Penalized regression for generalized linear models for
measurement error problems (aka. errors-in-variables). The package
contains a version of the lasso (L1-penalization) which corrects
for measurement error (Sorensen et al. (2015)

The goal of hdme is to provide penalized regression methods for High-Dimensional Measurement Error problems (errors-in-variables).

Install `hdme`

from CRAN using.

install.packages("hdme")

You can install the latest development version from github with:

# install.packages("devtools")devtools::install_github("osorensen/hdme")

The package `Rglpk`

is suggested when installing `hdme`

. In order to
install `Rglpk`

on macOS, you may need to first install `GLPK`

by
issuing the following statement on the command line:

brew install glpk

Then install `Rglpk`

:

install.packages("Rglpk")

If you are not able to install `Rglpk`

, then please install the
suggested package `lpSolveAPI`

instead, using the command

install.packages("lpSolveAPI")

The functions in `hdme`

that use `Rglpk`

, will switch to `lpSolveAPI`

automatically if the former is not available.

hdme provides implementations of the following algorithms:

The methods implemented in the package include

- Corrected Lasso for Linear Models (Loh and Wainwright (2012))
- Corrected Lasso for Generalized Linear Models (Sorensen, Frigessi, and Thoresen (2015))
- Matrix Uncertainty Selector for Linear Models (Rosenbaum and Tsybakov (2010))
- Matrix Uncertainty Selector for Generalized Linear Models (Sorensen et al. (2018))
- Matrix Uncertainty Lasso for Generalized Linear Models (Sorensen et al. (2018))
- Generalized Dantzig Selector (James and Radchenko (2009))

James, Gareth M., and Peter Radchenko. 2009. “A Generalized Dantzig
Selector with Shrinkage Tuning.” *Biometrika* 96 (2): 323–37.

Loh, Po-Ling, and Martin J. Wainwright. 2012. “High-Dimensional
Regression with Noisy and Missing Data: Provable Guarantees with
Nonconvexity.” *Ann. Statist.* 40 (3). The Institute of Mathematical
Statistics: 1637–64.

Rosenbaum, Mathieu, and Alexandre B. Tsybakov. 2010. “Sparse Recovery
Under Matrix Uncertainty.” *Ann. Statist.* 38 (5): 2620–51.

Sorensen, Oystein, Arnoldo Frigessi, and Magne Thoresen. 2015.
“Measurement Error in Lasso: Impact and Likelihood Bias Correction.”
*Statistica Sinica* 25 (2). Institute of Statistical Science, Academia
Sinica: 809–29.

Sorensen, Oystein, Kristoffer Herland Hellton, Arnoldo Frigessi, and
Magne Thoresen. 2018. “Covariate Selection in High-Dimensional
Generalized Linear Models with Measurement Error.” *Journal of
Computational and Graphical Statistics*. Taylor & Francis.

Fixed random number seed issue which caused test to fail in R-devel.

Internal fix to `fit_corrected_lasso`

and `cv_corrected_lasso`

. Got rid of duplicated code by calling the function `set_radius`

.

Since `Rglpk`

does not install automatically on macOS, this package was moved from **Imports** to **Suggests**. In addition, `lpSolveAPI`

was added to **Suggests**. This means that the package should build also on systems that do not have `Rglpk`

, in particular the versions of macOS on CRAN.

The changes involved adding an optional linear solver from `lpSolveAPI`

in the function `musalgorithm()`

.

Added a `plot.gds`

function, which plots the coefficients estimated by `fit_gds`

.

Internal adjustment, which removed importing of external packages into the namespace, and instead specified the functions explicitly using `::`

.

`tidyverse`

has been removed from **Suggests** field `DESCRIPTION`

. `dplyr`

and `tidyr`

have been added instead. Similarly, `library(tidyverse)`

in the vignette has been replaced by `library(dplyr)`

, `library(tidyr)`

, and `library(ggplot2)`

.

`hdme`

is now on CRAN.