Package: bhpm 1.7

Raymond Carragher

bhpm: Bayesian Hierarchical Poisson Models for Multiple Grouped Outcomes with Clustering

Bayesian hierarchical methods for the detection of differences in rates of related outcomes for multiple treatments for clustered observations. Theoretical background for the models is given in Carragher (2017) <https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.736866>. The models in this package are extensions for multiple treatments and clusters. This software was developed for the Precision Drug Theraputics: Risk Prediction in Pharmacoepidemiology project as part of a Rutherford Fund Fellowship at Health Data Research (UK), Medical Research Council (UK) award reference MR/S003967/1 (<https://gtr.ukri.org/>). Principal Investigator: Raymond Carragher.

Authors:Raymond Carragher [aut, cre]

bhpm_1.7.tar.gz
bhpm_1.7.tar.gz(r-4.5-noble)bhpm_1.7.tar.gz(r-4.4-noble)
bhpm_1.7.tgz(r-4.4-emscripten)bhpm_1.7.tgz(r-4.3-emscripten)
bhpm.pdf |bhpm.html✨
bhpm/json (API)

# Install 'bhpm' in R:
install.packages('bhpm', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

18 exports 0.00 score 2 dependencies 175 downloads

Last updated 4 years agofrom:65699e06ca. Checks:OK: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKSep 10 2024
R-4.5-linux-x86_64OKSep 10 2024

Exports:bhpm.cluster.1a.hier2bhpm.cluster.1a.hier3bhpm.cluster.BB.hier2bhpm.cluster.BB.hier3bhpm.convergence.diagbhpm.gen.initial.valuesbhpm.global.sim.param.defaultsbhpm.hyper.param.defaultsbhpm.monitor.defaultsbhpm.monitor.samplesbhpm.npmbhpm.pmbhpm.pointmass.weightsbhpm.print.convergence.summarybhpm.print.summary.statsbhpm.pthetabhpm.sim.control.paramsbhpm.summary.stats

Dependencies:codalattice

Readme and manuals

Help Manual

Help pageTopics
Bayesian Hierarchical Possion Models for Mulitple Grouped Outcomes with Clusteringbhpm-package
A Two-Level Hierarchical Model for Grouped Data with Clusters and without Point-Mass.bhpm.cluster.1a.hier2
A Three-Level Hierarchical Model for Grouped Data with Clusters and without Point-Mass.bhpm.cluster.1a.hier3
A Two-Level Hierarchical Model for grouped data and clusters with Point-Mass.bhpm.cluster.BB.hier2
A Three-Level Hierarchical Model for grouped data and clusters with Point-Mass.bhpm.cluster.BB.hier3
Cluster analysis data.bhpm.cluster.data1
Cluster analysis data.bhpm.cluster.data2
Convergence Diagnostics of the Simulationbhpm.convergence.diag
Generate a template simulation initial values.bhpm.gen.initial.values
Generate default global simulation parameters for a model.bhpm.global.sim.param.defaults
Generate default hyperparameter values for a model.bhpm.hyper.param.defaults
Generate default variable monitor list for a model.bhpm.monitor.defaults
Generate a template for choosing which samples to monitor.bhpm.monitor.samples
Cluster analysis data.bhpm.multi.treatments
Cluster analysis data.bhpm.multi.treatments.random.order
Fit a Bayesian Hierarchical Model for Grouped Data with Clusters and without Point-Mass.bhpm.npm
A Bayesian Hierarchical Model for grouped data and clusters with Point-Mass.bhpm.pm
Generate a template for the point-mass weightings.bhpm.pointmass.weights
Print a Summary of the Convergence Diagnostics of the Simulationbhpm.print.convergence.summary
Print the Summary Statistics of Posterior Distributionsbhpm.print.summary.stats
Reports the posterior probability that theta (the increase in the log-odds) is greater than zero, zero, and less than zero for each outcomebhpm.ptheta
Generate a template for the individual model parameter simulation control parameters.bhpm.sim.control.params
Summary Statistics for the Posterior Distributions in the model.bhpm.summary.stats