Description: Implementation of the algorithm introduced in Shah, R. D.
Data with thousands of predictors can be handled. The algorithm
performs sequential Lasso fits on design matrices containing

speed up subsequent fits, so the algorithm is very efficient.

Title: High-Dimensional Lasso Generalized Estimating Equations
Description: Fits generalized estimating equations with L1 regularization to longitudinal data with high dimensional covariates. Use a efficient iterative composite gradient descent algorithm.
Imports: Rcpp (>= 1.0.4), PGEE, MASS, mvtnorm, caret, SimCorMultRes

Author: Jonas Striaukas [aut, trl, cre] and Matthias Weber [aut]
Description: Contains functions to estimate a penalized regression model using 3CoSE algorithm, see Weber, Striaukas, Schumacher Binder (2018) <doi:10.2139/ssrn.3211163>.
Packaged: 2020-01-14 12:37:39 UTC; striaukas

Title: Sparsed Sliced Inverse Regression via Lasso
Description: Estimate the sufficient dimension reduction space using sparsed sliced inverse regression via Lasso (Lasso-SIR) introduced in Lin, Zhao, and Liu (2017) <arxiv:1611.06655>. The Lasso-SIR is consistent and achieve the optimal convergence rate under certain sparsity conditions for the multiple index models.

Title: Item Response Theory Implemented in R and Cpp
person("Milder Hernandez Cagua",email = "milderhc@gmail.com",role = "aut"),
person("Jhonatan Javier Guzman del Rio",email = "jhjguzmanri@unal.edu.co",role = "aut"),
person("Camilo Antonio Suarez Bolanos",email = "caasuarezbo@unal.edu.co",role = "cre"),
person("Alvaro Mauricio Montenegro",email = "ammontenegrod@unal.edu.co",role = "aut"),
person("Sergio Paez Moncaleano",email = "spaezm@unal.edu.co",role = "aut"),
person("Juan David Cortes Castillo",email = "jdcortesc@unal.edu.co",role = "aut"),
person("Campo Elias Pardo Turriago",email = "cepardot@unal.edu.co",role = "aut"),
person("Luisa Fernanda Parra Arboleda",email = "lfparraar@unal.edu.co",role = "aut"),
person("Emilio Pablo Berdugo Camacho",email = "epberdugoc@unal.edu.co",role = "aut"),
person("SICS Research Group",email = "sics_fcbog@unal.edu.co",role = "cph"))
Description: Provides a C++ implementation of the Multidimensional Item Response Theory (MIRT) capable of performing parameter and traits estimations. It also provides a list of options to perform an optimal analysis and obtain useful information about the resulting model. Acknowledgment: This work was Supported by Colciencias Research Grant 0039-2013 and SICS Research Group, Universidad Nacional de Colombia.
Imports: Rcpp (>= 0.12.6), randtoolbox, fastGHQuad, RSpectra,