Linear least absolute deviations with L1 regularization.
In estimation theory terms, this is the Maximum A Posterior (MAP) estimator for a Laplacian likelihood with Laplacian prior, i.e.
The algorithm yield by the Majorization-Minimization framework turns out to be
an iteratively reweighted least-squares. See
To install the development version, proceed as follows:
git clone https://github.com/mirca/lad.git pip install -e lad
Or install the lastest version on PyPi:
pip install lad
- numpy - tensorflow - pytest - pytest-cov
Inside the R console, type: