Package: starma 1.3

starma: Modelling Space Time AutoRegressive Moving Average (STARMA) Processes

Statistical functions to identify, estimate and diagnose a Space-Time AutoRegressive Moving Average (STARMA) model.

Authors:Felix Cheysson

starma_1.3.tar.gz
starma_1.3.zip(r-4.7)starma_1.3.zip(r-4.6)starma_1.3.zip(r-4.5)
starma_1.3.tgz(r-4.6-x86_64)starma_1.3.tgz(r-4.6-arm64)starma_1.3.tgz(r-4.5-x86_64)starma_1.3.tgz(r-4.5-arm64)
starma_1.3.tar.gz(r-4.7-arm64)starma_1.3.tar.gz(r-4.7-x86_64)starma_1.3.tar.gz(r-4.6-arm64)starma_1.3.tar.gz(r-4.6-x86_64)
starma_1.3.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
starma/json (API)

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

Bug tracker:https://github.com/fcheysson/starma/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
Datasets:
  • blist - Neighbourhood weight matrices for France's 94 departments
  • dlist - Neighbourhood weight matrices for France's 94 departments
  • klist - Neighbourhood weight matrices for France's 94 departments

On CRAN:

Conda:

openblascpp

2.62 score 3 stars 14 scripts 261 downloads 17 exports 19 dependencies

Last updated from:2e08ba5e12. Checks:11 NOTE, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64NOTE196
linux-devel-x86_64NOTE122
source / vignettesOK161
linux-release-arm64NOTE155
linux-release-x86_64NOTE185
macos-release-arm64NOTE143
macos-release-x86_64NOTE432
macos-oldrel-arm64NOTE112
macos-oldrel-x86_64NOTE296
windows-develNOTE163
windows-releaseNOTE136
windows-oldrelNOTE122
wasm-releaseOK118

Exports:print.starmaprint.stcor.testprint.summary.starmastacfstacfCPPstarmastarma.defaultstarmaCPPstcenterstcor.teststcor.test.defaultstcovstcovCPPstpacfstpacfCPPstplotsummary.starma

Dependencies:clicpp11farverggplot2gluegtableisobandlabelinglifecycleR6RColorBrewerRcppRcppArmadillorlangS7scalesvctrsviridisLitewithr