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as.data.frame(<tmbfit> )
Convert object of class tmbfit to data.frame. Calls extract_samples
as.tmbfit()
Construtor for tmbfit objects
benchmark_metrics()
Calculate gradient timings on a model for different metrics
check_snuts_diagnostics()
Check NUTS diagnostics of a fitted model
.get_Q_stats()
Get the joint precision matrix Q from an optimized TMB or RTMB obj, along with the standard errors and lower bound of the absolute maximum correlation. Done using efficient sparse methods, specifically the Takahashi approach implemented in the Takahashi_Davis function in the sparseinverse package.
.get_Qinv_stats()
Get the covariance matrix Qinv from an optimized TMB or RTMB obj, along with the standard errors and lower bound of the absolute maximum correlation.
.get_inits()
Get a single initial value vector in untransformed model space
.make_unique_names()
Function to take a character vector of parameter names and force them to be unique by appending numbers in square brackets as needed
.print.mat.stats()
Print matrix stats
.rotate_posterior()
Update algorithm for mass matrix.
extract_sampler_params()
Extract sampler parameters from a fit.
extract_samples()
Extract posterior samples from a model fit.
get_post()
Extract posterior samples from a tmbfit object
get_sparse_correlation()
Extract a single correlation from a sparse precision matrix
is.tmbfit()
Check object of class tmbfit
launch_shinytmb()
Launch shinystan for a TMB fit.
pairs(<tmbfit> )
Plot pairwise parameter posteriors and optionally the MLE points and confidence ellipses.
plot(<tmbfit> )
Plot object of class tmbfit
plot_Q()
Make an image plot showing the correlation (lower triangle) and sparsity (upper triangle).
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(<tmbfit> )
Print summary of tmbfit object
sample_inits()
Function to generate random initial values from a previous fit using SparseNUTS
sample_snuts()
NUTS sampling for TMB models using a sparse metric (BETA).
summary(<tmbfit> )
Print summary of object of class tmbfit
tmbfit()
Constructor for the "tmbfit" class