Package index • penetrance

All functions

absValue(): Function to return absolute values

apply_burn_in(): Apply Burn-In

apply_thinning(): Apply Thinning

baseline_data_default: Default Baseline Data

calculateBaseline(): Calculate Baseline Risk

calculateEmpiricalDensity(): Calculate Empirical Age Density

calculateNCPen(): Calculate Age-Specific Non-Carrier Penetrance

calculate_weibull_parameters(): Calculate Weibull Parameters

combine_chains(): Combine Chains Function to combine the posterior samples from the multiple chains.

combine_chains_noSex(): Combine Chains for Non-Sex-Specific Estimation

distribution_data_default: Default Distribution Data

drawBaseline(): Draw Ages Using the Inverse CDF Method from the baseline data

drawEmpirical(): Draw Ages Using the Inverse CDF Method from Empirical Density

generate_density_plots(): Generate Posterior Density Plots

generate_summary(): Generate Summary

generate_summary_noSex(): Generate Summary for Non-Sex-Specific Estimation

imputeAges(): Impute Missing Ages in Family-Based Data

imputeAgesInit(): Initialize Age Imputation

imputeUnaffectedAges(): Impute Ages for Unaffected Individuals

lik.fn(): Penetrance Function

lik_noSex(): Likelihood Calculation without Sex Differentiation

makePriors(): Make Priors

mhChain(): Execution of a Single Chain in Metropolis-Hastings for Cancer Risk Estimation

mhLogLikelihood_clipp(): Calculate Log Likelihood using clipp Package

mhLogLikelihood_clipp_noSex(): Calculate Log Likelihood without Sex Differentiation

out_sim: Simulated Output Data

penetrance(): penetrance: A Package for Penetrance Estimation

plot_acf(): Plot Autocorrelation for Multiple MCMC Chains (Posterior Samples)

plot_loglikelihood(): Plot Log-Likelihood for Multiple MCMC Chains

plot_pdf(): Plot Weibull Probability Density Function with Credible Intervals

plot_penetrance(): Plot Weibull Distribution with Credible Intervals

plot_trace(): Plot MCMC Trace Plots

printRejectionRates(): Print MCMC Rejection Rates

prior_params_default: Default Prior Parameters

risk_proportion_default: Default Risk Proportions

simulated_families: Processed Family Data

test_fam2: Processed Family Data

transformDF(): Transform Data Frame

validate_weibull_parameters(): Validate Weibull Parameters