c(
person("John","Deck", email = "jdeck88@gmail.com",
role = c("aut", "cre")),
person("Brian","Stucky",email = "stuckyb@flmnh.ufl.edu",
role = "aut"),
person("Ramona","Walls", email = "rwalls@cyverse.org",
role = "aut"),
person("Kjell", "Bolmgren", email = "Kjell.Bolmgren@slu.se",
role = "aut"),
person("Ellen", "Denny", email = "ellen@usanpn.org",
role = "aut"),
person("Robert","Guralnick", email = "rguralnick@flmnh.ufl.edu",
role = "aut")
)

Regularized PQL for Joint Selection in GLMMs
Francis K.C. Hui, Samuel Mueller, A.H. Welsh
Performs joint selection in Generalized Linear Mixed Models (GLMMs) using penalized likelihood methods. Specifically, the Penalized Quasi-Likelihood (PQL) is used as a loss function, and penalties are then "added on" to perform simultaneous fixed and random effects selection. Regularized PQL avoids the need for integration (or approximations such as the Laplace's method) during the estimation process, and so the full solution path for model selection can be constructed relatively quickly.
gamlss.dist, lme4, Matrix, MASS, mvtnorm

gamlss.dist, lme4, Matrix, MASS, mvtnorm, Rcpp,

Francis K.C. Hui <fhui28@gmail.com>, with contributions from Samuel Mueller <samuel.mueller@sydney.edu.au> and A.H. Welsh <Alan.Welsh@anu.edu.au>
Performs joint selection in Generalized Linear Mixed Models (GLMMs) using penalized likelihood methods. Specifically, the Penalized Quasi-Likelihood (PQL) is used as a loss function, and penalties are then augmented to perform simultaneous fixed and random effects selection. Regularized PQL avoids the need for integration (or approximations such as the Laplace's method) during the estimation process, and so the full solution path for model selection can be constructed relatively quickly.

Francis K.C. Hui <francis.hui@gmail.com>, with contributions from Samuel Mueller <samuel.mueller@sydney.edu.au> and A.H. Welsh <Alan.Welsh@anu.edu.au>

person('Daniel', 'Kovtun', email = 'quantumfusetrader@gmail.com', role = c('cre', 'aut'))
Wrapper to retrieve market data, explore available markets, and plot historical price data from the 'PredictIt' public API (<https://www.predictit.org/api/marketdata/all/>).
The package comes with a demo 'shiny' application for illustrating example use cases.
License to use data made available via the API is for non-commercial use and 'PredictIt' is the sole source of such data.
httr, jsonlite, dplyr, DT, dygraphs, magrittr, stringr,
quantmod, xts, shiny