mcEM: Monte-Carlo Expectation Maximization algorithm for...

In franciscorichter/emphasisR: Expectation Maximization on PHylogenetic Applications with Simulations and Importance Sampling

Description

MCEM routine with fixed sampling size

Usage

mcEM(brts, 
     pars, 
     sample_size, 
     model, 
     soc, 
     tol,
     burnin,
     print_process,
     paralell,
     cores)

Arguments

brts	branching times of the phylogenetic tree to be analyzed
pars	initial parameters for the MCEM routine
sample_size	Monte Carlo sample size
model	Species diversification model
soc	Initial number of species
tol	Tolerance level for the loglikelihood
burnin	Number of iterations to drop before performing MCEM for estimation
print_process	print loglikelihood estimation at every MCEM iteration
parallel	TRUE to perform the mcE step in paralell computing
cores	Number of cores to use in case of using parallel computing

Value

mcem	MCEM chain with parameter values, loglikelihood estimation and sample size at every iteration

Author(s)

Francisco Richter

References

[1] Richter, F., Haegeman, B., Etienne, R. S., & Wit, E. C. (2020). Introducing a general class of species diversification models for phylogenetic trees. Statistica Neerlandica.

franciscorichter/emphasisR documentation built on Dec. 20, 2021, 8:50 a.m.