'aaa_growthmodel-class.R' 'aab_growthmodel-constructor.R'
'aac_classes.R' 'aad_set_generics.R' 'all_easylinear.R'
'all_growthmodels.R' 'all_splines.R' 'antibiotic.R'
'bactgrowth.R' 'cost.R' 'extcoef_logistic.R' 'extract.R'
'fit_easylinear.R' 'fit_growthmodel.R' 'fit_spline.R'
'grow_baranyi.R' 'grow_exponential.R' 'grow_genlogistic.R'
'grow_gompertz.R' 'grow_huang.R' 'grow_logistic.R'
'grow_richards.R' 'grow_twostep.R' 'growthrates-package.R'
'lm_window.R' 'methods.R' 'multisplit.R' 'names.R'
'parse_formula.R' 'parse_formula_nonlin.R' 'plot.R' 'predict.R'
'utilities.R'

c(person("Thomas","Petzoldt", role = c("aut", "cre"),
email = "thomas.petzoldt@tu-dresden.de",
comment = c(ORCID = "0000-0002-4951-6468")))
'aaa_growthmodel-class.R' 'aab_growthmodel-constructor.R'
'aac_classes.R' 'aad_set_generics.R' 'all_easylinear.R'
'all_growthmodels.R' 'all_splines.R' 'antibiotic.R'
'bactgrowth.R' 'cost.R' 'extcoef_logistic.R' 'extract.R'
'fit_easylinear.R' 'fit_growthmodel.R' 'fit_spline.R'
'grow_baranyi.R' 'grow_exponential.R' 'grow_genlogistic.R'
'grow_gompertz.R' 'grow_gompertz2.R' 'grow_huang.R'
'grow_logistic.R' 'grow_richards.R' 'grow_twostep.R'
'growthrates-package.R' 'lm_window.R' 'methods.R'
'multisplit.R' 'names.R' 'parse_formula.R'
'parse_formula_nonlin.R' 'plot.R' 'predict.R' 'utilities.R'
Thomas Petzoldt [aut, cre] (<https://orcid.org/0000-0002-4951-6468>)

Penalized Cox Model for High-Dimensional Data with Grouped
Predictors
Fit the penalized Cox models with both non-overlapping and overlapping grouped penalties including the group lasso, group smoothly clipped absolute deviation, and group minimax concave penalty. The algorithms combine the MM approach and group-wise descent with some computational tricks including the screening, active set, and warm-start. Different tuning regularization parameter methods are provided.

Fit the penalized Cox models with both non-overlapping and overlapping grouped penalties including the group lasso, group smoothly clipped absolute deviation (SCAD), and group minimax concave penalty (MCP). The algorithms combine the Majorization Minimization (MM) approach and group-wise descent with some computational tricks including the screening, active set, and warm-start. Different tuning regularization parameter methods are provided.