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monomvn
View on CRAN: Click
here
Download and install monomvn package within the R console
Install from CRAN:
install.packages("monomvn")
Install from Github:
library("remotes")
install_github("cran/monomvn")
Install by package version:
library("remotes")
install_version("monomvn", "1.9-21")
Attach the package and use:
library("monomvn")
Maintained by
Robert B. Gramacy
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
10.32614/CRAN.package.monomvn . https://bobby.gramacy.com/r_packages/monomvn/ . monomvn results . monomvn.pdf . monomvn_1.9-21.tar.gz . monomvn_1.9-21.zip . monomvn_1.9-21.zip . monomvn_1.9-21.zip . monomvn_1.9-21.tgz . monomvn_1.9-21.tgz . monomvn_1.9-21.tgz . monomvn_1.9-21.tgz . monomvn archive . https://CRAN.R-project.org/package=monomvn .
First Published: 2007-06-11
Latest Update: 2024-01-11
Description:
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) .
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.
How to cite:
Robert B. Gramacy (2007). monomvn: Estimation for MVN and Student-t Data with Monotone Missingness. R package version 1.9-21, https://cran.r-project.org/web/packages/monomvn. Accessed 04 May. 2025.
Previous versions and publish date:
1.0-1 (2007-06-11 21:16), 1.0-2 (2007-07-10 14:12), 1.0-3 (2007-08-04 10:04), 1.0 (2007-06-11 14:42), 1.1-1 (2007-08-25 18:49), 1.1-2 (2007-11-03 19:34), 1.1-4 (2007-11-11 21:02), 1.1 (2007-08-13 16:26), 1.2 (2008-05-19 20:29), 1.3-1 (2008-06-20 10:36), 1.3 (2008-06-10 11:22), 1.4-1 (2008-09-22 16:58), 1.4 (2008-07-20 11:09), 1.5 (2008-11-13 16:45), 1.6-1 (2008-12-11 19:14), 1.6 (2008-12-04 10:49), 1.7-1 (2009-05-27 16:23), 1.7-2 (2009-06-02 19:29), 1.7-3 (2009-08-19 10:15), 1.7-4 (2009-11-17 21:29), 1.7 (2009-04-15 20:13), 1.8-1 (2010-03-09 12:37), 1.8-2 (2010-04-22 08:21), 1.8-3 (2010-04-25 09:26), 1.8-4 (2011-02-26 09:22), 1.8-5 (2011-03-08 08:27), 1.8-6 (2011-07-23 15:57), 1.8-7 (2011-12-17 16:25), 1.8-8 (2011-12-19 12:17), 1.8-9 (2012-01-07 18:54), 1.8-10 (2012-04-19 09:58), 1.8 (2010-01-14 12:58), 1.9-1 (2012-06-25 11:20), 1.9-2 (2012-07-01 11:18), 1.9-3 (2012-10-25 17:45), 1.9-4 (2013-04-18 21:41), 1.9-5 (2014-05-21 19:01), 1.9-6 (2016-02-11 00:43), 1.9-7 (2017-01-08 15:02), 1.9-8 (2018-09-14 19:40), 1.9-9 (2019-03-07 09:50), 1.9-10 (2019-06-11 18:20), 1.9-13 (2019-11-28 00:10), 1.9-14 (2022-03-30 21:30), 1.9-15 (2022-04-06 16:22), 1.9-16 (2022-10-20 01:02), 1.9-17 (2023-01-06 16:40), 1.9-18 (2023-08-21 09:32), 1.9-19 (2023-11-28 08:10), 1.9-20 (2024-01-11 15:20), 1.9 (2012-06-21 07:48)
Other packages that cited monomvn R package
View monomvn citation profile
Other R packages that monomvn depends,
imports, suggests or enhances
Complete documentation for monomvn
Functions, R codes and Examples using
the monomvn R package
Some associated functions: blasso . blasso.s3 . bmonomvn . cement . default.QP . metrics . monomvn-internal . monomvn-package . monomvn . monomvn.s3 . monomvn.solve.QP . plot.monomvn . randmvn . regress . returns . rmono . rwish .
Some associated R codes: addy.R . blasso.R . blasso.s3.R . bmonomvn.R . bmonomvn.traces.R . default.QP.R . kl.norm.R . monomvn.R . monomvn.s3.R . randmvn.R . regress.R . regress.fact.R . regress.lars.R . regress.ls.R . regress.pls.R . regress.ridge.R . rmono.R . rwish.R . util.R . Full monomvn package functions and examples
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