Package: monomvn 1.9-21

Robert B. Gramacy

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:Robert B. Gramacy [aut, cre]

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monomvn/json (API)

# Install 'monomvn' in R:
install.packages('monomvn', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
Datasets:
  • 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.

3.55 score 4 stars 125 scripts 1.2k downloads 2 mentions 15 exports 5 dependencies

Last updated 3 months agofrom:32ab4cac6c. Checks:OK: 2. Indexed: no.

TargetResultDate
Doc / VignettesOKNov 23 2024
R-4.5-linux-x86_64OKNov 23 2024

Exports:add.pe.QPbhsblassobmonomvnbridgedefault.QPEllik.normkl.normmonomvnmonomvn.solve.QPrandmvnregressrmonormse.muSrwish

Dependencies:larsMASSmvtnormplsquadprog