aclhs

cranv1.0.1

Autocorrelated Conditioned Latin Hypercube Sampling. Implementation of the autocorrelated conditioned Latin Hypercube Sampling (acLHS) algorithm for 1D (time-series) and 2D (spatial) data. The acLHS algorithm is an extension of the conditioned Latin Hypercube Sampling (cLHS) algorithm that allows sampled data to have similar correlative and statistica

License MIT + file LICENSE0 versions1 maintainers5 deps59 weekly dl
vargaslab/acLHS
42
/ 100
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safe to use

[email protected] is safe to use (health: 42/100)

Health breakdown0 – 100
15/25
maintenance
0/20
popularity
25/25
security
0/15
maturity
2/15
community
Vulnerabilities
0
none known
Maintainer trust
Active maintainers (3m)
1
Contributors (12m)
2
Primary author dominance
67%
GitHub stars
3
single active maintainer 3m

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First published · 2025-11-05 20:53:39

Last updated · 2025-11-05T19:10:02+00:00

aclhs — Health Score 42/100 | DepScope