FirstPassageTools is a Julia package for setting up first-passage time distributions for continuous-time, discrete-state Markov processes. The first-passage time distributions can then be fit to empirical first-passage time data using Turing.jl.

FirstPassageTools.jl can be installed from Juila's central package repository:

julia> ]
pkg> add FirstPassageTools

FirstPassageTools.jl can also be installed directly from Github in the Julia REPL:

julia> ]
pkg> add https://github.com/smith-garrett/FirstPassageTools.jl

To set up a first-passage time distribution, one needs to provide two transition rate matrices and a vector with the initial probability distribution over the transient states (p0 in the example below). The first matrix (T below) determines the transition rates between transient states of the system. The second determines the transition rates from the transient states to one or more absorbing states (A below). The rows of the transient matrix T should sum to the values given in the absorbing matrix. For both matrices, the $i,j$-th entry should provide the transition rate from state $j$ to state $i$.

Currently, the most complete functionality is available for the first-passage time to any absorbing state by time t. When a system has more than one absorbing state, one might be interested in the conditional first-passage time to reach a particular absorbing state before all others. Some methods are implemented for this, but not everything.

For a single transient state and a single absorbing state, the first-passage time distribution is equivalent to the exponential distribution:

julia> using FirstPassageTools
julia> T = [-1.0;;]  # transition rate matrices need to be 2-dimensional
julia> A = [1.0;;]
julia> p0 = [1.0]
julia> fp = fpdistribution(T, A, p0)

Available methods for first-passage time distributions include mean(), var(), rand(), pdf(), logpdf(), cdf(), and quantile(). From here, the transition rates of the distribution can be fit to data. See the notebooks directory for additional tutorials and parameter recovery exercises.

Because the first-passage time distributions here are sub-types of the continuous univariate distribution fromDistributions.jl, plotting functions from StatsPlots.jl work out of the box. For example, to plot the probability density function and the cumulative distribution function from the above sample, you can run:

julia> using Plots, StatsPlots
julia> plot(fp, label="PDF")
julia> plot!(fp, func=cdf, label="CDF")

Additional examples are provided in the notebooks directory. Verification of correct sampling, at least for certain statistical models, is provided in the SimulationBasedCalibration.jl script in the notebooks directory.