## All functions

adfit()

Constructor for the "adfit" (A-D fit) class

adnuts

as.data.frame(<adfit>)

Convert object of class adfit to data.frame. Calls extract_samples

check_identifiable()

Check identifiability from model Hessian

.check_ADMB_version()

Check that the model is compiled with the right version of ADMB which is 12.0 or later

.check_console_printing()

Check if the session is interactive or Rstudio which has implications for parallel output

.check_model_path()

Check that the file can be found

.getADMBHessian()

.sample_admb()

Hidden wrapper function for sampling from ADMB models

.update_model()

Convert model name depending on system

extract_sampler_params()

Extract sampler parameters from a fit.

extract_samples()

Extract posterior samples from a model fit.

is.adfit()

launch_shinyadmb()

Launch shinystan for an ADMB fit.

launch_shinytmb()

Launch shinystan for a TMB fit.

pairs_admb()

Plot pairwise parameter posteriors and optionally the MLE points and confidence ellipses.

plot(<adfit>)

plot_marginals()

Plot marginal distributions for a fitted model

plot_sampler_params()

Plot adaptation metrics for a fitted model.

plot_uncertainties()

Plot MLE vs MCMC marginal standard deviations for each parameter

print(<adfit>)

sample_admb()

Deprecated version of wrapper function. Use sample_nuts or sample_rwm instead.

sample_inits()

Function to generate random initial values from a previous fit using adnuts

sample_tmb()

Bayesian inference of a TMB model using the no-U-turn sampler.

sample_tmb_hmc()

Draw MCMC samples from a model posterior using a static HMC sampler.

sample_tmb_nuts()

Draw MCMC samples from a model posterior using the No-U-Turn (NUTS) sampler with dual averaging.

sample_tmb_rwm()

[Deprecated] Draw MCMC samples from a model posterior using a Random Walk Metropolis (RWM) sampler.

summary(<adfit>)

Print summary of object of class adfit

sample_nuts() sample_rwm()

Bayesian inference of an ADMB model using the no-U-turn sampler (NUTS) or random walk Metropolis (RWM) algorithms.