the details section of pprimarycensored(), but applies numerical integration instead of analytical solutions.

Value

Vector of computed primary event censored CDFs

See Also

pprimarycensored() for the mathematical details of the primary event censored CDF computation.

Low level primary event censored distribution objects and methods new_pcens(), pcens_cdf(), pcens_cdf.pcens_pdiscretehazard(), pcens_cdf.pcens_pdiscretestep(), pcens_cdf.pcens_pgamma_dunif(), pcens_cdf.pcens_pgengamma.orig_dunif(), pcens_cdf.pcens_pgengamma_dunif(), pcens_cdf.pcens_plnorm_dunif(), pcens_cdf.pcens_pweibull_dunif(), pcens_quantile(), pcens_quantile.default()

Examples

# Create a primarycensored object with gamma delay and uniform primary
pcens_obj <- new_pcens(
  pdist = pgamma,
  dprimary = dunif,
  primary_args = list(min = 0, max = 1),
  shape = 3,
  scale = 2
)

# Compute CDF for a single value
pcens_cdf(pcens_obj, q = 9, pwindow = 1)

# Compute CDF for multiple values
pcens_cdf(pcens_obj, q = c(4, 6, 8), pwindow = 1)

---

Method for hazard CDF delay with general primary event distribution

Description

Computes the analytic primary event censored CDF for a hazard-parameterised piecewise-constant delay distribution. Converts hazards to a PMF via hazards_to_pmf() then dispatches back through pcens_cdf() using a freshly constructed step-distribution object. The same primary event distribution and arguments are preserved.

Usage

## S3 method for class 'pcens_pdiscretehazard'
pcens_cdf(object, q, pwindow, use_numeric = FALSE)

Arguments

object	A primarycensored object as created by new_pcens().
q	Vector of quantiles
pwindow	Primary event window
use_numeric	Logical, if TRUE forces use of numeric integration even for distributions with analytical solutions. This is primarily useful for testing purposes or for settings where the analytical solution breaks down.

Value

Vector of computed primary event censored CDFs

See Also

Low level primary event censored distribution objects and methods new_pcens(), pcens_cdf(), pcens_cdf.default(), pcens_cdf.pcens_pdiscretestep(), pcens_cdf.pcens_pgamma_dunif(), pcens_cdf.pcens_pgengamma.orig_dunif(), pcens_cdf.pcens_pgengamma_dunif(), pcens_cdf.pcens_plnorm_dunif(), pcens_cdf.pcens_pweibull_dunif(), pcens_quantile(), pcens_quantile.default()

---

Method for step CDF delay with general primary event distribution

Description

Computes the analytic primary event censored CDF for a piecewise-constant (step) delay distribution and an arbitrary primary event distribution whose CDF $F_{primary}$ is available via object$pprimary.

Usage

## S3 method for class 'pcens_pdiscretestep'
pcens_cdf(object, q, pwindow, use_numeric = FALSE)

Arguments

object	A primarycensored object as created by new_pcens().
q	Vector of quantiles
pwindow	Primary event window
use_numeric	Logical, if TRUE forces use of numeric integration even for distributions with analytical solutions. This is primarily useful for testing purposes or for settings where the analytical solution breaks down.

Details

The observation CDF is

$F_{obs}(q) = \int_0^{pwindow} F_{step}(q-p)\,dF_{primary}(p)$

Because $F_{step}$ is piecewise constant, the integral reduces to

$F_{obs}(q) = \sum_k c_k \,[F_{primary}(p^{end}_k) - F_{primary}(p^{start}_k)]$

where $c_k$ is the constant value of $F_{step}$ on the $k$-th sub-interval of the primary event window induced by the step-function knots. The partition is exact for any bin widths, so bins may be wider or narrower than pwindow, and for boundaries that start below zero.

Falls back to pcens_cdf.default when use_numeric = TRUE or when no primary CDF is available on the object.

Value

Vector of computed primary event censored CDFs

See Also

Low level primary event censored distribution objects and methods new_pcens(), pcens_cdf(), pcens_cdf.default(), pcens_cdf.pcens_pdiscretehazard(), pcens_cdf.pcens_pgamma_dunif(), pcens_cdf.pcens_pgengamma.orig_dunif(), pcens_cdf.pcens_pgengamma_dunif(), pcens_cdf.pcens_plnorm_dunif(), pcens_cdf.pcens_pweibull_dunif(), pcens_quantile(), pcens_quantile.default()

---

Method for Gamma delay with uniform primary

Description

Method for Gamma delay with uniform primary

Usage

## S3 method for class 'pcens_pgamma_dunif'
pcens_cdf(object, q, pwindow, use_numeric = FALSE)

Arguments

object	A primarycensored object as created by new_pcens().
q	Vector of quantiles
pwindow	Primary event window
use_numeric	Logical, if TRUE forces use of numeric integration even for distributions with analytical solutions. This is primarily useful for testing purposes or for settings where the analytical solution breaks down.

