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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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2023.3.8
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install.packages('BNPdensity')
Monthly Downloads
367
Version
2023.3.8
License
GPL (>= 2)
Maintainer
Guillaume Kon Kam King
Last Published
March 24th, 2023
Functions in BNPdensity (2023.3.8)
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MixNRMI1cens
Normalized Random Measures Mixture of Type I for censored data
Mv
Continuous Jump heights function
MixPY2
Pitman-Yor process mixture of Type II
MvInv
Invert jump heights function
MixPY1
Pitman-Yor process mixture of Type I
add
Add x and y
acidity
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
MixNRMI2
Normalized Random Measures Mixture of Type II
MixNRMI2cens
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
dt_
Non-standard student-t density
dhalfcauchy
Density half Cauchy
dkcens2_1val
Density evaluation once
dhalfnorm
Density half normal
fcondYZXA
Conditional posterior distribution of the bivariate latents (Y,Z)
censor_code_rl
Censor code right-left
fcondYXAcens2
Conditional posterior distribution of the latents Y in the censoring case
cpo.NRMI1
Extract the Conditional Predictive Ordinates (CPOs) from a fitted object
cpo.NRMI2
Extract the Conditional Predictive Ordinates (CPOs) from a fitted object
dhalft
Density half Student-t
fcondXA2
Conditional density evaluation in the fully nonparametric model
fcondXA
Conditional density evaluation in the semiparametric model
grid_from_data_censored
Create a plotting grid from censored data.
dk
Kernel density function
comp1
Ties function: univariate
cpo
Conditional predictive ordinate function
dkcens2
Density of the chosen kernel
dtnorm
Density truncated normal
fcondXA2cens2
Conditional density evaluation in the fully nonparametric model for censored data
fcondYXA
Conditional posterior distribution of the latents Y
grid_from_data_noncensored
Create a plotting grid from non-censored data.
gs4
Resampling Ystar function
gs3
Conditional posterior distribution of latent U
compute_optimal_clustering
Compute the optimal clustering from an MCMC sample
comp2
Ties function: bivariate
fcondYZXAcens2
Conditional posterior distribution of the bivariate latents (Y,Z) in the case of censoring
gs4cens2
Resampling Ystar function in the case of censoring
expected_number_of_components_Dirichlet
Computes the expected number of components for a Dirichlet process.
cpo.multNRMI
Extract the Conditional Predictive Ordinates (CPOs) from a list of fitted objects
galaxy
Galaxy Data Set
give_kernel_name
Gives the kernel name from the integer code
dist_name_k_index_converter
Convert distribution names to indices
enzyme
Enzyme Dataset
expected_number_of_components_stable
Computes the expected number of components for a stable process.
grid_from_data
Create a plotting grid from censored or non-censored data.
plot.NRMI2
Plot the density estimate and the 95% credible interval
gs5cens2
Conditional posterior distribution of sigma in the case of censoring
gs5
Conditional posterior distribution of sigma
fill_sigmas
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.
multMixNRMI1
Multiple chains of MixNRMI1
plot.NRMI1
Plot the density estimate and the 95% credible interval
pp_plot_noncensored
Plot the percentile-percentile graph for non censored data.
plot.PY1
Plot the density estimate and the 95% credible interval
gs3_adaptive3
Conditional posterior distribution of latent U
phalft
Distribution function half Student-t
print.NRMI1
S3 method for class 'MixNRMI1'
gsHP
Updates the hyper-parameters of py0
logf_u_cond_y
Target logdensity of U given the data
gsYZstarcens2
Jointly resampling Ystar and Zstar function in the case of censoring
logdprop_logu
Contribution of the proposal kernel logdensity to the Metropolis-Hastings ratio
is_censored
Test if the data is censored
gsYZstar
Jointly resampling Ystar and Zstar function
logacceptance_ratio_logu
Metropolis-Hastings ratio for the conditional of logU
gs3_log
Conditional posterior distribution of latent logU
logf_logu_cond_y
Contribution of the target logdensity of logU to the Metropolis-Hastings ratio
print.PY2
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.
qhalft
Quantile function half Student-t
multMixNRMI2cens
Multiple chains of MixNRMI2cens
plotPDF_censored
Plot the density for censored data.
multMixNRMI2
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.
print.PY1
S3 method for class 'PY1'
print.NRMI2
S3 method for class 'MixNRMI2'
pk
Kernel distribution function
multMixNRMI1cens
Multiple chains of MixNRMI1cens
thresholdGG
Choosing the truncation level for the NGG process
print.multNRMI
S3 method for class 'multNRMI'
plotCDF_censored
Plot the Turnbull CDF and fitted CDF for censored data.
plot.multNRMI
Plot the density estimate and the 95% credible interval
plotfit_noncensored
Plot the density estimate and the 95% credible interval for noncensored data
rk
Kernel density sampling function
summary.NRMI2
S3 method for class 'MixNRMI2'
qhalfcauchy
Quantile function half Cauchy
qhalfnorm
Quantile function half Normal
qtnorm
Quantile function truncated normal
qq_plot_censored
Plot the quantile-quantile graph for censored data.
p0
Centering function
phalfcauchy
Distribution function half Cauchy
rfyzstarcens2
Conditional posterior distribution of the distinct vectors (Ystar,Zstar) in the case of censoring
pt_
Distribution function non-standard student-t
process_dist_name
Process the distribution name argument into a distribution index
plot.PY2
Plot the density estimate and the 95% credible interval
phalfnorm
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.
ptnorm
Distribution function truncated normal
rhalfnorm
Random number generator half Normal
qgeneric
Generic function to find quantiles of a distribution
summary.multNRMI
S3 method for class 'multNRMI'
summary.PY2
S3 method for class 'PY2'
rfyzstar
Conditional posterior distribution of the distinct vectors (Ystar,Zstar)
rprop_logu
Proposal distribution for logU
rfystarcens2
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.
rfystar
Conditional posterior distribution of the distinct Ystar
salinity
Salinity tolerance
qt_
Quantile function non-standard Student-t
rtnorm
Random number generator for a truncated normal distribution
summarytext
Common text for the summary S3 methods
rt_
Random number generator non-standard Student-t
rhalfcauchy
Random number generator half Cauchy
summary.NRMI1
S3 method for class 'MixNRMI1'
rhalft
Random number generator half Student-t
summary.PY1
S3 method for class 'PY1'
traceplot
Draw a traceplot for multiple chains
GOFplots
Plot Goodness of fits graphical checks for censored data
MixNRMI1
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
Galaxy1.out
Fit of MixNRMI1 function to the galaxy dataset
Enzyme2.out
Fit of MixNRMI2 function to the enzyme dataset
Galaxy2.out
Fit of MixNRMI2 function to the galaxy dataset
BNPdensity-package
Bayesian nonparametric density estimation
Enzyme1.out
Fit of MixNRMI1 function to the enzyme dataset