Norm Normal distribution rounded to integer values and negative Pois Poisson distribution, parameter lambda. Nbinom Negative binomial distribution, parameters n and Geom Geometric distribution, parameter p. P(x) = choose(m, x) choose(n, k-x) / choose(m n, k) In the urn), k (the number of balls drawn from the urn). Number of white balls in the urn), n (the number of white balls Hyper Hypergeometric distribution, parameters m (the All the non-negative integers between min y max have the sameīinom Binomial distribution, parameters n and p. Unif Discrete uniform distribution, parameters min and These are the univariate distributions available: The structure need it for each classification is illustrated in the examples. Independents This option is for BGMW processes where each offspringĭistribution is a joint distribution of d combined independentĭiscrete random variables, one for each type of individuals, in thisĬase, it is required as input data d^2 univariate distributions. Multinomial distribution and a d \times d matrix where each rowĬontains probabilities of the d possible outcomes for each multinomial Trials, in this case, it is required as input data, d univariateĭistributions related to the random number of trials for each Multinomial This option is for BGMW processes where each offspringĭistribution is a multinomial distribution with a random number of ![]() Respective, greater than zero, probability. Input data for each distribution, all d-dimensional vectors with their ![]() The offspring distributions, in this case, it is required as General This option is for BGWM processes without conditions over Watson process (BGWM) from its offspring distributions.įrom particular offspring distributions and taking into account aĭifferentiated algorithmic approach, we propose the following classes This function performs a simulation of a multi-type Bienayme - Galton Process with the number of individuals for every combination parent type. ![]() The name of the output file where the generated trajectory of the Generated trajectory of the process with the relative frequencies by type. Generated trajectory of the process with the number of descendents by type. Generated trajectory of the process with the number of individuals forĮvery combination parent type - descendent type. Logical value, if TRUE, the output object will include the Nonnegative integer vector of size d initial population by type. Positive integer, maximum lenght of the wanted trajectory. Of the Bienayme - Galton - Watson process (See details and examples).Ĭlass or family of the Bienayme - Galton - Watson process RBGWM ( dists, type = c ( "general", "multinomial", "independents" ), d, n, z0 = rep ( 1, d ), c.s = TRUE, tt.s = TRUE, rf.s = TRUE, file = NULL )
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