Imports: survival, rms, statmod, methods, coda, Runuran (>= 0.12), Rcpp
Title: Bayesian Fractional Polynomials for GLMs
Description: Implements the Bayesian paradigm
for fractional polynomials in generalized linear
models. See package 'bfp' for the treatment of normal

models, described by Held, Gravestock, Sabanes Bove (2015) <doi:10.1214/14-STS510>.
See package 'bfp' for the treatment of normal

Title: Adaptive LASSO and Network Regularized Generalized Linear Models
Description: Efficient procedures for adaptive LASSO and network regularized for Gaussian, logistic, and Cox model. Provides network estimation procedure (combination of methods proposed by Ucar, et al. (2007) <doi:10.1093/bioinformatics/btm423> and Meinshausen and Buhlmann (2006) <doi:10.1214/009053606000000281>), cross validation and stability selection proposed by Meinshausen and Buhlmann (2010) <doi:10.1111/j.1467-9868.2010.00740.x> and Liu, Roeder and Wasserman (2010) <arXiv:1006.3316> methods. Interactive R app is available.
Imports: Rcpp (>= 1.0.0), methods, stats, Matrix, ggplot2, gridExtra,
maxstat, survminer, plotROC, shiny, foreach, pROC, huge,

Depends: R (>= 3.5.0), survival, data.table

Description: Efficient procedures for adaptive LASSO and network regularized for Gaussian, logistic, and Cox model. Provides network estimation procedure (combination of methods proposed by Ucar, et. al (2007) <doi:10.1093/bioinformatics/btm423> and Meinshausen and Buhlmann (2006) <doi:10.1214/009053606000000281>), cross validation and stability selection proposed by Meinshausen and Buhlmann (2010) <doi:10.1111/j.1467-9868.2010.00740.x> and Liu, Roeder and Wasserman (2010) <arXiv:1006.3316> methods. Interactive R app is available.
Depends: R (>= 3.6.0), survival, data.table

Title: All Hierarchical Models for Generalized Linear Model
Description: Find all hierarchical models of specified generalized linear
model with information criterion (AIC, BIC, or AICc) within specified
cutoff of minimum value.  Use branch and bound algorithm so we do not