The following classes in CNPBayes are deprecated and are provided only for compatability.

`component`

The component densities.

`overall`

The overall (marginal across batches and components) estimate of the density.

`modes`

A numeric vector providing the estimated modes in the overall density. The modes are defined by a crude estimate of the first derivative of the overall density (see

`findModes`

).`data`

A numeric vector containing the data

`clusters`

A vector providing the k-means clustering of the component means using the modes as centers. If an object of class

`DensityModel`

is instantiated with`merge=FALSE`

, this slot takes values 1, ..., K, where K is the number of components.`k`

An integer value specifying the number of latent classes.

`hyperparams`

An object of class 'Hyperparameters' used to specify the hyperparameters of the model.

`theta`

the means of each component and batch

`sigma2`

the variances of each component and batch

`nu.0`

the shape parameter for sigma2

`sigma2.0`

the rate parameter for sigma2

`pi`

mixture probabilities which are assumed to be the same for all batches

`mu`

overall mean

`tau2`

overall variance

`data`

the data for the simulation.

`data.mean`

the empirical means of the components

`data.prec`

the empirical precisions

`z`

latent variables

`zfreq`

table of latent variables

`probz`

n x k matrix of probabilities

`logprior`

log likelihood of prior: log(p(sigma2.0)p(nu.0)p(mu))

`loglik`

log likelihood:

*∑ p_k Φ(θ_k, σ_k)*`mcmc.chains`

an object of class 'McmcChains' to store MCMC samples

`batch`

a vector of the different batch numbers

`batchElements`

a vector labeling from which batch each observation came from

`modes`

the values of parameters from the iteration which maximizes log likelihood and log prior

`mcmc.params`

An object of class 'McmcParams'

`label_switch`

length-one logical vector indicating whether label-switching occurs (possibly an overfit model)

`.internal.constraint`

Constraint on parameters. For internal use only.

`k`

An integer value specifying the number of latent classes.

`hyperparams`

An object of class 'Hyperparameters' used to specify the hyperparameters of the model.

`theta`

the means of each component and batch

`sigma2`

the variances of each component and batch

`nu.0`

the shape parameter for sigma2

`sigma2.0`

the rate parameter for sigma2

`pi`

mixture probabilities which are assumed to be the same for all batches

`mu`

means from batches, averaged across batches

`tau2`

variances from batches, weighted by precisions

`data`

the data for the simulation.

`data.mean`

the empirical means of the components

`data.prec`

the empirical precisions

`z`

latent variables

`zfreq`

table of latent variables

`probz`

n x k matrix of probabilities

`logprior`

log likelihood of prior: log(p(sigma2.0)p(nu.0)p(mu))

`loglik`

log likelihood:

*∑ p_k Φ(θ_k, σ_k)*`mcmc.chains`

an object of class 'McmcChains' to store MCMC samples

`batch`

a vector of the different batch numbers

`batchElements`

a vector labeling from which batch each observation came from

`modes`

the values of parameters from the iteration which maximizes log likelihood and log prior

`label_switch`

length-one logical vector indicating whether label-switching occurs (possibly an overfit model)

`mcmc.params`

An object of class 'McmcParams'

`.internal.constraint`

Constraint on parameters. For internal use only.

`k`

An integer value specifying the number of latent classes.

`hyperparams`

An object of class 'Hyperparameters' used to specify the hyperparameters of the model.

`theta`

the means of each component and batch

`sigma2`

the variances of each component and batch

`nu.0`

the shape parameter for sigma2

`sigma2.0`

the rate parameter for sigma2

`pi`

mixture probabilities which are assumed to be the same for all batches

`mu`

means from batches, averaged across batches

`tau2`

variances from batches, weighted by precisions

`data`

the data for the simulation.

`data.mean`

the empirical means of the components

`data.prec`

the empirical precisions

`z`

latent variables

`zfreq`

table of latent variables

`probz`

n x k matrix of probabilities

`logprior`

log likelihood of prior: log(p(sigma2.0)p(nu.0)p(mu))

`loglik`

log likelihood:

*∑ p_k Φ(θ_k, σ_k)*`mcmc.chains`

an object of class 'McmcChains' to store MCMC samples

`batch`

a vector of the different batch numbers

`batchElements`

a vector labeling from which batch each observation came from

`modes`

the values of parameters from the iteration which maximizes log likelihood and log prior

`label_switch`

length-one logical vector indicating whether label-switching occurs (possibly an overfit model)

`mcmc.params`

An object of class 'McmcParams'

`.internal.constraint`

Constraint on parameters. For internal use only.

`k`

An integer value specifying the number of latent classes.

`hyperparams`

An object of class 'Hyperparameters' used to specify the hyperparameters of the model.

`theta`

the means of each component and batch

`sigma2`

the variances of each component and batch

`nu.0`

the shape parameter for sigma2

`sigma2.0`

the rate parameter for sigma2

`pi`

mixture probabilities which are assumed to be the same for all batches

`mu`

overall mean

`tau2`

overall variance

`data`

the data for the simulation.

`data.mean`

the empirical means of the components

`data.prec`

the empirical precisions

`z`

latent variables

`zfreq`

table of latent variables

`probz`

n x k matrix of probabilities

`logprior`

log likelihood of prior: log(p(sigma2.0)p(nu.0)p(mu))

`loglik`

log likelihood:

*∑ p_k Φ(θ_k, σ_k)*`mcmc.chains`

an object of class 'McmcChains' to store MCMC samples

`batch`

a vector of the different batch numbers

`batchElements`

a vector labeling from which batch each observation came from

`modes`

the values of parameters from the iteration which maximizes log likelihood and log prior

`mcmc.params`

An object of class 'McmcParams'

`label_switch`

length-one logical vector indicating whether label-switching occurs (possibly an overfit model)

`.internal.constraint`

Constraint on parameters. For internal use only.

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