EM_phylo: A montecarlo EM algorithm implementation to diversification...
In franciscorichter/dmea: Diversification and Macro-Evolution Analysis

1	EM_phylo(wt, init_par, n_trees = 100, n_it = 30, printpar = TRUE, mu = NULL, impsam = FALSE, dummy = 0)

`wt`	waiting times of the (extant only) phylogenetic tree
`init_par`	Initial parameters for the EM algorithm
`n_trees`	Number of trees to be samplead for the montecarlo algorithm at each EM iteration
`n_it`	Number of iterations for the EM algorithm (this should be replaced by a tolerance value and a stoping criteria)
`printpar`	if printpar=TRUE then the EM estimations will be printed at each iteration
`mu`	You can use this in case you do not want to estimate mu
`impsam`	if impsam=TRUE then an importance sampling approach will be used on the montecarlo sampling
`dummy`

##---- Should be DIRECTLY executable !! ----
##-- ==>  Define data, use random,
##--	or do  help(data=index)  for the standard data sets.

## The function is currently defined as
function (wt, init_par, n_trees = 100, n_it = 30, printpar = TRUE,
    mu = NULL, impsam = FALSE, dummy = 0)
{
    pars = init_par
    for (j in 1:n_it) {
        if (printpar)
            print(pars)
        trees <- sim_srt(wt = wt, pars = pars, parallel = F,
            n_trees = n_trees)
        if (length(init_par) == 3) {
            pars = subplex(par = c(8, 0.175, 0.9), fn = llik_st,
                setoftrees = trees, impsam = impsam)$par
        }
        else {
            pars = subplex(par = c(8, 0.175), mu2par = mu, fn = llik_st,
                setoftrees = trees, impsam = impsam)$par
        }
        pars[3] = pars[3] + dummy
    }
    return(pars)
  }