Algorithms compute concave convex (CC) estimators including robust (penalized) generalized linear models and robust support vector machines via the COCO - composite optimization by conjugation operator. 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 (2019) <arXiv:1912.11119>,
Wang (2020) <arXiv:2010.02848>.

Algorithms compute concave convex (CC) estimators including robust (penalized) generalized linear models and robust support vector machines via the COCO - composite optimization by conjugation operator. 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>.

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>.