Package index

Available data

Datasets included in the package.

exampleData_CNA_dormancy

Dataset with a toy example for the CNA model with dormancy

exampleData_CNA

Dataset with a toy example for the CNA model

exampleData_Driver

Dataset with a toy example for the Driver model

exampleFit

Example of a TOSCA object with compleated inference

UPN06

Input data for patient UPN06

D9MRCY

Input data for patient D9MRCY

Create TOSCA object

Functions to create the TOSCA object and check the format of input data.

init()

Initialise TOSCA object

convert_date_real()

Converts real into date

convert_real_date()

Converts date into real

print(<TOSCA>)

Print for class 'TOSCA'.

Fit model

Function to fit model.

fit()

Infer the timing of the events of interest fitting the appropriate TOSCA model

get_inference_data()

Get inference data

Plot fit

Function to visualise the results of the fit.

check_ppc()

Posterior Predictive checks

plot_expected_N()

Posterior distribution of the number of cells collected in the first and second samples.

plot_ppc()

Plot posterior predictive checks

plot_ppc_single_mut()

Plot the posterior predictive distribution and compares it to the real number of mutations

plot_prior_vs_posterior()

Produce collective plot of prior vs posterior for all inferred parameters

plot_prior_vs_posterior_single_parameter()

Prior vs Posterior distribution of inferred parameter

plot_timing()

Plot clinical timeline + posterior times with histogram

plot_timing_MAP()

Plot clinical timeline + posterior times with MAP

plot_timing_days()

Plot clinical timeline + posterior times with histogram in days from a chosen date

days_from()

Computes the days of the event of interest from a date of interest

Examine fit

Get the results from the inference.

get_fit_summary()

Get mean, mode and q5, q95

get_cmdstanr_posterior()

Get posterior draws from cmdstanr obj

plot_mcmc_chains()

Plot the MCMC chains

plot_divergent_transitions()

Plot the sampling of parameters highlighting the iterations where divergent transitions occurred

plot_pairs()

Plot univariate and multivariate distributions of selected parameters

rhats_plot()

Plot Rhats of selected parameters

ess_plot()

Plot the Effective Sample Size of selected parameters

autocorrelation_plot()

Plot the autocorrelation of MCMC samples with progressive iterations

energy_plot()

Plot to quantiy the heaviness of the tails of the posterior

get_variables_names()

Get mapping of input variables names and names used in the model