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ConstrainedLasso.jl implements algorithms for fitting the constrained lasso problem
where is the response vector,
is the design matrix of predictor or covariates,
is the vector of unknown regression coefficients, and
is a tuning parameter that controls the amount of regularization.
Within Julia, use the package manager to install ConstrainedLasso:
Pkg.clone("git://github.com/Hua-Zhou/ConstrainedLasso.jl.git")
This package supports Julia v0.6.
The original method paper on the constrained lasso is
James, G. M., Paulson, C. and Rusmevichientong, P. (2013). "Penalized and constrained regression," mimeo, Marshall School of Business, University of Southern California. http://www-bcf.usc.edu/~gareth/research/PAC.pdf
If you use ConstrainedLasso package in your research, please cite the following paper on the algorithms:
Gaines, B., Kim, J. and Zhou, H. (2018). “Algorithms for Fitting the Constrained Lasso,” under revision.