Value

Vector of computed primary event censored CDFs

See Also

Low level primary event censored distribution objects and methods new_pcens(), pcens_cdf(), pcens_cdf.default(), pcens_cdf.pcens_pdiscretehazard(), pcens_cdf.pcens_pdiscretestep(), pcens_cdf.pcens_pgengamma.orig_dunif(), pcens_cdf.pcens_pgengamma_dunif(), pcens_cdf.pcens_plnorm_dunif(), pcens_cdf.pcens_pweibull_dunif(), pcens_quantile(), pcens_quantile.default()

---

Method for generalised gamma (Prentice parameterisation) delay with uniform primary

Description

Analytical solution for the generalised gamma distribution in the Prentice parameterisation used by flexsurv::pgengamma(), with parameters mu, sigma and Q. For Q > 0 this is mapped to the Stacy parameterisation of pcens_cdf.pcens_pgengamma.orig_dunif() via shape = Q / sigma, scale = exp(mu) * Q^(2 * sigma / Q) and k = 1 / Q^2. For Q <= 0 (the lognormal and reflected cases) the numerical pcens_cdf.default() method is used.

Usage

## S3 method for class 'pcens_pgengamma_dunif'
pcens_cdf(object, q, pwindow, use_numeric = FALSE)

Arguments

object	A primarycensored object as created by new_pcens().
q	Vector of quantiles
pwindow	Primary event window
use_numeric	Logical, if TRUE forces use of numeric integration even for distributions with analytical solutions. This is primarily useful for testing purposes or for settings where the analytical solution breaks down.

Value

Vector of computed primary event censored CDFs

See Also

Low level primary event censored distribution objects and methods new_pcens(), pcens_cdf(), pcens_cdf.default(), pcens_cdf.pcens_pdiscretehazard(), pcens_cdf.pcens_pdiscretestep(), pcens_cdf.pcens_pgamma_dunif(), pcens_cdf.pcens_pgengamma.orig_dunif(), pcens_cdf.pcens_plnorm_dunif(), pcens_cdf.pcens_pweibull_dunif(), pcens_quantile(), pcens_quantile.default()

---

Method for generalised gamma delay with uniform primary

Description

Analytical solution for the generalised gamma distribution in the Stacy parameterisation used by flexsurv::pgengamma.orig(), with parameters shape, scale and k. The delay CDF is $F_T(t) = P(k, (t / \theta)^a)$ with $P$ the regularised lower incomplete gamma function, $a$ the shape and $\theta$ the scale. The mean is $E[T] = \theta \Gamma(k + 1/a) / \Gamma(k)$ and the partial expectation distribution is $\tilde F_T(t) = P(k + 1/a, (t / \theta)^a)$, so the solution generalises the gamma (shape = 1) and Weibull (k = 1) cases. See vignette("analytic-solutions") for the derivation.

Usage

## S3 method for class 'pcens_pgengamma.orig_dunif'
pcens_cdf(object, q, pwindow, use_numeric = FALSE)

Arguments

object	A primarycensored object as created by new_pcens().
q	Vector of quantiles
pwindow	Primary event window
use_numeric	Logical, if TRUE forces use of numeric integration even for distributions with analytical solutions. This is primarily useful for testing purposes or for settings where the analytical solution breaks down.

Value

Vector of computed primary event censored CDFs

See Also

Low level primary event censored distribution objects and methods new_pcens(), pcens_cdf(), pcens_cdf.default(), pcens_cdf.pcens_pdiscretehazard(), pcens_cdf.pcens_pdiscretestep(), pcens_cdf.pcens_pgamma_dunif(), pcens_cdf.pcens_pgengamma_dunif(), pcens_cdf.pcens_plnorm_dunif(), pcens_cdf.pcens_pweibull_dunif(), pcens_quantile(), pcens_quantile.default()

