Package: VBphenoR 1.1.0
Brian Buckley
VBphenoR: Variational Bayes for Latent Patient Phenotypes in EHR
Identification of Latent Patient Phenotype from Electronic Health Records (EHR) Data using Variational Bayes Gaussian Mixture Model for Latent Class Analysis and Variational Bayes regression for Biomarker level shifts, both implemented by Coordinate Ascent Variational Inference algorithms. Variational methods are used to enable Bayesian analysis of very large Electronic Health Records data. For VB GMM details see Bishop (2006,ISBN:9780-387-31073-2). For Logistic VB see Jaakkola and Jordan (2000) <doi:10.1023/A:1008932416310>. Please see preprint of JSS-submitted paper <doi:10.48550/arXiv.2512.14272>.
Authors:
VBphenoR_1.1.0.tar.gz
VBphenoR_1.1.0.zip(r-4.7)VBphenoR_1.1.0.zip(r-4.6)VBphenoR_1.1.0.zip(r-4.5)
VBphenoR_1.1.0.tgz(r-4.6-any)VBphenoR_1.1.0.tgz(r-4.5-any)
VBphenoR_1.1.0.tar.gz(r-4.6-any)VBphenoR_1.1.0.tar.gz(r-4.5-any)
VBphenoR_1.1.0.tgz(r-4.5-emscripten)
VBphenoR.pdf |VBphenoR.html✨
VBphenoR/json (API)
NEWS
| # Install 'VBphenoR' in R: |
| install.packages('VBphenoR', repos = c('https://buckleybrian.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/buckleybrian/vbphenor/issues
- scd_cohort - Synthetic Sickle Cell Anaemia data
Last updated from:ce1bbb599a. Checks:7 WARNING, 2 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | WARNING | 139 | ||
| source / vignettes | OK | 194 | ||
| linux-release-x86_64 | WARNING | 146 | ||
| macos-release-arm64 | WARNING | 216 | ||
| macos-oldrel-arm64 | WARNING | 148 | ||
| windows-devel | WARNING | 88 | ||
| windows-release | WARNING | 113 | ||
| windows-oldrel | WARNING | 105 | ||
| wasm-release | OK | 359 |
Exports:logit_CAVIrun_Modelvb_gmm_cavi
Dependencies:CholWishartclicpp11data.tabledbscanevaluatefarvergenericsggplot2gluegtablehighrisobandknitrlabelinglifecyclepracmaR6RColorBrewerRcpprlangS7scalesvctrsviridisLitewithrxfunyaml
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Variational inference for Bayesian logistic regression using CAVI algorithm | logit_CAVI |
| Run the Variational Bayes patient phenotyping model | run_Model |
| Synthetic Sickle Cell Anaemia data | scd_cohort |
| Main algorithm function for the VB CAVI GMM | vb_gmm_cavi |
| Calculate the Evidence Lower Bound (ELBO) | VB_GMM_ELBO |
| Variational Bayes Expectation step | VB_GMM_Expectation |
| Initialise the variational parameters and the hyper parameters | VB_GMM_Init |
| Variational Bayes Maximisation step | VB_GMM_Maximisation |
