Reference

CoPheScan with fixed priors
per.snp.priors()	per.snp.priors
adjust_priors()	adjust_priors
hypothesis.priors()	hypothesis.priors
cophe.single()	Bayesian cophescan analysis using Approximate Bayes Factors
cophe.single.lbf()	cophe.single.lbf
cophe.susie()	run cophe.susie using susie to detect separate signals
cophe.susie.lbf()	cophe.susie.lbf
combine.bf()	combine.bf
cophe.multitrait()	Run cophescan on multiple traits at once
summary(<cophe>)	print the summary of results from cophescan single or susie
multitrait.simplify()	Simplifying the output obtained from cophe.multitrait, cophe.single or cophe.susie
logsum()	logsum
CoPheScan with hierarchical priors
run_metrop_priors()	Run the hierarchical Metropolis Hastings model to infer priors
average_piks()	Average of priors: pnk, pak and pck
average_piks_list()	Average of priors: pnk, pak and pck from list (memory intensive)
average_posterior_prob()	Average of posterior probabilities: Hn, Ha and Hc
average_posterior_prob_list()	Average of posterior probabilities: Hn, Ha and Hc from list (memory intensive)
get_posterior_prob()	Calculation of the posterior prob of Hn, Ha and Hc
get_beta()	Extract beta and p-values of queried variant
sample_alpha()	sample alpha
sample_beta()	sample beta
sample_gamma()	sample gamma
logd_alpha()	dnorm for alpha
logd_beta()	dgamma for beta
logd_gamma()	dgamma for gamma
loglik()	Log likelihood calculation
logpost()	Log posterior calculation
logsumexp()	Log sum
metrop_run()	Run the hierarchical mcmc model to infer priors
pars2pik()	Conversion of parameters alpha, beta and gamma to pnk, pak and pck
piks()	List of priors: pn, pa and pc over all iterations
posterior_prob()	List of posterior probabilities: Hn, Ha and Hc over all iterations
logpriors()	Calculate log priors
pars_init()	Initiate parameters alpha, beta and gamma
propose()	Proposal distribution
target()	Target distribution
Predict hypothesis
cophe.hyp.predict()	Predict cophescan hypothesis for tested associations
Hc.cutoff.fdr()	Estimate the Hc.cutoff for the required FDR
Visualization
plot_trait_manhat()	Plot region Manhattan for a trait highlighting the queried variant
cophe_plot()	cophe_plots showing the Ha and Hc of all traits and labelled above the specified threshold
cophe_heatmap()	Heatmap of multi-trait cophescan results
prepare_plot_data()	Prepare data for plotting
Test data
cophe_multi_trait_data	Simulated multi-trait data
Package
cophescan-package cophescan	The 'cophescan' package.