CensoredDistributions.jl

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Frequently Asked Questions ​

This page contains frequently asked questions about CensoredDistributions.jl. If you have a question that is not answered here, please open a discussion on the GitHub repository.

Working with Tutorials ​

Q: How can I run the tutorial notebooks? ​

A: You have two options:

Option 1: Copy and paste (easiest)

  • Copy code blocks from the online tutorials

  • Paste into your Julia REPL or script

  • Modify as needed for your analysis

Option 2: Run tutorial scripts directly

  1. Clone the repository: git clone https://github.com/EpiAware/CensoredDistributions.jl.git

  2. Start Julia in the repository directory: julia --project=docs

  3. Run a tutorial: include("docs/src/getting-started/tutorials/analytical-primarycensored-cdfs.jl")

The tutorial .jl files are plain Julia scripts that can be run top-to-bottom in the REPL or executed as scripts.

Using the Package ​

Q: How do I create a primary censored distribution? ​

A: Use the primary_censored function:

julia
using CensoredDistributions, Distributions

# Delay distribution (e.g., incubation period)
delay_dist = Gamma(2, 3)

# Primary event distribution (e.g., infection within a day)
primary_dist = Uniform(0, 1)

# Create censored distribution
censored_dist = primary_censored(delay_dist, primary_dist)
CensoredDistributions.PrimaryCensored{Distributions.Gamma{Float64}, Distributions.Uniform{Float64}, CensoredDistributions.AnalyticalSolver{Integrals.QuadGKJL{typeof(LinearAlgebra.norm), Nothing}}}(
dist: Distributions.Gamma{Float64}(α=2.0, θ=3.0)
primary_event: Distributions.Uniform{Float64}(a=0.0, b=1.0)
method: CensoredDistributions.AnalyticalSolver{Integrals.QuadGKJL{typeof(LinearAlgebra.norm), Nothing}}(Integrals.QuadGKJL{typeof(LinearAlgebra.norm), Nothing}(7, LinearAlgebra.norm, nothing))
)

Q: What's the difference between the different types of censoring? ​

A:

  • Primary event censoring: The timing of the initial event in a delay distribution is uncertain. See the tutorials for detailed examples.

  • Interval censoring: Continuous values are observed only within discrete intervals. See the API documentation for interval_censored.

  • Double interval censoring: Combines both types of censoring. See double_interval_censored in the API documentation.

Q: How do I fit censored distributions to data? ​

A: See the Fitting with Turing.jl tutorial in the tutorials section for Bayesian inference examples using Turing.jl.

Q: Which distributions have analytical solutions for better performance? ​

A: See the Analytical CDF Solutions tutorial in the tutorials section for details on which distribution combinations have optimized implementations.

Q: Can I use this with automatic differentiation? ​

A: Yes! The package supports ForwardDiff.jl, ReverseDiff.jl, Zygote.jl, and Enzyme.jl for use with Turing.jl and other AD-based workflows.

Common Issues ​

Q: I get "Package not found" errors ​

A: Make sure you're in the right environment:

julia
using Pkg
Pkg.activate(".")           # Activate current directory
Pkg.instantiate()           # Install dependencies
Pkg.add("CensoredDistributions")  # Add the package if needed

Q: How do I cite this package? ​

A: Please cite the GitHub repository and mention the version you used. Citation information for the associated paper will be added when available.

Q: I want to contribute to development ​

A: See the Developer FAQ and Contributing Guide for development-specific questions and guidelines.

Getting Help ​

Still have questions?