L1pack: Routines for L1 estimation

L1pack provides routines to perform L1 estimation in linear regression models and multivariate random number generation for the Laplace distribution. A basic set of methods for printing the results is also available.

Resources and Downloads
Installation Instructions
How to cite the package
About the Authors

Providing Feedback

Please report any bugs/suggestions/improvements to Felipe Osorio, Universidad Técnica Federico Santa María. If you find these routines useful or not then please let me know. Also, acknowledgement of the use of the routines is appreciated.

Resources

Latest binaries and sources for L1pack are availables from CRAN package repository .

L1pack_0.41-24.tar.gz - Package sources
L1pack_0.41-2.zip - Windows binaries (R-release)
L1pack_0.41-2.tgz - MacOS binaries (R-release, arm64)
L1pack_0.41-24.tgz - MacOS binaries (R-release, x86_64)
L1pack.pdf - Reference Manual

Source code is also available at GitHub: github.com/faosorios/L1pack

Installation instructions

To install this package, start R and enter:

install.packages("L1pack")

Alternatively, you can download the source as a tarball or as a zip file. Unpack the tarball or zipfile (thereby creating a directory named, L1pack) and install the package source by executing (at the console prompt)

R CMD INSTALL L1pack

Next, you can load the package by using the command: library(L1pack)

To cite the L1pack in publications use:

Osorio, F., and Wołodźko, T. (2023). Routines for L1 estimation. R package version 0.41-24. URL: http://l1pack.mat.utfsm.cl/

Some papers using L1pack:

Plate, M., Bernstein, R., Hoppe, A., Bienefeld, K. (2019). Comparison of infinitesimal and finite locus models for long-term breeding simulations with direct and maternal effects at the example of honeybess. PLOS ONE 14, e0213270.
Wang, W., Yu, P., Lin, L., Tong, T. (2019). Robust estimation of derivatives using locally weighted least absolute deviation regression. Journal of Machine Learning Research 20 (60), 1-49.
VandenHeuvel, D., Wu, J., Wang, Y.G. (2022). Robust regression for electricity demand forecast against cyberattacks. International Journal of Forecasting , doi: 10.1016/j.ijforecast.2022.10.004.

About the Authors

Felipe Osorio is Assistant Professor at Department of Mathematics of the Universidad Técnica Federico Santa María, Chile.

Webpage: fosorios.mat.utfsm.cl
Email: felipe.osorios AT usm.cl

Tymoteusz Wołodźko is Software Engineer and Machine Learning Engineer. Also is elected moderator for the CrossValidated.com

Webpage: twolodzko.github.io
Email: twolodzko+ghpage AT gmail.com

Felipe and Tymoteusz contribute as developers/maintainers of the following R packages:

extraDistr - Additional univariate and multivariate distributions.
fastmatrix - Fast computation of some matrices useful in statistics.
heavy - Robust estimation using heavy-tailed distributions.
MVT - Estimation and testing for the multivariate t-distribution.
SpatialPack - Package for analysis of spatial data.