AM_VnkDelta             Compute the value V(n,k), needed to caclulate
                        the eppf of a Finite Dirichlet process when the
                        prior on the component-weigts of the mixture is
                        a Dirichlet with parameter 'gamma' (i.e. when
                        unnormailized weights are distributed as
                        Gamma(gamma,1) ) when the number of component
                        are fixed to 'M^*', i.e. a Dirac prior
                        assigning mass only to 'M^*' is assumed. See
                        Section 9.1.1 of the Paper Argiento de Iorio
                        2019 for more details.
AM_VnkNegBin            Compute the value V(n,k), needed to caclulate
                        the eppf of a Finite Dirichlet process when the
                        prior on the component-weigts of the mixture is
                        a Dirichlet with parameter 'gamma' (i.e. when
                        unnormailized weights are distributed as
                        Gamma(gamma,1) ) when the prior on the number
                        of componet is Negative Binomial with parameter
                        'r' and 'p'with mean is mu =1+ r*p/(1-p) TODO:
                        CHECK THIS FORMULA!!!. See Section 9.1.1 of the
                        Paper Argiento de Iorio 2019 for more details
AM_VnkPoisson           Compute the value V(n,k), needed to caclulate
                        the eppf of a Finite Dirichlet process when the
                        prior on the component-weigts of the mixture is
                        a Dirichlet with parameter 'gamma' (i.e. when
                        unnormailized weights are distributed as
                        Gamma(gamma,1) ) when the prior on the number
                        of componet is Shifted Poisson of parameter
                        'Lambda'. See Section 9.1.1 of the Paper
                        Argiento de Iorio 2019.
AM_clustering_estimation_average
                        Return maximum likelihood estimation (average)
                        Given a MCMC output, this function return
                        maximum likelihood estimation.
AM_clustering_estimation_laugreen
                        Return maximum likelihood estimation (laugreen)
                        Given a MCMC output, this function return
                        maximum likelihood estimation.
AM_clustering_estimation_squared_loss
                        Return maximum likelihood estimation
                        (squared_loss) Given a MCMC output, this
                        function return maximum likelihood estimation.
AM_coclustering         Return co-clustering Given a MCMC output, this
                        function return co-clustering matrix
AM_coclustering_slow    Return co-clustering slowly Given a MCMC
                        output, this function return co-clustering
                        matrix
AM_compute_stirling_ricor_abs
                        Compute the logarithm of the absolute value of
                        the generalized Sriling number of second Kind
                        (mi pare) See charambeloides, using a recursive
                        formula Devo vedere la formula
AM_compute_stirling_ricor_log
                        Compute ...
AM_emp_bayes_uninorm    this function compute the hyperparameters of an
                        Normal-Inverse-Gamma distribution using an
                        empirical Bayes approach.
AM_find_gamma_Delta     Once specified a fixed value of components
                        'M^*' this function adopt a _bisection method_
                        to find the value of 'gamma' such that the
                        induced distribution on the number of clusers
                        is centered around a user specifed value K^*,
                        i.e. the function use a bisection method to
                        solve Eq.~eq:findgamma of WE NEED TO CITE
                        ANTMAN PAPER. The user can provide a lower
                        gamma_{l} and an upper gamma_{u} bound for the
                        possible values of $gamma$. The default values
                        are gamma_l= 10^{-3} and gamma_{u}=10. A
                        default value for the tolerance is epsilon=0.1.
                        Moreover, after a maximum number of iteration
                        (default is 31), the function stops warning
                        that convergence has not bee reached.
AM_find_gamma_NegBin    Once the prior on the numbuer of mixture $M$ is
                        assumed to be a Negative Binomial Negative
                        Binomial with parameter 'r>0' and '0<p<1', with
                        mean is 1+ r*p/(1-p), this function adopt a
                        _bisection method_ to find the value of 'gamma'
                        such that the induced distribution on the
                        number of clusers is centered around a user
                        specifed value K^*, i.e. the function use a
                        bisection method to solve Eq.~eq:findgamma of
                        WE NEED TO CITE ANTMAN PAPER. The user can
                        provide a lower gamma_{l} and an upper
                        gamma_{u} bound for the possible values of
                        $gamma$. The default values are gamma_l=
                        10^{-3} and gamma_{u}=10.  A defaault value for
                        the tolerance is epsilon=0.1. Moreover, after a
                        maximum number of iteration (default is 31),
                        the function stops warning that convergence has
                        not bee reached.
AM_find_gamma_Pois      Once the prior on the numbuer of mixture $M$ is
                        assumed to be a Shifted Posson of parameter
                        'Lambda', this function adopt a _bisection
                        method_ to find the value of 'gamma' such that
                        the induced distribution on the number of
                        clusers is centered around a user specifed
                        value K^*, i.e. the function use a bisection
                        method to solve Eq.~eq:findgamma of WE NEED TO
                        CITE ANTMAN PAPER. The user can provide a lower
                        gamma_{l} and an upper gamma_{u} bound for the
                        possible values of $gamma$. The default values
                        are gamma_l= 10^{-3} and gamma_{u}=10. A
                        defaault value for the tolerance is
                        epsilon=0.1. Moreover, after a maximum number
                        of iteration (default is 31), the function
                        stops warning that convergence has not bee
                        reached.
