Visualising Output from Sequential Monte Carlo Samplers and Ensemble-Based Methods

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Documentation for package ‘ggsmc’ version 0.1.2.0

• DESCRIPTION file.
• User guides, package vignettes and other documentation.

Help Pages

animate_density	An animated density of a single variable across targets.
animate_histogram	An animated histogram of a single variable across targets.
animate_reveal_time_series	Plot animated line graph showing parameter value vs dimension (revealed in the animation) from algorithm output.
animate_scatter	A histogram of a single variable from a single target.
animate_time_series	Plot animated line graph showing parameter value vs dimension across targets from algorithm output.
cwna_data	Data generated from a constant velocity (or continuous white noise acceleration, CWNA) model for 20 time steps.
lv_output	10000 simulations from a stochastic Lotka-Volterra model, assigned weights according to a Gaussian approximate Bayesian computation kernel with tolerance equal to 50.
matrix2tidy	Convert IS, SMC or EnK output stored as a matrix to tidy format.
mixture_25_particles	The output of an SMC sampler where the initial distribution is a Gaussian and the final target is a mixture of Gaussians. 25 particles were used, with an adaptive method to determine the sequence of targets, and a Metropolis-Hastings move to move the particles at each step.
plot_density	A density of a single variable.
plot_genealogy	Plot an SMC or EnK genealogy from algorithm output.
plot_histogram	A histogram of a single variable.
plot_scatter	A histogram of a single variable from a single target
plot_time_series	Plot line graph showing parameter value vs dimension from algorithm output.
sir_cwna_model	The output of a bootstrap particle filter on the 'cwna_data'. The output consists of 100 particles over 20 time steps.