Package: openEBGM 0.9.1

openEBGM: EBGM Disproportionality Scores for Adverse Event Data Mining

An implementation of DuMouchel's (1999) <doi:10.1080/00031305.1999.10474456> Bayesian data mining method for the market basket problem. Calculates Empirical Bayes Geometric Mean (EBGM) and posterior quantile scores using the Gamma-Poisson Shrinker (GPS) model to find unusually large cell counts in large, sparse contingency tables. Can be used to find unusually high reporting rates of adverse events associated with products. In general, can be used to mine any database where the co-occurrence of two variables or items is of interest. Also calculates relative and proportional reporting ratios. Builds on the work of the 'PhViD' package, from which much of the code is derived. Some of the added features include stratification to adjust for confounding variables and data squashing to improve computational efficiency. Includes an implementation of the EM algorithm for hyperparameter estimation loosely derived from the 'mederrRank' package.

Authors:John Ihrie [cre, aut], Travis Canida [aut], Ismaïl Ahmed [ctb], Antoine Poncet [ctb], Sergio Venturini [ctb], Jessica Myers [ctb]

openEBGM_0.9.1.tar.gz
openEBGM_0.9.1.zip(r-4.5)openEBGM_0.9.1.zip(r-4.4)openEBGM_0.9.1.zip(r-4.3)
openEBGM_0.9.1.tgz(r-4.4-any)openEBGM_0.9.1.tgz(r-4.3-any)
openEBGM_0.9.1.tar.gz(r-4.5-noble)openEBGM_0.9.1.tar.gz(r-4.4-noble)
openEBGM_0.9.1.tgz(r-4.4-emscripten)openEBGM_0.9.1.tgz(r-4.3-emscripten)
openEBGM.pdf |openEBGM.html
openEBGM/json (API)
NEWS

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

Peer review:

Datasets:
  • caers - Dietary supplement reports and products
  • caers_raw - Raw CAERS data

On CRAN:

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

3.71 score 1 stars 1 packages 34 scripts 439 downloads 14 exports 29 dependencies

Last updated 1 years agofrom:6e6885cc87. Checks:OK: 3 NOTE: 4. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 09 2024
R-4.5-winNOTEOct 09 2024
R-4.5-linuxNOTEOct 09 2024
R-4.4-winNOTEOct 09 2024
R-4.4-macNOTEOct 09 2024
R-4.3-winOKOct 09 2024
R-4.3-macOKOct 09 2024

Exports:autoHyperautoSquashebgmebScoresexploreHypershyperEMnegLLnegLLsquashnegLLzeronegLLzeroSquashprocessRawQnquantBisectsquashData

Dependencies:clicolorspacedata.tablefansifarverggplot2gluegtableisobandlabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepillarpkgconfigR6RColorBrewerrlangscalestibbleutf8vctrsviridisLitewithr

Empirical Bayes Metrics with openEBGM

Rendered fromvignette04-posteriorCalculation.Rmdusingknitr::rmarkdownon Oct 09 2024.

Last update: 2023-09-15
Started: 2023-09-15

Hyperparameter Estimation with openEBGM

Rendered fromvignette03-hyperparameterEstimation.Rmdusingknitr::rmarkdownon Oct 09 2024.

Last update: 2023-09-15
Started: 2023-09-15

Introduction to openEBGM

Rendered fromvignette01-introAndDataPrep.Rmdusingknitr::rmarkdownon Oct 09 2024.

Last update: 2023-09-15
Started: 2023-09-15

openEBGM Objects and Class Functions

Rendered fromvignette05-openEBGMObject.Rmdusingknitr::rmarkdownon Oct 09 2024.

Last update: 2023-09-15
Started: 2023-09-15

Processing Raw Data with openEBGM

Rendered fromvignette02-rawDataProcessing.Rmdusingknitr::rmarkdownon Oct 09 2024.

Last update: 2023-09-15
Started: 2023-09-15