AM_mcmc_fit             Performs a Gibbs sampling
AM_mcmc_output          S3 class AM_mcmc_output.
AM_mcmc_parameters      MCMC Parameters
AM_mix_components_prior_dirac
                        Generate a configuration object that contains a
                        Point mass prior.
AM_mix_components_prior_negbin
                        Negative Binomial Prior.
AM_mix_components_prior_pois
                        Generate a configuration object for a Poisson
                        prior on the number of mixture components.
AM_mix_weights_prior_gamma
                        Generate a configuration object to specify a
                        prior on the hyper-parameter gamma for the
                        Dirichlet prior on the mixture weights.
AM_multiber_mix_hyperparams
                        Multivariate Bernoulli Mixture Hyperparameters
                        (Latent Class analysis)
AM_multinorm_mix_hyperparams
                        Multivariate Normal Mixture Hyperparameters.
AM_plot_similarity_matrix
                        Plot the Similarity Matrix Given a MCMC output,
                        this function will produce an image of the
                        Similarity Matrix
AM_prior_K_Delta        This function compute the prior on the number
                        of cluster, i.e. occupied component of the
                        mixutre for a Finite Dirichlet process when the
                        prior on the component-weigts of the mixture is
                        a Dirichlet with parameter 'gamma' (i.e. when
                        unnormailized weights are distributed as
                        Gamma(gamma,1) ) when the number of component
                        are fixed to 'M^*', i.e. a Dirac prior
                        assigning mass only to 'M^*' is assumed. See
                        Section 9.1.1 of the Paper Argiento de Iorio
                        2019 for more details.#' There are no default
                        values.
AM_prior_K_NegBin       This function compute the prior on the number
                        of cluster, i.e. occupied component of the
                        mixutre for a Finite Dirichlet process when the
                        prior on the component-weigts of the mixture is
                        a Dirichlet with parameter 'gamma' (i.e. when
                        unnormailized weights are distributed as
                        Gamma(gamma,1) ) when the prior on the number
                        of componet is Negative Binomial with parameter
                        'r>0' and '0<p<1', with mean is mu =1+
                        r*p/(1-p) TODO: CHECK THIS FORMULA!!!. See
                        Section 9.1.1 of the Paper Argiento de Iorio
                        2019 for more details.
AM_prior_K_Pois         This function compute the prior on the number
                        of cluster, i.e. occupied component of the
                        mixutre for a Finite Dirichlet process when the
                        prior on the component-weigts of the mixture is
                        a Dirichlet with parameter 'gamma' (i.e. when
                        unnormailized weights are distributed as
                        Gamma(gamma,1) ) when the prior on the number
                        of componet is Shifted Poisson of parameter
                        'Lambda'. See Section 9.1.1 of Argiento de
                        Iorio (2019) for more details.
AM_unibin_mix_hyperparams
                        Univariate Binomial Mixture Hyperparameters.
                        Generate a configuration object that specifies
                        the prior hyperparameters for a mixture of
                        Univariate Binomial kernels wth probability of
                        success tau and size N. The conjugate prior on
                        tau is a Beta distribution: pi(tau\mid
                        alpha,beta)=\frac{Gamma(alpha+beta)}{Gamma(alpha)Gamma(beta)}
                        tau^{alpha-1}<=ft( 1-tau \right)^{beta-1} ,
                        \qquad 0<=tau<=1 N is fixed by the user and
                        should always be specified. Here, alpha
                        corresponds to 'a0',beta to 'b0'. The default
                        for the prior hyperparameters is a0=1, b0=1.
AM_uninorm_mix_hyperparams
                        Univariate Normal Mixture Hyperparameters
AM_unipois_mix_hyperparams
                        Univariate Poisson Mixture Hyperparameters.
AntMAN                  AntMAN: A package for fitting Finite Bayesian
                        Mixture model with random number of component.
brain                   Teen Brain Images from the National Institutes
                        of Health, U.S.
carcinoma               carcinoma The carcinoma data from Agresti
                        (2002, 542) consist of seven dichotomous
                        variables that represent the ratings by seven
                        pathologists of 118 slides on the presence or
                        absence of carcinoma. The purpose of studying
                        these data is to model "interobserver
                        agreement" by examining how subjects might be
                        divided into groups depending upon the
                        consistency of their diagnoses.
galaxy                  Galaxy velocities dataset
plot.AM_mcmc_output     plot AM_mcmc_output
said                    Usage frequency of the word said in the Brown
                        corpus.
summary.AM_mcmc_output
                        summary AM_mcmc_output Print some useful
                        informations about the mcmc results