Examples

pcens_obj <- new_pcens(
  pdist = flexsurv::pgengamma.orig,
  dprimary = dunif,
  dprimary_args = list(min = 0, max = 1),
  shape = 1.5,
  scale = 2,
  k = 0.8
)
pcens_cdf(pcens_obj, q = c(1, 4, 8), pwindow = 1)

---

Method for Log-Normal delay with uniform primary

Description

Method for Log-Normal delay with uniform primary

Usage

## S3 method for class 'pcens_plnorm_dunif'
pcens_cdf(object, q, pwindow, use_numeric = FALSE)

Arguments

object	A primarycensored object as created by new_pcens().
q	Vector of quantiles
pwindow	Primary event window
use_numeric	Logical, if TRUE forces use of numeric integration even for distributions with analytical solutions. This is primarily useful for testing purposes or for settings where the analytical solution breaks down.

Value

Vector of computed primary event censored CDFs

See Also

Low level primary event censored distribution objects and methods new_pcens(), pcens_cdf(), pcens_cdf.default(), pcens_cdf.pcens_pdiscretehazard(), pcens_cdf.pcens_pdiscretestep(), pcens_cdf.pcens_pgamma_dunif(), pcens_cdf.pcens_pgengamma.orig_dunif(), pcens_cdf.pcens_pgengamma_dunif(), pcens_cdf.pcens_pweibull_dunif(), pcens_quantile(), pcens_quantile.default()

---

Method for Weibull delay with uniform primary

Description

Method for Weibull delay with uniform primary

Usage

## S3 method for class 'pcens_pweibull_dunif'
pcens_cdf(object, q, pwindow, use_numeric = FALSE)

Arguments

object	A primarycensored object as created by new_pcens().
q	Vector of quantiles
pwindow	Primary event window
use_numeric	Logical, if TRUE forces use of numeric integration even for distributions with analytical solutions. This is primarily useful for testing purposes or for settings where the analytical solution breaks down.

Value

Vector of computed primary event censored CDFs

See Also

Low level primary event censored distribution objects and methods new_pcens(), pcens_cdf(), pcens_cdf.default(), pcens_cdf.pcens_pdiscretehazard(), pcens_cdf.pcens_pdiscretestep(), pcens_cdf.pcens_pgamma_dunif(), pcens_cdf.pcens_pgengamma.orig_dunif(), pcens_cdf.pcens_pgengamma_dunif(), pcens_cdf.pcens_plnorm_dunif(), pcens_quantile(), pcens_quantile.default()

---

Compute primary event censored quantiles

Description

This function inverts the primary event censored CDF to compute quantiles. It uses numerical optimisation via optim to find the value q such that pcens_cdf() is close to the specified probability. Currently, only the default numerical inversion method is implemented. Future analytical solutions may be added.

Usage

pcens_quantile(object, p, pwindow, L = -Inf, D = Inf, use_numeric = FALSE, ...)

Arguments

object	A primarycensored object as created by new_pcens().
p	A vector of probabilities at which to compute the quantiles.
pwindow	Primary event window
L	Minimum delay (lower truncation point). Defaults to -Inf, meaning no left truncation. For any finite value of L the distribution is left-truncated at L.
D	Maximum delay (upper truncation point). If finite, the distribution is truncated at D. If set to Inf, no upper truncation is applied. Defaults to Inf.
use_numeric	Logical; if TRUE forces the use of numeric inversion even if an analytical solution is available (not yet implemented).
...	Additional arguments to be passed to pdist

Value

Vector of primary event censored quantiles.

See Also

Low level primary event censored distribution objects and methods new_pcens(), pcens_cdf(), pcens_cdf.default(), pcens_cdf.pcens_pdiscretehazard(), pcens_cdf.pcens_pdiscretestep(), pcens_cdf.pcens_pgamma_dunif(), pcens_cdf.pcens_pgengamma.orig_dunif(), pcens_cdf.pcens_pgengamma_dunif(), pcens_cdf.pcens_plnorm_dunif(), pcens_cdf.pcens_pweibull_dunif(), pcens_quantile.default()

---

Default method for computing primary event censored quantiles

Description

This method inverts the primary event censored CDF by root-finding via stats::uniroot() with extendInt = "upX". The censored CDF is monotone in q, so stats::uniroot() extends its starting bracket outward as needed and handles infinite L or D without special casing.

