R: Visualising Output from Sequential Monte Carlo and Ensemble-Based Methods

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

## Documentation for package ‘ggsmc’ version 0.2.0

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package NEWS
- Package README

## 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.