Functions and datasets used in the book: Fernandez-Casal, R., 
    Costa, J. and Oviedo-de la Fuente, M. (2024) "Metodos predictivos de 
    aprendizaje estadistico" <https://rubenfcasal.github.io/aprendizaje_estadistico/>.

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 (2024) <doi:10.1111/anzs.12409>.

Provides tools for importing and cleaning Experience Sampling
    Method (ESM) data collected via the 'm-Path' platform. The goal is
    to provide with a few utility functions to be able to read and perform
    some common operations in ESM data collected through the 'm-Path'
    platform (<https://m-path.io/landing/>). Functions include raw data
    handling, format standardization, and basic data checks, as well as to
    calculate the response rate in data from ESM studies.

Overcomes one of the major challenges in mobile (passive)
    sensing, namely being able to pre-process the raw data that comes from
    a mobile sensing app, specifically 'm-Path Sense' <https://m-path.io>.
    The main task of 'mpathsenser' is therefore to read 'm-Path Sense'
    JSON files into a database and provide several convenience functions
    to aid in data processing.

Performs treatment allocation in two-arm clinical trials by the maximal procedure described by Berger et al. (2003) <doi:10.1002/sim.1538>. To that end, the algorithm provided by Salama et al. (2008) <doi:10.1002/sim.3014> is implemented.