Overview | Basic Usage Example | Inference Under Right Censoring | Diagnostic Plots | References
Package: MYIS 0.1.0
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).
Authors:
MYIS_0.1.0.tar.gz
MYIS_0.1.0.zip(r-4.7-any)MYIS_0.1.0.zip(r-4.6-any)MYIS_0.1.0.zip(r-4.5-any)
MYIS_0.1.0.tgz(r-4.6-any)MYIS_0.1.0.tgz(r-4.5-any)
MYIS_0.1.0.tar.gz(r-4.7-any)MYIS_0.1.0.tar.gz(r-4.6-any)
MYIS_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
MYIS/json (API)
| # Install 'MYIS' in R: |
| install.packages('MYIS', repos = c('https://shikhartyagi.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated from:ab601594a1. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel | OK | 146 | ||
| source / vignettes | OK | 168 | ||
| linux-release | OK | 133 | ||
| macos-release | OK | 165 | ||
| macos-oldrel | OK | 122 | ||
| windows-devel | OK | 148 | ||
| windows-release | OK | 106 | ||
| windows-oldrel | OK | 114 | ||
| wasm-release | OK | 81 |
Exports:batch_means_covbuild_negative_log_posterioreffective_sample_size_ismy_envelopemy_is_estimatesafe_expsafe_logsample_pi_lambda_hmcsample_pi_lambda_malasample_pi_lambda_rwmweighted_quantiles
Dependencies:
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Compute Batch-Means Asymptotic Covariance Matrix | batch_means_cov |
| Construct Negative Log-Posterior Potential Function | build_negative_log_posterior |
| Compute Importance Sampling Effective Sample Size | effective_sample_size_is |
| Moreau-Yosida Envelope and Proximal Mapping Computation | my_envelope |
| Moreau-Yosida MCMC Importance Sampling Estimator | my_is_estimate |
| Diagnostic Plots for Moreau-Yosida Importance Sampling | plot.myis |
| Print Moreau-Yosida Importance Sampling Results | print.myis |
| Print Summary of Moreau-Yosida Importance Sampling Object | print.summary.myis |
| Numerically Safe Exponential | safe_exp |
| Numerically Safe Logarithm | safe_log |
| Hamiltonian Monte Carlo targeting Moreau-Yosida Density | sample_pi_lambda_hmc |
| Metropolis-Adjusted Langevin Algorithm targeting Moreau-Yosida Density | sample_pi_lambda_mala |
| Random Walk Metropolis targeting Moreau-Yosida Density | sample_pi_lambda_rwm |
| Summary of Moreau-Yosida Importance Sampling Object | summary.myis |
| Compute Weighted Quantiles (Chen and Shao Method) | weighted_quantiles |