![[R logo]](../../../doc/html/Rlogo.svg)
![[Up]](../../../doc/html/left.jpg)
![[Top]](../../../doc/html/up.jpg)
| AntMAN-package | AntMAN: A package for fitting Finite Bayesian Mixture model with random number of component. |
| 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. |
| 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. |
| 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 |