Package: ggsmc 0.2.0

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

Functions for plotting, and animating, the output of importance samplers, sequential Monte Carlo samplers (SMC) and ensemble-based methods. The package can be used to plot and animate histograms, densities, scatter plots and time series, and to plot the genealogy of an SMC or ensemble-based algorithm. These functions all rely on algorithm output to be supplied in tidy format. A function is provided to transform algorithm output from matrix format (one Monte Carlo point per row) to the tidy format required by the plotting and animating functions.

Authors:Richard G Everitt [aut, cre]

ggsmc_0.2.0.tar.gz
ggsmc_0.2.0.zip(r-4.7)ggsmc_0.2.0.zip(r-4.6)ggsmc_0.2.0.zip(r-4.5)
ggsmc_0.2.0.tgz(r-4.6-any)ggsmc_0.2.0.tgz(r-4.5-any)
ggsmc_0.2.0.tar.gz(r-4.7-any)ggsmc_0.2.0.tar.gz(r-4.6-any)
ggsmc_0.2.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
ggsmc/json (API)

# Install 'ggsmc' in R:

install.packages('ggsmc', repos = c('https://richardgeveritt.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/richardgeveritt/ggsmc/issues

Pkgdown/docs site:https://richardgeveritt.github.io

Datasets:

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.
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.
sir_cwna_model - The output of a bootstrap particle filter on the 'cwna_data'. The output consists of 100 particles over 20 time steps.

On CRAN:

4.18 score 7 scripts 500 downloads 11 exports 41 dependencies

Last updated from:90fff4c88a. Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel-x86_64	OK	135
source / vignettes	OK	211
linux-release-x86_64	OK	118
macos-release-arm64	OK	164
macos-oldrel-arm64	OK	191
windows-devel	OK	148
windows-release	OK	69
windows-oldrel	OK	80
wasm-release	OK	121

Exports:animate_density animate_histogram animate_reveal_time_series animate_scatter animate_time_series matrix2tidy plot_density plot_genealogy plot_histogram plot_scatter plot_time_series

Dependencies:class classInt cli cpp11 crayon DBI e1071 farver gganimate ggplot2 glue gtable hms isoband KernSmooth labeling lifecycle lpSolve magrittr MASS pkgconfig poorman prettyunits progress proxy R6 RColorBrewer Rcpp rlang s2 S7 scales sf stringi transformr tweenr units vctrs viridisLite withr wk

Visualising the output of Monte Carlo methods with ggsmc

Rendered fromVisualising.Rmdusingknitr::rmarkdown

Last update: 2024-07-23
Started: 2024-07-21

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

Package: ggsmc 0.2.0

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

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