mpath - Regularized Linear Models
Algorithms compute robust estimators for loss functions in
the concave convex (CC) family by the iteratively reweighted
convex optimization (IRCO), an extension of the iteratively
reweighted least squares (IRLS). The IRCO reduces the weight of
the observation that leads to a large loss; it also provides
weights to help identify outliers. Applications include robust
(penalized) generalized linear models and robust support vector
machines. The package also contains penalized Poisson, negative
binomial, zero-inflated Poisson, zero-inflated negative
binomial regression models and robust models with non-convex
loss functions. Wang et al. (2014) <doi:10.1002/sim.6314>, Wang
et al. (2015) <doi:10.1002/bimj.201400143>, Wang et al. (2016)
<doi:10.1177/0962280214530608>, Wang (2021)
<doi:10.1007/s11749-021-00770-2>, Wang (2020)
<arXiv:2010.02848>.
