Package: particlefield 0.0.1

particlefield: Sequential Monte Carlo for Latent Conditional Autoregressive Model

Functions for replicating the results of the latent Gaussian Markov random field experiment of Lindsten, Helske, Vihola (2018), XX. Contains also functions for performing particle Markov chain Monte Carlo estimation of the model parameters.

Authors:Jouni Helske [aut, cre]

particlefield_0.0.1.tar.gz
particlefield_0.0.1.zip(r-4.7-x86_64)particlefield_0.0.1.zip(r-4.7-arm64)particlefield_0.0.1.zip(r-4.6-x86_64)particlefield_0.0.1.zip(r-4.6-arm64)particlefield_0.0.1.zip(r-4.5-x86_64)
particlefield_0.0.1.tgz(r-4.6-x86_64)particlefield_0.0.1.tgz(r-4.6-arm64)particlefield_0.0.1.tgz(r-4.5-x86_64)particlefield_0.0.1.tgz(r-4.5-arm64)
particlefield_0.0.1.tar.gz(r-4.7-arm64)particlefield_0.0.1.tar.gz(r-4.7-x86_64)particlefield_0.0.1.tar.gz(r-4.6-arm64)particlefield_0.0.1.tar.gz(r-4.6-x86_64)
particlefield_0.0.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
particlefield/json (API)

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

Bug tracker:https://github.com/helske/particlefield/issues

Uses libs:
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

cpp

2.18 score 3 stars 4 scripts 5 exports 5 dependencies

Last updated from:4cbb2c5c96. Checks:13 ERROR, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64ERROR138
linux-devel-x86_64ERROR147
source / vignettesOK322
linux-release-arm64ERROR119
linux-release-x86_64ERROR156
macos-release-arm64ERROR143
macos-release-x86_64ERROR267
macos-oldrel-arm64ERROR131
macos-oldrel-x86_64ERROR239
windows-devel-arm64ERROR250
windows-devel-x86_64ERROR198
windows-release-arm64ERROR254
windows-release-x86_64ERROR142
windows-oldrel-x86_64ERROR162
wasm-releaseOK105

Exports:approximate_binomial_carbsf_carmcmc_binomial_carprint_graphpsi_car

Dependencies:codalatticeMatrixRcppRcppEigen