Description: Provides an interface to 'HDFql' <https://www.hdfql.com/>
microbenchmark (>= 1.4), testthat (>= 2.1.0), rmarkdown (>=

Author: Lukas Meier, Nicolai Meinshausen
Description: Implementation of multiple approaches to perform inference in

Author: Lukas Meier, Nicolai Meinshausen, Ruben Dezeure

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

Title: Statistical Inference for Noisy Vector Autoregression
Authors@R: c(person("Xiang", "Lyu", role = c("aut","cre"), email="xianglyu@berkeley.edu"),
person("Jian", "Kang", role = c("aut")),
Description: 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>.

Title: Fits high dimensional linear models
Description: Mimics the lm function found in the recommended package
stats to fit high dimensional regression models with point
estimates, standard errors, and p-values. Methods for printing
and summarizing the results are given. Future work will
additionally include methods for plotting and predicting.

Title: Fitting High Dimensional Linear Models
and summarizing the results are given. Supports lasso
estimator, Dantzig selector, mc+, root-lasso, and scad. Methods
are provided for the resulting class hdlm corresponding to

Depends: R (>= 2.14), glmnet, foreach, MASS

Description: Mimics the lm function found in the package stats to fit
high dimensional regression models with point estimates,
standard errors, and p-values. Methods for printing and
Depends: R (>= 2.14), glmnet, foreach, MASS, iterators