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fastLISA

1.0.1Package record

Fast Local Indicators of Spatial Association (LISA)

1packages depend
1Kdownloads / year
93.0%test coverage
13/13flavors OK

Overview

About

Computes various Local Indicators of Spatial Association (LISA) statistics, including univariate and bivariate local Moran's I, Empirical Bayes local Moran's I, univariate and multivariate local Geary's C, and Getis-Ord G and G* statistics. The methods follow Anselin (1995), Getis and Ord (1992), and Anselin (2019). Leverages a high-performance, plain-C backend with optional 'OpenMP' multi-core support for fast permutation-based pseudo-p-value calculation. Accepts any 'spdep' listw spatial weight matrix, including custom and non-contiguity weights. Uses sample standardisation (n-1) and 'rgeoda'-style permutation p-values. Output cluster codes match 'rgeoda' conventions, including the Isolated category for observations without neighbours.

Changes in 1.0.1
  • Vignette: corrected the "Reproducibility" note, which incorrectly stated that multi-core permutation results "may differ between runs". Permutation inference is thread-invariant: because the RNG is re-seeded per observation from iseed and the observation index alone, the pseudo-p-values and z-scores are bit-for-bit identical for any n.cores and under any OpenMP schedule.

From NEWS.md in the release

Install

Health

CRAN checks
13OK
Slowest check: 2.4 min · r-devel-windows-x86_64
Status changes
  • OK13 OK2026-08-05
  • NOTE12 OK, 1 NOTE2026-08-01
  • OK10 OK2026-07-08
  • ERROR6 OK, 1 ERROR2026-07-07
Advisories

none known

Documentation

In release 1.0.1
READMEYes · 555 wordsVignettesYes · 1 · dynamicNEWSYes · 100% structured · mentions 1.0.1Citation fileNoWebsitenot declared in DESCRIPTION
Help pages
8 (1 internal)includes a package overview page
Examples that run
100%7 run in a plain check (7 of them under a condition)
Exported functions with examples
100%
Documented parameters
100%
Return-value docs
100%
Help pages citing references
88%

Downloads

1K
CRAN downloads in the past year
Rank #14,787 · ~3/day · ~87/mo
Daily download trend is not available in this view yet.
27730 days
81890 days
1K1 year
Compare downloads with other packages →
Also on61 r2u13 autocran

Repository

3Stars
0Forks
0Open PRs
0Releases
7Commits
1Commit identities
License file reads as GPL-3.0 · 7 commits · Last activity 2026-07-07 · 0% stars, 30d
Responsiveness
not measured

Stars over time

2026-07-08 · 22026-08-06 · 3
PracticesBeta

Checks run against github.com/lizhongc/fastlisa on 2026-10-21.

No development-tooling practices detected in the upstream repository.

How this is detected·Detection ruleset v4 (2026-10-20)

Dependencies

Declared dependencies
1 external dependency (excludes base and recommended)

Installing fastLISA adds no packages beyond those that come with R.

Depends (0)
none
Imports (1)
stats
LinkingTo (0)
none
Suggests (1)
Enhances (0)
none
Reverse dependencies
1direct
0indirect
Imports 1
Depends, Imports or LinkingTo, most downloaded first

People & History

People (1)
Author, Maintainer since 1.0.0 (2026)
Releases (2)

Pick two releases to compare their code metrics.

  • 1.0.1Latest
    record
    2026-07-07 · current release · diff ↗
  • 1.0.0
    record
    2026-07-06
  • R
    R 4.6.0 released · 2026-04-24

Package metadata

First published
2026-07-06
Total releases
2 / 1 yrs
License
GPL-3 OSI
Download size
148 KB

Cite

Cite this package

Run in R for the authors' preferred citation:

citation("fastLISA")
Chen, L. (2026). fastLISA: Fast Local Indicators of Spatial Association (LISA) (Version 1.0.1) [Computer software]. https://doi.org/10.32614/CRAN.package.fastLISA

This package has no citation file, so citation() returns this entry built from DESCRIPTION.

Cite the R Observatory

For a number measured here: a download total, a coverage figure, an archival date.

APA

Balamuta, J. J. (2026). R Observatory: Metrics for fastLISA version 1.0.1 [Data set]. HJJB, LLC. Data release v2026-10-25. https://doi.org/10.5281/zenodo.21843040

From data release v2026-10-25, which the citation names so these numbers can be found later. More on citing and the projects behind them.

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