Package: BeviMed 5.10

BeviMed: Bayesian Evaluation of Variant Involvement in Mendelian Disease

A fast integrative genetic association test for rare diseases based on a model for disease status given allele counts at rare variant sites. Probability of association, mode of inheritance and probability of pathogenicity for individual variants are all inferred in a Bayesian framework - 'A Fast Association Test for Identifying Pathogenic Variants Involved in Rare Diseases', Greene et al 2017 <doi:10.1016/j.ajhg.2017.05.015>.

Authors:Daniel Greene, Sylvia Richardson and Ernest Turro

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BeviMed.pdf |BeviMed.html✨
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NEWS

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

Peer review:

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

On CRAN:

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

19 exports 1.51 score 3 dependencies 5 mentions 17 scripts 1.1k downloads

Last updated 4 months agofrom:060b063457. Checks:OK: 9. Indexed: yes.

TargetResultDate
Doc / VignettesOKAug 28 2024
R-4.5-win-x86_64OKAug 28 2024
R-4.5-linux-x86_64OKAug 28 2024
R-4.4-win-x86_64OKAug 28 2024
R-4.4-mac-x86_64OKAug 28 2024
R-4.4-mac-aarch64OKAug 28 2024
R-4.3-win-x86_64OKAug 28 2024
R-4.3-mac-x86_64OKAug 28 2024
R-4.3-mac-aarch64OKAug 28 2024

Exports:bevimedbevimed_mbevimed_polytomousconditional_prob_pathogenicexpected_explainedexplaining_variantsextract_conditional_prob_pathogenicextract_expected_explainedextract_explaining_variantsextract_gamma1_evidenceextract_prob_associationextract_prob_pathogenicgamma0_evidencegamma1_evidencelog_BFprob_associationprob_association_mprob_pathogenicsubset_variants

Dependencies:latticeMatrixRcpp

BeviMed Guide

Rendered fromGuide.Rnwusingknitr::knitron Aug 28 2024.

Last update: 2024-05-30
Started: 2017-03-01

BeviMed Introduction

Daniel Greene

Rendered fromIntro.Rmdusingknitr::rmarkdownon Aug 28 2024.

Last update: 2024-05-30
Started: 2016-05-03

BeviMed with VCFs

Daniel Greene

Rendered fromvcf.Rmdusingknitr::rmarkdownon Aug 28 2024.

Last update: 2024-05-30
Started: 2017-02-16

Readme and manuals

Help Manual

Help pageTopics
Bayesian Evaluation of Variant Involvement in Mendelian DiseaseBeviMed-package BeviMed
Bayesian Evaluation of Variant Involvement in Mendelian Diseasebevimed
Perform inference under model gamma = 1 conditional on mode of inheritancebevimed_m
Model selection for multiple association modelsbevimed_polytomous
R interface to BeviMed c++ MCMC procedurecall_cpp
Estimate confidence interval for estimated marginal likelihoodCI_gamma1_evidence
Calculate probability of pathogencity for variants conditional on mode of inheritance.conditional_prob_pathogenic
Calculate expected number of explained casesexpected_explained
Calculate expected number of pathogenic variants in casesexplaining_variants
Extract probability of pathogenicity for variant conditional on a given association modelextract_conditional_prob_pathogenic
Extract expected number of explained casesextract_expected_explained
Extract expected number of pathogenic variants in casesextract_explaining_variants
Extract evidence for model gamma = 1extract_gamma1_evidence
Extract the posterior probability of associationextract_prob_association
Extract variant marginal probabilities of pathogenicityextract_prob_pathogenic
Calculate marginal probability of observed case-control status y under model gamma = 0gamma0_evidence
Calculate evidence under model gamma = 1gamma1_evidence
Calculate log Bayes factor between an association model with a given mode of inheritance and model gamma = 0log_BF
Print readable summary of 'BeviMed' objectprint.BeviMed
Print 'BeviMed_m' objectprint.BeviMed_m
Print readable summary of 'BeviMed_summary' object.print.BeviMed_summary
Calculate probability of associationprob_association
Calculate probability of association for one mode of inheritanceprob_association_m
Calculate variant marginal probabilities of pathogencityprob_pathogenic
Concatenate objects of class 'BeviMed_raw'stack_BeviMeds
Apply the MCMC algorithm in blocks until conditions are metstop_chain
Remove variants with no data for pathogenicitysubset_variants
Calculate marginal likelihood from power posteriors outputsum_ML_over_PP
Summarise a 'BeviMed' objectsummary.BeviMed
Summarise a 'BeviMed_m' objectsummary.BeviMed_m
Tune proposal standard deviation for MH sampled parameterstune_proposal_sds
Tune temperaturestune_temperatures