An implementation of the induced smoothing (IS) idea to lasso regularization models to allow estimation and inference on the model coefficients (currently hypothesis testing only). Linear, logistic, Poisson and gamma regressions with several link functions are implemented. The algorithm is described in the original paper; see <doi:10.1177/0962280219842890> and discussed in a tutorial <doi:10.13140/RG.2.2.16360.11521>.

We provide the collection of data-sets used in the book 'An Introduction to Statistical Learning with Applications in R'.

We provide the collection of data-sets used in the book 'An Introduction to Statistical Learning with Applications in R, Second Edition'. These include many data-sets that we used in the first edition (some with minor changes), and some new datasets.

The development of ISM was made by Warfield in 1974. 
      ISM is the process of collaborating distinct or related essentials into a simplified and an organized format. Hence, ISM is a methodology that seeks the interrelationships among the various elements considered and endows with a hierarchical and multilevel structure.
      To run this package user needs to provide a matrix (VAXO) converted into 0's and 1's.
      Warfield,J.N. (1974) <doi:10.1109/TSMC.1974.5408524>
      Warfield,J.N. (1974, E-ISSN:2168-2909).

Functions to support the computations carried out in
  `An Introduction to Statistical Modeling of Extreme Values' by
  Stuart Coles. The functions may be divided into the following 
  groups; maxima/minima, order statistics, peaks over thresholds
  and point processes.

Paquete creado con el fin de facilitar el cálculo y distribución del índice Socio Material Territorial (ISMT), elaborado por el Observatorio de Ciudades UC. La metodología completa está disponible en "ISMT" (<https://ideocuc-ocuc.hub.arcgis.com/datasets/6ed956450cfc4293b7d90df3ce3474e4/about>) [Observatorio de Ciudades UC (2019)]. || Package created to facilitate the calculation and distribution of the Socio-Material Territorial Index by Observatorio de Ciudades UC. The full methodology is available at "ISMT" (<https://ideocuc-ocuc.hub.arcgis.com/datasets/6ed956450cfc4293b7d90df3ce3474e4/about>) [Observatorio de Ciudades UC (2019)].