'Common_functions.R' 'Compound.R' 'H5constants.R'
'Helper_functions.R' 'Misc.R' 'R6Classes.R' 'R6Classes_H5A.R'
'R6Classes_H5D.R' 'R6Classes_H5File.R' 'R6Classes_H5Group.R'
'R6Classes_H5P.R' 'R6Classes_H5R.R' 'R6Classes_H5S.R'
'R6Classes_H5T.R' 'adapt_during_onLoad.R' 'convert.R'
'factor_ext.R' 'globalVariables.R' 'h5errorHandling.R'
'h5wrapper.R' 'hdf5r.R' 'high_level_UI.R' 'open_objs.R' 'zzz.R'

testthat, knitr, rmarkdown, nycflights13, reshape2, formatR,

person("Michael", "Koohafkan", email = "michael.koohafkan@gmail.com", role = c("aut", "cre"))
Provides an interface to 'HDFql' <http://www.hdfql.com/>
and helper functions for reading data from and writing data to 'HDF5' files. 'HDFql'
provides a high-level language for managing 'HDF5' data that is platform independent.
For more information, see the reference manual
<http://www.hdfql.com/resources/HDFqlReferenceManual.pdf>.

Provides an interface to 'HDFql' <https://www.hdfql.com/>
and helper functions for reading data from and writing data to 'HDF5' files. 'HDFql'
provides a high-level language for managing 'HDF5' data that is platform independent.
For more information, see the reference manual
<https://www.hdfql.com/resources/HDFqlReferenceManual.pdf>.

Implementation of multiple approaches to perform inference in
high-dimensional models

Lukas Meier, Nicolai Meinshausen, Ruben Dezeure

Lukas Meier [aut, cre], Ruben Dezeure [aut], Nicolai Meinshausen [aut], Martin Maechler [aut], Peter Buehlmann [aut]
Implementation of multiple approaches to perform inference in high-dimensional models.
grDevices, graphics, stats, parallel, MASS, glmnet, linprog

Statistical Inference for Noisy Vector Autoregression
c(person("Xiang", "Lyu", role = c("aut","cre"), email="xianglyu@berkeley.edu"),
person("Jian", "Kang", role = c("aut")),
person("Lexin", "Li", role = c("aut")))
The model is high-dimensional vector autoregression with measurement error, also known as linear gaussian state-space model. Provable sparse expectation-maximization algorithm is provided for the estimation of transition matrix and noise variances. Global and simultaneous testings are implemented for transition matrix with false discovery rate control. For more information, see the accompanying paper: Lyu, X., Kang, J., & Li, L. (2020). "Statistical inference for high-dimensional vector autoregression with measurement error", arXiv preprint <arXiv:2009.08011>.
Xiang Lyu [aut, cre],
Jian Kang [aut],
Lexin Li [aut]