Usage

## Default S3 method:
pcens_quantile(
  object,
  p,
  pwindow,
  L = -Inf,
  D = Inf,
  use_numeric = FALSE,
  init = 5,
  tol = 1e-08,
  max_iter = 10000,
  ...
)

Arguments

object	A primarycensored object as created by new_pcens().
p	A vector of probabilities at which to compute the quantiles.
pwindow	Primary event window
L	Minimum delay (lower truncation point). Defaults to -Inf, meaning no left truncation. For any finite value of L the distribution is left-truncated at L.
D	Maximum delay (upper truncation point). If finite, the distribution is truncated at D. If set to Inf, no upper truncation is applied. Defaults to Inf.
use_numeric	Logical; if TRUE forces the use of numeric inversion even if an analytical solution is available (not yet implemented).
init	Half-width of the initial search interval used when one or both truncation bounds are infinite. The starting interval is taken as ⁠[-init, init]⁠ (or ⁠[L, L + 2 * init]⁠ / ⁠[D - 2 * init, D]⁠ when only one bound is finite) and then extended outward by stats::uniroot() as needed. Defaults to 5, which brackets the bulk of most commonly used delay distributions.
tol	Numeric tolerance passed to stats::uniroot().
max_iter	Maximum number of stats::uniroot() iterations.
...	Additional arguments passed to underlying functions.

Value

A numeric vector containing the computed primary event censored quantiles.

See Also

Low level primary event censored distribution objects and methods new_pcens(), pcens_cdf(), pcens_cdf.default(), pcens_cdf.pcens_pdiscretehazard(), pcens_cdf.pcens_pdiscretestep(), pcens_cdf.pcens_pgamma_dunif(), pcens_cdf.pcens_pgengamma.orig_dunif(), pcens_cdf.pcens_pgengamma_dunif(), pcens_cdf.pcens_plnorm_dunif(), pcens_cdf.pcens_pweibull_dunif(), pcens_quantile()

Examples

# Create a primarycensored object with gamma delay and uniform primary
pcens_obj <- new_pcens(
  pdist = pgamma,
  dprimary = dunif,
  primary_args = list(min = 0, max = 1),
  shape = 3,
  scale = 2
)

# Compute quantile for a single probability
pcens_quantile(pcens_obj, p = 0.8, pwindow = 1)

# Compute quantiles for multiple probabilities
pcens_quantile(pcens_obj, p = c(0.25, 0.5, 0.75), pwindow = 1)

# Compute quantiles for multiple probabilities with truncation
pcens_quantile(pcens_obj, p = c(0.25, 0.5, 0.75), pwindow = 1, D = 10)

# Compute quantiles with left truncation
pcens_quantile(pcens_obj, p = c(0.25, 0.5, 0.75), pwindow = 1, L = 1, D = 10)

---

Hazard-parameterised piecewise-constant CDF

Description

Returns the CDF of a discrete distribution specified by its bin-wise discrete-time hazards. Converts hazards to a PMF via hazards_to_pmf() then delegates to pdiscretestep().

Usage

pdiscretehazard(q, boundaries = NULL, hazards)

Arguments

q	Numeric vector of quantiles.
boundaries	Numeric vector of length $K+1$ defining the bin edges. Must be strictly increasing. Defaults to 0:K.
hazards	Numeric vector of length $K$ (or $K-1$, in which case a trailing 1 is appended) giving the discrete-time conditional hazard for each bin. Values must be in $[0, 1]$.

Details

Outside fitting it is a deterministic wrapper around pdiscretestep(). It earns its keep as a fitting parameterisation in fitdistdoublecens() because the random walk on the logit hazard smooths the recovered PMF.

Value

Numeric vector of CDF values, the same length as q.

See Also

pdiscretestep(), hazards_to_pmf(), pmf_to_hazards(), fitdistdoublecens()

Other pdiscretehazard: ddiscretehazard(), discretehazard_start(), rdiscretehazard()

Examples

hazards <- c(0.3, 0.5, 1)
pdiscretehazard(c(0.5, 1, 2, 3), boundaries = 0:3, hazards = hazards)

---

Step (piecewise-constant) CDF

Description

Returns the CDF of a discrete distribution whose mass is concentrated at the right boundary of each bin. The CDF is right-continuous and piecewise constant with jumps at boundaries[2], ..., boundaries[K+1].

Usage

pdiscretestep(q, boundaries = NULL, pmf)

Arguments

q	Numeric vector of quantiles.
boundaries	Numeric vector of length $K+1$ defining the bin edges. Must be strictly increasing. Defaults to 0:K (unit-width daily bins) where K is inferred from length(pmf).
pmf	Numeric vector of length $K$ giving the probability mass for each bin. Must be non-negative and sum to approximately 1; if either condition is violated the function returns a vector of zeros (a soft simplex penalty for use inside optimisation).

