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PReMiuM (version 3.2.13)

Dirichlet Process Bayesian Clustering, Profile Regression

Description

Bayesian clustering using a Dirichlet process mixture model. This model is an alternative to regression models, non-parametrically linking a response vector to covariate data through cluster membership. The package allows Bernoulli, Binomial, Poisson, Normal, survival and categorical response, as well as Normal and discrete covariates. It also allows for fixed effects in the response model, where a spatial CAR (conditional autoregressive) term can be also included. Additionally, predictions may be made for the response, and missing values for the covariates are handled. Several samplers and label switching moves are implemented along with diagnostic tools to assess convergence. A number of R functions for post-processing of the output are also provided. In addition to fitting mixtures, it may additionally be of interest to determine which covariates actively drive the mixture components. This is implemented in the package as variable selection. The main reference for the package is Liverani, Hastie, Azizi, Papathomas and Richardson (2015) .

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Version

Install

install.packages('PReMiuM')

Monthly Downloads

740

Version

3.2.13

License

GPL-2

Maintainer

Last Published

January 9th, 2024

Functions in PReMiuM (3.2.13)

profRegr

Profile Regression
is.wholenumber

Function to check if a number is a whole number
rALD

Asymmetric Laplace Distribution
mapforGeneratedData

Map generated data
margModelPosterior

Marginal Model Posterior
plotPredictions

Plot the conditional density using the predicted scenarios
setHyperparams

Definition of characteristics of sample datasets for profile regression
vec2mat

Vector to upper triangular matrix
plotRiskProfile

Plot the Risk Profiles
simBenchmark

Benchmark for simulated examples
summariseVarSelectRho

summariseVarSelectRho
PReMiuM-package

Dirichlet Process Bayesian Clustering
calcDissimilarityMatrix

Calculates the dissimilarity matrix
computeRatioOfVariance

computeRatioOfVariance
globalParsTrace

Plot of the trace of some of the global parameters
heatDissMat

Plot the heatmap of the dissimilarity matrix
calcOptimalClustering

Calculation of the optimal clustering
calcAvgRiskAndProfile

Calculation of the average risks and profiles
clusSummaryBernoulliDiscrete

Sample datasets for profile regression
calcPredictions

Calculates the predictions
generateSampleDataFile

Generate sample data files for profile regression