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BNPdensity (version 2023.3.8)

Ferguson-Klass Type Algorithm for Posterior Normalized Random Measures

Description

Bayesian nonparametric density estimation modeling mixtures by a Ferguson-Klass type algorithm for posterior normalized random measures.

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Install

install.packages('BNPdensity')

Monthly Downloads

382

Version

2023.3.8

License

GPL (>= 2)

Maintainer

Guillaume Kon Kam King

Last Published

March 24th, 2023

Functions in BNPdensity (2023.3.8)

Normalized Random Measures Mixture of Type I for censored data

Continuous Jump heights function

Pitman-Yor process mixture of Type II

Invert jump heights function

Pitman-Yor process mixture of Type I

Acidity Index Dataset

asNumeric_no_warning

If the function Rmpfr::asNumeric returns a warning about inefficiency, silence it.

as.mcmc.multNRMI

Convert the output of multMixNRMI into a coda mcmc object

Normalized Random Measures Mixture of Type II

Normalized Random Measures Mixture of Type II for censored data

compute_thinning_grid

Compute the grid for thinning the MCMC chain

convert_to_mcmc

Convert the output of multMixNRMI into a coda mcmc object

comment_on_NRMI_type

Comment on the NRMI process depending on the value of the parameters

cens_data_check

Censoring data check

Non-standard student-t density

Density half Cauchy

Density evaluation once

Density half normal

Conditional posterior distribution of the bivariate latents (Y,Z)

Censor code right-left

Conditional posterior distribution of the latents Y in the censoring case

Extract the Conditional Predictive Ordinates (CPOs) from a fitted object

Extract the Conditional Predictive Ordinates (CPOs) from a fitted object

Density half Student-t

Conditional density evaluation in the fully nonparametric model

Conditional density evaluation in the semiparametric model

grid_from_data_censored

Create a plotting grid from censored data.

Kernel density function

Ties function: univariate

Conditional predictive ordinate function

Density of the chosen kernel

Density truncated normal

Conditional density evaluation in the fully nonparametric model for censored data

Conditional posterior distribution of the latents Y

grid_from_data_noncensored

Create a plotting grid from non-censored data.

Resampling Ystar function

Conditional posterior distribution of latent U

compute_optimal_clustering

Compute the optimal clustering from an MCMC sample

Ties function: bivariate

Conditional posterior distribution of the bivariate latents (Y,Z) in the case of censoring

Resampling Ystar function in the case of censoring

expected_number_of_components_Dirichlet

Computes the expected number of components for a Dirichlet process.

Extract the Conditional Predictive Ordinates (CPOs) from a list of fitted objects

Galaxy Data Set

give_kernel_name

Gives the kernel name from the integer code

dist_name_k_index_converter

Convert distribution names to indices

expected_number_of_components_stable

Computes the expected number of components for a stable process.

Create a plotting grid from censored or non-censored data.

Plot the density estimate and the 95% credible interval

Conditional posterior distribution of sigma in the case of censoring

Conditional posterior distribution of sigma

Repeat the common scale parameter of a semiparametric model to match the dimension of the location parameters.

plotCDF_noncensored

Plot the empirical and fitted CDF for non censored data.

Multiple chains of MixNRMI1

Plot the density estimate and the 95% credible interval

pp_plot_noncensored

Plot the percentile-percentile graph for non censored data.

Plot the density estimate and the 95% credible interval

Conditional posterior distribution of latent U

Distribution function half Student-t

S3 method for class 'MixNRMI1'

Updates the hyper-parameters of py0

Target logdensity of U given the data

Jointly resampling Ystar and Zstar function in the case of censoring

Contribution of the proposal kernel logdensity to the Metropolis-Hastings ratio

Test if the data is censored

Jointly resampling Ystar and Zstar function

logacceptance_ratio_logu

Metropolis-Hastings ratio for the conditional of logU

Conditional posterior distribution of latent logU

logf_logu_cond_y

Contribution of the target logdensity of logU to the Metropolis-Hastings ratio

S3 method for class 'PY2'

is_semiparametric

Tests if a fit is a semi parametric or nonparametric model.

plotPDF_noncensored

Plot the density and a histogram for non censored data.

Quantile function half Student-t

multMixNRMI2cens

Multiple chains of MixNRMI2cens

plotPDF_censored

Plot the density for censored data.

Multiple chains of MixNRMI2

pp_plot_censored

Plot the percentile-percentile graph for non censored data, using the Turnbull estimator the position of the percentiles.

S3 method for class 'PY1'

S3 method for class 'MixNRMI2'

Kernel distribution function

multMixNRMI1cens

Multiple chains of MixNRMI1cens

Choosing the truncation level for the NGG process

S3 method for class 'multNRMI'

plotCDF_censored

Plot the Turnbull CDF and fitted CDF for censored data.

Plot the density estimate and the 95% credible interval

plotfit_noncensored

Plot the density estimate and the 95% credible interval for noncensored data

Kernel density sampling function

S3 method for class 'MixNRMI2'

Quantile function half Cauchy

Quantile function half Normal

Quantile function truncated normal

qq_plot_censored

Plot the quantile-quantile graph for censored data.

Centering function

Distribution function half Cauchy

Conditional posterior distribution of the distinct vectors (Ystar,Zstar) in the case of censoring

Distribution function non-standard student-t

process_dist_name

Process the distribution name argument into a distribution index

Plot the density estimate and the 95% credible interval

Distribution function half Normal

plot_clustering_and_CDF

Plot the clustering and the Cumulative Distribution Function

qq_plot_noncensored

Plot the quantile-quantile graph for non censored data.

Distribution function truncated normal

Random number generator half Normal

Generic function to find quantiles of a distribution

summary.multNRMI

S3 method for class 'multNRMI'

S3 method for class 'PY2'

Conditional posterior distribution of the distinct vectors (Ystar,Zstar)

Proposal distribution for logU

Conditional posterior distribution of the distinct Ystar in the case of censoring

plotfit_censored

Plot the density estimate and the 95% credible interval for censored data

plot_prior_number_of_components

This plots the prior distribution on the number of components for the stable process. The Dirichlet process is provided for comparison.

Conditional posterior distribution of the distinct Ystar

Salinity tolerance

Quantile function non-standard Student-t

Random number generator for a truncated normal distribution

Common text for the summary S3 methods

Random number generator non-standard Student-t

Random number generator half Cauchy

S3 method for class 'MixNRMI1'

Random number generator half Student-t

S3 method for class 'PY1'

Draw a traceplot for multiple chains

Plot Goodness of fits graphical checks for censored data

Normalized Random Measures Mixture of Type I

GOFplots_censored

Plot Goodness of fits graphical checks for censored data

GOFplots_noncensored

Plot Goodness of fits graphical checks for non censored data

Fit of MixNRMI1 function to the galaxy dataset

Fit of MixNRMI2 function to the enzyme dataset

Fit of MixNRMI2 function to the galaxy dataset

BNPdensity-package

Bayesian nonparametric density estimation

Fit of MixNRMI1 function to the enzyme dataset