Package index • SparseNUTS

All functions

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(): Get the joint precision matrix Q from an optimized TMB or RTMB obj.
.get_Qinv(): Get the joint covariance Sigma from an optimized TMB or RTMB obj without random effects.
.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
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