Details

Below boundaries[1] the function returns 0. At boundaries[i+1] (the right edge of bin i), F jumps by pmf[i], so $F(boundaries[i+1]) = \sum_{j=1}^{i} pmf_j$. For q in $[boundaries[i], boundaries[i+1])$, F equals $\sum_{j=1}^{i-1} pmf_j$. At or above boundaries[K+1] the function returns 1.

Use with fitdistdoublecens()

This function carries the attribute vector_param = "pmf" so that fitdistdoublecens can drive it from a flat list of scalar parameters p1, ..., p_{K-1}. The free parameters are the first $K-1$ bin probabilities; the last is set to 1 - sum(p1, ..., p_{K-1}). When the implied probabilities violate the simplex (any negative entry, or sum departing from 1 by more than $10^{-8}$), the function returns 0 (or near-zero density in ddiscretestep) rather than erroring; this drives the optimiser back to the feasible region.

Value

Numeric vector of CDF values, the same length as q.

See Also

Other pdiscretestep: ddiscretestep(), hazards_to_pmf(), pmf_to_hazards(), rdiscretestep()

Examples

# Two-bin PMF: mass 0.3 at x=1, mass 0.7 at x=2
pdiscretestep(c(0.5, 1, 1.5, 2), boundaries = 0:2, pmf = c(0.3, 0.7))

---

Convert a PMF to discrete-time hazards

Description

Inverts hazards_to_pmf(): given a PMF, computes the discrete-time conditional hazard at each time point.

$h_i = pmf_i / (1 - \sum_{j < i} pmf_j)$

Usage

pmf_to_hazards(pmf)

Arguments

pmf	Numeric vector of probabilities. Must be non-negative and sum to approximately 1.

Details

The returned vector has the same length as pmf, with the last entry equal to 1.

Value

Numeric vector of hazards in $[0, 1]$.

See Also

Other pdiscretestep: ddiscretestep(), hazards_to_pmf(), pdiscretestep(), rdiscretestep()

Examples

pmf_to_hazards(c(0.2, 0.3, 0.5))

---

Compute the primary event censored CDF for delays

Description

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) and minimum delay (L). The function allows for custom primary event distributions and delay distributions.

Usage

pprimarycensored(
  q,
  pdist,
  pwindow = 1,
  L = -Inf,
  D = Inf,
  dprimary = dunif,
  primary_args = NULL,
  pprimary = NULL,
  dprimary_args = NULL,
  ...,
  check = TRUE
)

ppcens(
  q,
  pdist,
  pwindow = 1,
  L = -Inf,
  D = Inf,
  dprimary = dunif,
  primary_args = NULL,
  pprimary = NULL,
  dprimary_args = NULL,
  ...,
  check = TRUE
)

Arguments

q	Vector of quantiles
pdist	Distribution function (CDF). The package can identify base R distributions for potential analytical solutions. For non-base R functions, users can apply add_name_attribute() to yield properly tagged functions if they wish to leverage the analytical solutions.
pwindow	Primary event window
L	Minimum delay (lower truncation point). Defaults to -Inf, meaning no left truncation. For any finite value of L the distribution is left-truncated at L.
D	Maximum delay (upper truncation point). If finite, the distribution is truncated at D. If set to Inf, no upper truncation is applied. Defaults to Inf.
dprimary	Function to generate the probability density function (PDF) of primary event times. This function should take a value x and a pwindow parameter, and return a probability density. It should be normalized to integrate to 1 over [0, pwindow]. Defaults to a uniform distribution over [0, pwindow]. Users can provide custom functions or use helper functions like dexpgrowth for an exponential growth distribution. See pcd_primary_distributions() for examples. The package can identify base R distributions for potential analytical solutions. For non-base R functions, users can apply add_name_attribute() to yield properly tagged functions if they wish to leverage analytical solutions.
primary_args	List of additional arguments to be passed to dprimary (and the matching primary CDF). For example, when using dexpgrowth, you would pass list(min = 0, max = pwindow, r = 0.2) to set the minimum, maximum, and rate parameters. Replaces the deprecated dprimary_args; defaults to NULL.
pprimary	Optional CDF for the primary event distribution. May be a function or a character string naming a primary distribution in pcd_primary_distributions. Defaults to NULL, in which case the primary CDF is looked up automatically from the registry using the "name" attribute of dprimary. When both dprimary and pprimary carry a "name" attribute (or are base R functions whose name can be inferred), the two names must agree on everything other than the leading d/p prefix; mismatches such as dunif + pexpgrowth raise an error. Supplying pprimary explicitly is mainly useful when using a custom primary distribution whose CDF is not in the registry.
dprimary_args	[Deprecated] Use primary_args instead.
...	Additional arguments to be passed to pdist
check	Logical; if TRUE (the default) pdist is validated with check_pdist() and dprimary with check_dprimary(). Set to FALSE to skip both when they have already been validated. check_pdist() evaluates pdist at four points drawn with stats::runif(), so skipping it avoids that cost and leaves the random number stream untouched. Must be given by its full name, as it follows ....

