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depscope/conda/r-mcmc

r-mcmc

condav0.9_6.1

Simulates continuous distributions of random vectors using Markov chain Monte Carlo (MCMC). Users specify the distribution by an R function that evaluates the log unnormalized density. Algorithms are random walk Metropolis algorithm (function metrop), simulated tempering (function temper), and morphometric random walk Metropolis (Johnson and Geyer, 2012, <https://doi.org/10.1214/12-AOS1048>, function morph.metrop), which achieves geometric ergodicity by change of variable.

License MITpermissive5 versions1 maintainers0 deps696 weekly dl
49
/ 100
Health
safe to use

[email protected]_6.1 is safe to use (health: 49/100)

Health breakdown0 – 100
10/25
maintenance
3/20
popularity
25/25
security
9/15
maturity
2/15
community
Vulnerabilities
0
none known

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First published · 2021-05-22 08:45:12.651000+00:00

Last updated · 2025-09-11 22:48:33.402000+00:00

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