estimate_multipleBERPM: Estimation of
In isci1102/ERPM:

estimate_multipleBERPM(partitions, presence.tables, nodes, objects, effects, mean.priors, sd.priors, start.chains = NULL, burnin.1 = 30, thining.1 = 10, num.chains = 3, length.chains = 1000, burnin.2 = 30, neighborhood.partition = c(0.7, 0.3, 0, 0, 0, 0), neighborhood.augmentation = NULL, sizes.allowed = NULL, sizes.simulated = NULL, parallel = F, cpus = 1)

`partitions`	Observed partitions.
`presence.tables`	Matrix indicating which actors were present for each observations (mandatory).
`nodes`	Data frame containing the nodes.
`objects`	Objects used for statistics calculation. A list with a vector "name" and a vector "object".
`effects`	Effects or sufficient statistics. A list with a vector "names" and a vector "objects".
`mean.priors`	Means of the normal distributions of prior parameters.
`sd.priors`	Standard deviations of the normal distributions of prior parameters.
`start.chains`
`burnin.1`
`thining.1`
`num.chains`
`length.chains`
`burnin.2`
`neighborhood.partition`
`neighborhood.augmentation`
`sizes.allowed`
`sizes.simulated`
`parallel`
`cpus`

##---- 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 (partitions, presence.tables, nodes, objects, effects,
    mean.priors, sd.priors, start.chains = NULL, burnin.1 = 30,
    thining.1 = 10, num.chains = 3, length.chains = 1000, burnin.2 = 30,
    neighborhood.partition = c(0.7, 0.3, 0, 0, 0, 0), neighborhood.augmentation = NULL,
    sizes.allowed = NULL, sizes.simulated = NULL, parallel = F,
    cpus = 1)
{
    num.effects <- length(effects$names)
    z.obs <- rowSUms(computeStatistics_multiple(partitions, presence.tables,
        nodes, effects, objects))
    print("Observed statistics")
    print(z.obs)
    print("Burn-in")
    print(burnin.1)
    print("Thining")
    print(thining.1)
    if (is.null(start.chains)) {
        start.chains <- list()
        for (p in 1:num.effects) {
            start.chains[[p]] <- rnorm(num.effects, mean = mean.priors,
                sd = sd.priors)
        }
    }
    if (is.null(neighborhood.augmentation)) {
        neighborhood.augmentation <- rep(0.1, num.effects)
    }
    results_exchange <- draw_exchangealgorithm_multiple(partitions,
        z.obs, presence.tables, nodes, objects, effects, mean.priors,
        sd.priors, start.chains, burnin.1, thining.1, num.chains,
        length.chains, burnin.2, neighborhood.partition, neighborhood.augmentation,
        sizes.allowed, sizes.simulated, parallel, cpus)
    results <- data.frame(effect = effects$names, object = effects$objects,
        post.mean = results_exchange$post.mean, post.sd = results_exchange$post.sd)
    print_results_bayesian(results)
    return(list(results = results, all.chains = results_exchange$all.chains))
  }