Details

The primary event censored CDF is computed by integrating the product of the delay distribution function (CDF) and the primary event distribution function (PDF) over the primary event window. The integration is adjusted for truncation if specified.

The primary event censored CDF, $F_{\text{cens}}(q)$, is given by:

$F_{\text{cens}}(q) = \int_{0}^{pwindow} F(q - p) \cdot f_{\text{primary}}(p) \, dp$

where $F$ is the CDF of the delay distribution, $f_{\text{primary}}$ is the PDF of the primary event times, and $pwindow$ is the primary event window.

If truncation is applied (finite D or finite L), the CDF is normalized:

$F_{\text{cens,norm}}(q) = \frac{F_{\text{cens}}(q) - F_{\text{cens}}(L)}{ F_{\text{cens}}(D) - F_{\text{cens}}(L)}$

where $F_{\text{cens,norm}}(q)$ is the normalized CDF. For values $q \leq L$, the function returns 0; for values $q \geq D$, it returns 1.

This function creates a primarycensored object using new_pcens() and then computes the primary event censored CDF using pcens_cdf(). This abstraction allows for automatic use of analytical solutions when available, while seamlessly falling back to numerical integration when necessary.

See methods(pcens_cdf) for which combinations have analytical solutions implemented.

Value

Vector of primary event censored CDFs, normalized over [L, D] if truncation is applied

See Also

new_pcens() and pcens_cdf()

Primary event censored distribution functions dprimarycensored(), qprimarycensored(), rprimarycensored()

Examples

# Example: Lognormal distribution with uniform primary events
pprimarycensored(c(0.1, 0.5, 1), plnorm, meanlog = 0, sdlog = 1)

# Example: Lognormal distribution with exponential growth primary events
pprimarycensored(
  c(0.1, 0.5, 1), plnorm,
  dprimary = dexpgrowth,
  primary_args = list(r = 0.2), meanlog = 0, sdlog = 1
)

# Example: Left-truncated distribution (e.g., for generation intervals)
pprimarycensored(
  c(1, 2, 3), plnorm,
  L = 1, D = 10,
  meanlog = 0, sdlog = 1
)

---

Compute quantiles corresponding to target probabilities for primary event censored delays

Description

This function computes the quantiles (delay values) that correspond to specified probabilities in the primary event censored distribution. For a given probability p, it computes the delay value q such that the cumulative probability up to q equals p in the primary event censored distribution. The distribution accounts for both the delay distribution and the primary event timing distribution.

Usage

qprimarycensored(
  p,
  pdist,
  pwindow = 1,
  L = -Inf,
  D = Inf,
  dprimary = dunif,
  primary_args = NULL,
  pprimary = NULL,
  dprimary_args = NULL,
  ...,
  check = TRUE
)

qpcens(
  p,
  pdist,
  pwindow = 1,
  L = -Inf,
  D = Inf,
  dprimary = dunif,
  primary_args = NULL,
  pprimary = NULL,
  dprimary_args = NULL,
  ...,
  check = TRUE
)

