Package: dust 0.15.3

Rich FitzJohn

dust: Iterate Multiple Realisations of Stochastic Models

An Engine for simulation of stochastic models. Includes support for running stochastic models in parallel, either with shared or varying parameters. Simulations are run efficiently in compiled code and can be run with a fraction of simulated states returned to R, allowing control over memory usage. Support is provided for building bootstrap particle filter for performing Sequential Monte Carlo (e.g., Gordon et al. 1993 <doi:10.1049/ip-f-2.1993.0015>). The core of the simulation engine is the 'xoshiro256**' algorithm (Blackman and Vigna <arxiv:1805.01407>), and the package is further described in FitzJohn et al 2021 <doi:10.12688/wellcomeopenres.16466.2>.

Authors:Rich FitzJohn [aut, cre], Alex Hill [aut], John Lees [aut], Imperial College of Science, Technology and Medicine [cph]

dust_0.15.3.tar.gz
dust_0.15.3.zip(r-4.7-x86_64)dust_0.15.3.zip(r-4.7-arm64)dust_0.15.3.zip(r-4.6-x86_64)dust_0.15.3.zip(r-4.6-arm64)dust_0.15.3.zip(r-4.5-x86_64)
dust_0.15.3.tgz(r-4.6-x86_64)dust_0.15.3.tgz(r-4.6-arm64)dust_0.15.3.tgz(r-4.5-x86_64)dust_0.15.3.tgz(r-4.5-arm64)
dust_0.15.3.tar.gz(r-4.7-arm64)dust_0.15.3.tar.gz(r-4.7-x86_64)dust_0.15.3.tar.gz(r-4.6-arm64)dust_0.15.3.tar.gz(r-4.6-x86_64)
dust_0.15.3.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
dust/json (API)

# Install 'dust' in R:
install.packages('dust', repos = c('https://mrc-ide.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/mrc-ide/dust/issues

Uses libs:
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library

On CRAN:

Conda:

cppopenmp

7.77 score 20 stars 4 packages 61 scripts 15 exports 16 dependencies

Last updated from:c135a2fa92 (on master). Checks:4 ERROR, 9 NOTE, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64ERROR229
linux-devel-x86_64NOTE255
source / vignettesOK253
linux-release-arm64ERROR232
linux-release-x86_64NOTE247
macos-release-arm64NOTE213
macos-release-x86_64NOTE351
macos-oldrel-arm64NOTE205
macos-oldrel-x86_64NOTE625
windows-devel-arm64ERROR339
windows-devel-x86_64NOTE341
windows-release-arm64ERROR355
windows-release-x86_64NOTE245
windows-oldrel-x86_64NOTE193
wasm-releaseOK124

Exports:dustdust_cuda_configurationdust_cuda_optionsdust_datadust_exampledust_generatedust_ode_controldust_openmp_supportdust_openmp_threadsdust_packagedust_repair_environmentdust_rngdust_rng_distributed_pointerdust_rng_distributed_statedust_rng_pointer

Dependencies:callrclicpp11descfsgluelifecycleotelpkgbuildpkgloadprocessxpsR6rlangrprojrootwithr

Comparing models and data
Coping with missing data
Rendered fromdata.Rmdusingknitr::rmarkdown

Last update: 2023-06-28
Started: 2021-08-25

Introduction to dust
A simple example - random walk | Model code | Constructing a model | Running a model in parallel | A more interesting example | Other methods | Reordering particles | Set particle state | Reset the model | Use within a package
Rendered fromdust.Rmdusingknitr::rmarkdown

Last update: 2023-04-27
Started: 2020-07-06

Using RNGs from packages
Background using R's random number generator | Basic implementation using dust | Parallel implementation with dust and OpenMP | More on the pointer object
Rendered fromrng_package.Rmdusingknitr::rmarkdown

Last update: 2023-04-27
Started: 2021-11-12

Principles and design of dust
Running multiple realisations | Parallelisation | Efficient running | Efficient state handling | Useful verbs | A compilation target
Rendered fromdesign.Rmdusingknitr::rmarkdown

Last update: 2023-03-17
Started: 2021-02-25

Distributed parallel random numbers
A note on seeding | Distributed seeding | Continuing the streams | Considerations | Use cases | Summary
Rendered fromrng_distributed.Rmdusingknitr::rmarkdown

Last update: 2022-11-08
Started: 2021-11-17

Running models on GPUs with CUDA
Principles | Running a model with GPU support | Writing a GPU-capable model | Data comparison functions | Developing a GPU model
Rendered fromgpu.Rmdusingknitr::rmarkdown

Last update: 2022-11-08
Started: 2021-11-08

Random number generation
Supported distributions | Performance | Underlying random number engine | Reusing the random random number generator in other projects | In a package | Standalone, parallel with OpenMP | Standalone, parallel on a GPU | Other packages with similar functionality
Rendered fromrng.Rmdusingknitr::rmarkdown

Last update: 2022-11-02
Started: 2020-07-06

Algorithms used to compute random numbers
Box-Muller | Polar | Ziggurat | Sampling | Sampling from the tail | The edges | Optimisations
Rendered fromrng_algorithms.Rmdusingknitr::rmarkdown

Last update: 2021-11-16
Started: 2021-11-16

Multiple parameter sets
Considerations
Rendered frommulti.Rmdusingknitr::rmarkdown

Last update: 2021-10-20
Started: 2021-02-24