discretise: Discretise a <dist_spec>

In EpiNow2: Estimate and Forecast Real-Time Infection Dynamics

Discretise a <dist_spec>

Description

Discretise a <dist_spec>

Usage

## S3 method for class 'dist_spec'
discretise(x, strict = TRUE, remove_trailing_zeros = TRUE, ...)

discretize(x, ...)

Arguments

x	A ⁠<dist_spec>⁠
strict	Logical; If TRUE (default) an error will be thrown if a distribution cannot be discretised (e.g., because no finite maximum has been specified or parameters are uncertain). If FALSE then any distribution that cannot be discretised will be returned as is.
remove_trailing_zeros	Logical; If TRUE (default), trailing zeroes in the resulting PMF will be removed. If FALSE, trailing zeroes will be retained.
...	ignored

Value

A ⁠<dist_spec>⁠ where all distributions with constant parameters are nonparametric.

Methodological details

The probability mass function is computed using the {primarycensored} package, which provides double censored PMF calculations. This correctly represents the probability mass function of a double censored distribution arising from the difference of two censored events.

The probability mass function of the discretised probability distribution is a vector where the first entry corresponds to the integral over the (0,1] interval of the corresponding continuous distribution (probability of integer 0), the second entry corresponds to the (0,2] interval (probability mass of integer 1), the third entry corresponds to the (1, 3] interval (probability mass of integer 2), etc.

References

Charniga, K., et al. "Best practices for estimating and reporting epidemiological delay distributions of infectious diseases using public health surveillance and healthcare data", arXiv e-prints, 2024. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.48550/arXiv.2405.08841")} Park, S. W., et al., "Estimating epidemiological delay distributions for infectious diseases", medRxiv, 2024. \Sexpr[results=rd]{tools:::Rd_expr_doi("https://doi.org/10.1101/2024.01.12.24301247")} Abbott S., et al., "primarycensored: Primary Event Censored Distributions", 2025. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.5281/zenodo.13632839")}

Examples

# A fixed gamma distribution with mean 5 and sd 1.
dist1 <- Gamma(mean = 5, sd = 1, max = 20)

# An uncertain lognormal distribution with meanlog and sdlog normally
# distributed as Normal(3, 0.5) and Normal(2, 0.5) respectively
dist2 <- LogNormal(
  meanlog = Normal(3, 0.5),
  sdlog = Normal(2, 0.5),
  max = 20
)

# The maxf the sum of two distributions
discretise(dist1 + dist2, strict = FALSE)

EpiNow2 documentation built on June 17, 2026, 1:07 a.m.