MYIS
'Moreau-Yosida' Importance Sampling for Statistical Inference
Implements 'Moreau-Yosida' Markov chain Monte Carlo ('MCMC') importance sampling for parameter estimation and Bayesian inference under smooth, non-differentiable, or light-tailed target posterior distributions and arbitrary probability models with complete or censored data. Users supply user-defined probability density functions, optional distribution functions, parameter ranges, and observations subject to complete, right, left, interval, Type-I, Type-II, progressive Type-II, first-failure, or truncation schemes. Constructs 'Moreau-Yosida' envelopes, gradient-based proposals ('MALA', 'HMC', or 'RWM'), self-normalized importance weights, batch-means asymptotic variance estimates, and Bayesian marginal quantiles. Methodologies are based on 'Shukla', 'Vats', and 'Chi' (2025) <doi:10.48550/arXiv.2501.02228>, 'Pereyra' (2016) <doi:10.1111/sjos.12208>, 'Durmus' and others (2022) <doi:10.1214/22-EJS2027>, 'Chen' and 'Shao' (1999) <doi:10.1214/ss/1009211804>, 'Roberts' and 'Rosenthal' (1998) <doi:10.1214/aoap/1028903378>, 'Geweke' (1989) <doi:10.2307/2290062>, 'Hesterberg' (1995) <doi:10.1080/00031305.1995.10476138>, and 'Balakrishnan' and 'Aggarwala' (2000, ISBN:978-0-8176-4001-9).
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.1.0 |
rolling linux/jammy R-4.5 | MYIS_0.1.0.tar.gz |
111.1 KiB |
0.1.0 |
rolling linux/noble R-4.5 | MYIS_0.1.0.tar.gz |
110.8 KiB |
0.1.0 |
rolling source/ R- | MYIS_0.1.0.tar.gz |
37.9 KiB |
0.1.0 |
latest linux/jammy R-4.5 | MYIS_0.1.0.tar.gz |
111.1 KiB |
0.1.0 |
latest linux/noble R-4.5 | MYIS_0.1.0.tar.gz |
110.8 KiB |
0.1.0 |
latest source/ R- | MYIS_0.1.0.tar.gz |
37.9 KiB |
0.1.0 |
2026-04-23 source/ R- | MYIS_0.1.0.tar.gz |
0 B |