Arguments

p	Vector of probabilities between 0 and 1 for which to compute corresponding quantiles
pdist	Distribution function (CDF). The package can identify base R distributions for potential analytical solutions. For non-base R functions, users can apply add_name_attribute() to yield properly tagged functions if they wish to leverage the analytical solutions.
pwindow	Primary event window
L	Minimum delay (lower truncation point). Defaults to -Inf, meaning no left truncation. For any finite value of L the distribution is left-truncated at L.
D	Maximum delay (upper truncation point). If finite, the distribution is truncated at D. If set to Inf, no upper truncation is applied. Defaults to Inf.
dprimary	Function to generate the probability density function (PDF) of primary event times. This function should take a value x and a pwindow parameter, and return a probability density. It should be normalized to integrate to 1 over [0, pwindow]. Defaults to a uniform distribution over [0, pwindow]. Users can provide custom functions or use helper functions like dexpgrowth for an exponential growth distribution. See pcd_primary_distributions() for examples. The package can identify base R distributions for potential analytical solutions. For non-base R functions, users can apply add_name_attribute() to yield properly tagged functions if they wish to leverage analytical solutions.
primary_args	List of additional arguments to be passed to dprimary (and the matching primary CDF). For example, when using dexpgrowth, you would pass list(min = 0, max = pwindow, r = 0.2) to set the minimum, maximum, and rate parameters. Replaces the deprecated dprimary_args; defaults to NULL.
pprimary	Optional CDF for the primary event distribution. May be a function or a character string naming a primary distribution in pcd_primary_distributions. Defaults to NULL, in which case the primary CDF is looked up automatically from the registry using the "name" attribute of dprimary. When both dprimary and pprimary carry a "name" attribute (or are base R functions whose name can be inferred), the two names must agree on everything other than the leading d/p prefix; mismatches such as dunif + pexpgrowth raise an error. Supplying pprimary explicitly is mainly useful when using a custom primary distribution whose CDF is not in the registry.
dprimary_args	[Deprecated] Use primary_args instead.
...	Additional arguments to be passed to pdist
check	Logical; if TRUE (the default) pdist is validated with check_pdist() and dprimary with check_dprimary(). Set to FALSE to skip both when they have already been validated. check_pdist() evaluates pdist at four points drawn with stats::runif(), so skipping it avoids that cost and leaves the random number stream untouched. Must be given by its full name, as it follows ....

Details

For each probability, the function finds the delay value where that proportion of events have occurred by that time in the primary event censored distribution. This is done by inverting the cumulative distribution function.

The function creates a primarycensored object using new_pcens() and then computes the quantiles using pcens_quantile(). This approach allows for analytical solutions when available, falling back to numerical methods when necessary.

For example, if p = 0.5, the function returns the median delay (truncated over [L, D] if specified) where 50% of censored events occur by this time and 50% occur after.

See methods(pcens_quantile) for which combinations have analytical solutions implemented.

Value

Vector of delay values (quantiles) corresponding to the input probabilities

See Also

new_pcens() and pcens_quantile()

Primary event censored distribution functions dprimarycensored(), pprimarycensored(), rprimarycensored()

Examples

# Compute delays where 25%, 50%, and 75% of events occur by (quartiles)
# Using lognormal delays with uniform primary events
qprimarycensored(c(0.25, 0.5, 0.75), plnorm, meanlog = 0, sdlog = 1)

# Same quartiles but with exponential growth in primary events
qprimarycensored(
  c(0.25, 0.5, 0.75), plnorm,
  dprimary = dexpgrowth,
  primary_args = list(r = 0.2), meanlog = 0, sdlog = 1
)

# Same quartiles but with truncation at 10
qprimarycensored(
  c(0.25, 0.5, 0.75), plnorm,
  dprimary = dexpgrowth,
  primary_args = list(r = 0.2), meanlog = 0, sdlog = 1, D = 10
)

# Left-truncated distribution (e.g., for generation intervals)
qprimarycensored(
  c(0.25, 0.5, 0.75), plnorm,
  L = 1, D = 10, meanlog = 0, sdlog = 1
)

---

Sample from a hazard-parameterised step distribution

Description

Draws n independent samples from the discrete distribution defined by hazards. Converts hazards to a PMF via hazards_to_pmf() then delegates to rdiscretestep().

Usage

rdiscretehazard(n, boundaries = NULL, hazards)

Arguments

n	Integer. Number of samples to draw.
boundaries	Numeric vector of length $K+1$ defining the bin edges. Must be strictly increasing. Defaults to 0:K.
hazards	Numeric vector of length $K$ (or $K-1$, in which case a trailing 1 is appended) giving the discrete-time conditional hazard for each bin. Values must be in $[0, 1]$.

Details

Outside fitting it is a deterministic wrapper around rdiscretestep(). It earns its keep as a fitting parameterisation in fitdistdoublecens() because the random walk on the logit hazard smooths the recovered PMF.

Value

Numeric vector of length n.

