BeviMed: Bayesian Evaluation of Variant Involvement in Mendelian Disorders

Bayesian inference procedures for evaluating evidence of involvement of variants in Mendelian disorders based on intuitive models of dominant and recessive inheritance.

AuthorDaniel Greene
Date of publication2016-07-05 23:43:22
MaintainerDaniel Greene <dg333@cam.ac.uk>
LicenseGPL (>= 2)
Version2.2

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Man pages

bevimed: Perform inference under model gamma = 1

BeviMed-package: A short title line describing what the package does

call_cpp: R interface to BeviMed c++ MCMC procedure

CI_gamma1_evidence: Estimate confidence interval for estimated marginal...

conditional_prob_pathogenic: Calculate probability of pathogencity for variants in region...

exact_evidence: Calculate exact evidence for model gamma=1

gamma0_evidence: Calculate marginal probability of observed case-control...

gamma1_evidence: Calculate marginal probability of observed case-control...

gamma1_prob: Calculate probability of an association

log_BF: Calculate log Bayes factor between models gamma=1 and gamma=0...

lower_bound_gamma1_evidence: Calculate log lower bound for marginal probability of...

print.BeviMed: Print readable summary of 'BeviMed' object.

print.BeviMed_summary: Print readable summary of 'BeviMed_summary' object.

prob_association: Calculate probability of an association between...

prob_pathogenic: Calculate probability of pathogencity for variants in region...

region_association_evidence: Calculate marginal probability of observed genotypes under...

stack_BeviMeds: Concatenate objects of class 'BeviMed'

stop_chain: Apply the MCMC algorithm in blocks until conditions are met

summary.BeviMed: Create summary of 'BeviMed' classed-object

sum_ML_over_PP: Calculate the Marginal Likelihood by summation over power...

tune_proposal_sds: Tune the proposal standard deviations for the...

tune_temperatures: Tune temperatures using interval bisection to minimimise...

Questions? Problems? Suggestions? or email at ian@mutexlabs.com.

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