ppp_next_n

Simulate n events from a homogeneous Poisson Point Process.

Simulates events from one dimensional nonhomogeneous Poisson point processes (NHPPPs) as per Trikalinos and Sereda (2024, <doi:10.48550/arXiv.2402.00358> and 2024, <doi:10.1371/journal.pone.0311311>). Functions are based on three algorithms that provably sample from a target NHPPP: the time-transformation of a homogeneous Poisson process (of intensity one) via the inverse of the integrated intensity function (Cinlar E, "Theory of stochastic processes" (1975, ISBN:0486497996)); the generation of a Poisson number of order statistics from a fixed density function; and the thinning of a majorizing NHPPP via an acceptance-rejection scheme (Lewis PAW, Shedler, GS (1979) <doi:10.1002/nav.3800260304>).

Thomas Trikalinos

nhppp

Simulating Nonhomogeneous Poisson Point Processes

Yuliia Sereda

ppp_next_n function

<dl><dt>n</dt>
<dd>scalar number of samples</dd>
<dt>rate</dt>
<dd>scalar instantaneous rate</dd>
<dt>t_min</dt>
<dd>scalar for the starting time value</dd>
<dt>rng_stream</dt>
<dd><a href="https://lifecycle.r-lib.org/articles/stages.html#deprecated"><img src="figures/lifecycle-deprecated.svg?package=nhppp&version=1.0.2" alt="[Deprecated]"></a> an <code>rstream</code> object</dd></dl>

Arguments

Simulate n events from a homogeneous Poisson Point Process. — ppp_next_n

<dl>

<dt>n</dt>
<dd>scalar number of samples</dd>


<dt>rate</dt>
<dd>scalar instantaneous rate</dd>


<dt>t_min</dt>
<dd>scalar for the starting time value</dd>


<dt>rng_stream</dt>
<dd><a href='https://lifecycle.r-lib.org/articles/stages.html#deprecated'><img src='figures/lifecycle-deprecated.svg' alt='[Deprecated]' /></a> an <code>rstream</code> object</dd>

</dl>

ppp_next_n: Simulate n events from a homogeneous Poisson Point Process.

Description

Usage

Value

Arguments

Examples