See Also

rdiscretestep(), hazards_to_pmf(), pmf_to_hazards(), fitdistdoublecens()

Other pdiscretehazard: ddiscretehazard(), discretehazard_start(), pdiscretehazard()

Examples

set.seed(42)
rdiscretehazard(10, boundaries = 0:3, hazards = c(0.3, 0.5, 1))

---

Sample from a step distribution

Description

Draws n independent samples from the discrete distribution with probability mass pmf[i] at boundaries[i+1].

Usage

rdiscretestep(n, boundaries = NULL, pmf)

Arguments

n	Integer. Number of samples to draw.
boundaries	Numeric vector of length $K+1$ defining the bin edges. Must be strictly increasing. Defaults to 0:K (unit-width daily bins) where K is inferred from length(pmf).
pmf	Numeric vector of length $K$ giving the probability mass for each bin. Must be non-negative and sum to approximately 1; if either condition is violated the function returns a vector of zeros (a soft simplex penalty for use inside optimisation).

Value

Numeric vector of length n.

See Also

Other pdiscretestep: ddiscretestep(), hazards_to_pmf(), pdiscretestep(), pmf_to_hazards()

Examples

set.seed(42)
rdiscretestep(10, boundaries = 0:3, pmf = c(0.2, 0.5, 0.3))

---

Generate random samples from a primary event censored distribution

Description

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) and minimum delay (L). The function allows for custom primary event distributions and delay distributions.

Usage

rprimarycensored(
  n,
  rdist,
  pwindow = 1,
  swindow = 1,
  L = -Inf,
  D = Inf,
  rprimary = stats::runif,
  rprimary_args = list(),
  oversampling_factor = 1.2,
  ...
)

rpcens(
  n,
  rdist,
  pwindow = 1,
  swindow = 1,
  L = -Inf,
  D = Inf,
  rprimary = stats::runif,
  rprimary_args = list(),
  oversampling_factor = 1.2,
  ...
)

Arguments

n	Number of random samples to generate.
rdist	Function to generate random samples from the delay distribution for example stats::rlnorm() for lognormal distribution.
pwindow	Primary event window
swindow	Integer specifying the window size for rounding the delay (default is 1). If swindow = 0 then no rounding is applied.
L	Minimum delay (lower truncation point). Defaults to -Inf, meaning no left truncation. For any finite value of L the distribution is left-truncated at L.
D	Maximum delay (upper truncation point). If finite, the distribution is truncated at D. If set to Inf, no upper truncation is applied. Defaults to Inf.
rprimary	Function to generate random samples from the primary distribution (default is stats::runif()).
rprimary_args	List of additional arguments to be passed to rprimary.
oversampling_factor	Factor by which to oversample the number of samples to account for truncation (default is 1.2).
...	Additional arguments to be passed to the distribution function.

Details

The mathematical formulation for generating random samples from a primary event censored distribution is as follows:

1. Generate primary event times (p) from the specified primary event distribution (f_p) with parameters phi, defined between 0 and the primary event window (pwindow):

   $p \sim f_p(\phi), \quad p \in [0, pwindow]$

2. Generate delays (d) from the specified delay distribution (f_d) with parameters theta:

   $d \sim f_d(\theta)$

3. Calculate the total delays (t) by adding the primary event times and the delays:

   $t = p + d$

4. Apply upper truncation to remove delays >= D:

   $t_{upper} = \{t \mid t < D\}$

5. Round the delays to the nearest secondary event window (swindow):

   $t_{rounded} = \lfloor \frac{t_{upper}}{swindow} \rfloor \times swindow$

6. Apply lower truncation on the rounded values to ensure observed delays are >= L:

   $t_{valid} = \{t_{rounded} \mid t_{rounded} \geq L\}$

The function oversamples to account for potential truncation and generates additional samples if needed to reach the desired number of valid samples.

Value

Vector of random samples from the primary event censored distribution censored by the secondary event window.

See Also

Primary event censored distribution functions dprimarycensored(), pprimarycensored(), qprimarycensored()

Examples

# Example: Lognormal distribution with uniform primary events
rprimarycensored(10, rlnorm, meanlog = 0, sdlog = 1)

# Example: Lognormal distribution with exponential growth primary events
rprimarycensored(
  10, rlnorm,
  rprimary = rexpgrowth, rprimary_args = list(r = 0.2),
  meanlog = 0, sdlog = 1
)

# Example: Left-truncated distribution (e.g., for generation intervals)
rprimarycensored(10, rlnorm, L = 1, D = 10, meanlog = 0, sdlog = 1)

---