Package: simBKMRdata 0.2.1

simBKMRdata: Helper Functions for Bayesian Kernel Machine Regression

Provides a suite of helper functions to support Bayesian Kernel Machine Regression (BKMR) analyses in environmental health research. It enables the simulation of realistic multivariate exposure data using Multivariate Skewed Gamma distributions, estimation of distributional parameters by subgroup, and application of adaptive, data-driven thresholds for feature selection via Posterior Inclusion Probabilities (PIPs). It is especially suited for handling skewed exposure data and enhancing the interpretability of BKMR results through principled variable selection. The methodology is shown in Hasan et. al. (2025) <doi:10.1101/2025.04.14.25325822>.

Authors:Kazi Tanvir Hasan [aut, cre], Ibrahimou Boubakari [aut], Guerini Cristian [aut], Bursac Zoran [aut], Roberto Lucchini [aut], Gabriel Odom [aut]

simBKMRdata_0.2.1.tar.gz
simBKMRdata_0.2.1.zip(r-4.7)simBKMRdata_0.2.1.zip(r-4.6)simBKMRdata_0.2.1.zip(r-4.5)
simBKMRdata_0.2.1.tgz(r-4.6-any)simBKMRdata_0.2.1.tgz(r-4.5-any)
simBKMRdata_0.2.1.tar.gz(r-4.7-any)simBKMRdata_0.2.1.tar.gz(r-4.6-any)
simBKMRdata_0.2.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
simBKMRdata/json (API)

# Install 'simBKMRdata' in R:
install.packages('simBKMRdata', repos = c('https://khasa006.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/khasa006/simbkmrdata/issues

Datasets:

On CRAN:

Conda:

quarto

4.18 score 10 scripts 530 downloads 12 exports 1 dependencies

Last updated from:a94acbec92. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK166
source / vignettesOK284
linux-release-x86_64OK167
macos-release-arm64OK148
macos-oldrel-arm64OK237
windows-develOK104
windows-releaseOK114
windows-oldrelOK108
wasm-releaseOK140

Exports:calculate_pip_thresholdcalculate_stats_gammacalculate_stats_gaussianestimate_mv_momentsestimate_mv_shape_rategenerate_mvGamma_datasimulate_group_datasimulate_group_gammasimulate_group_gaussiantrans_logtrans_ratiotrans_root

Dependencies:MASS

Helper Functions for Bayesian Kernel Machine Regression
Abstract | Keywords | Introduction | Materials and Methods | Functionality in this R Package | Software Implementation | Example Analysis of Heavy Metal Exposure | Data Exploration | Parameter Estimation | Data Simulation | Dynamic Threshold Calculation | Bayesian Kernel Machine Regression Analysis | Results | Discussion | Implications of Pediatric Heavy Metal Exposure | Utility in Future Methodological Research | Conclusion
Rendered frompackageOverview.qmdusingquarto::html

Last update: 2025-05-18
Started: 2025-04-09

Simulation and Estimation for each group
Introduction | Setup | Simulate Multivariate Normal Data (Using Pre-Estimated Statistics) | Visualizing the Multivariate Normal Data | 2. Estimate Multivariate Moments (Using an Existing Dataset) | Example: Estimating Moments from an Existing Dataset | Output Explanation | 3. Simulate Multivariate Skewed Gamma Data (Using Pre-Estimated Statistics) | Visualizing the Skewed Gamma Data | 4. Estimate Multivariate Skewed Gamma Parameters (Using an Existing Dataset) | Example: Estimating Skewed Gamma Parameters Using MoM | Conclusion
Rendered fromestimation_and_simulation.qmdusingquarto::html

Last update: 2025-04-14
Started: 2025-03-27

Calculate PIP Threshold from Response Vector
Introduction | 1. Required Libraries | 2. Data Preprocessing | 3. Exploratory Data Analysis (EDA) | 4. Model Setup | 5. Generating Knot Points for the Model | 6. Fitting the BKMR Model | 7. Extracting Results | 9. Interpretation of Results | Conclusion
Rendered frombkmr_threshold.qmdusingquarto::html

Last update: 2025-04-04
Started: 2025-03-30