Package: monomvn 1.9-21
monomvn: Estimation for MVN and Student-t Data with Monotone Missingness
Estimation of multivariate normal (MVN) and student-t data of arbitrary dimension where the pattern of missing data is monotone. See Pantaleo and Gramacy (2010) <doi:10.48550/arXiv.0907.2135>. Through the use of parsimonious/shrinkage regressions (plsr, pcr, lasso, ridge, etc.), where standard regressions fail, the package can handle a nearly arbitrary amount of missing data. The current version supports maximum likelihood inference and a full Bayesian approach employing scale-mixtures for Gibbs sampling. Monotone data augmentation extends this Bayesian approach to arbitrary missingness patterns. A fully functional standalone interface to the Bayesian lasso (from Park & Casella), Normal-Gamma (from Griffin & Brown), Horseshoe (from Carvalho, Polson, & Scott), and ridge regression with model selection via Reversible Jump, and student-t errors (from Geweke) is also provided.
Authors:
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monomvn.pdf |monomvn.html✨
monomvn/json (API)
# Install 'monomvn' in R: |
install.packages('monomvn', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
- cement - Hald's Cement Data
- cement.miss - Hald's Cement Data
- market - Financial Returns data from NYSE and AMEX
- market.test - Financial Returns data from NYSE and AMEX
- returns - Financial Returns data from NYSE and AMEX
- returns.test - Financial Returns data from NYSE and AMEX
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 3 months agofrom:32ab4cac6c. Checks:OK: 2. Indexed: no.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 23 2024 |
R-4.5-linux-x86_64 | OK | Nov 23 2024 |
Exports:add.pe.QPbhsblassobmonomvnbridgedefault.QPEllik.normkl.normmonomvnmonomvn.solve.QPrandmvnregressrmonormse.muSrwish