c(
person(given="Joshua",family="Lambert",email="joshua.lambert@uky.edu",role=c("aut","cre")),
person(given="Liyu",family="Gong",email="liyu.gong@uky.edu",role="aut"),
person(given="Corrine",family="Elliott",email="corrine.elliott@uky.edu",role="aut"),
person(given="Sarah",family="Janse",email="sawi234@g.uky.edu",role="ctb"))
Assists in statistical model building to find optimal and semi-optimal higher order interactions
and best subsets. Uses the lm(), glm(), and other R functions to fit models generated from a feasible
solution algorithm. Discussed in Subset Selection in Regression, A Miller (2002). Applied and explained
for least median of squares in Hawkins (1993) <doi:10.1016/0167-9473(93)90246-P>. The feasible solution
algorithm comes up with model forms of a specific type that can have fixed variables, higher order
interactions and their lower order terms.
Joshua Lambert [aut, cre],
Liyu Gong [aut],
Corrine Elliott [aut],
Sarah Janse [ctb]

c(
person(given="Joshua",family="Lambert",email="joshua.lambert@uc.edu",role=c("aut","cre")),
person(given="Liyu",family="Gong",email="liyu.gong@uky.edu",role="aut"),
person(given="Corrine",family="Elliott",email="cfelliott@berkeley.edu",role="aut"),
person(given="Sarah",family="Janse",email="Sarah.Janse@osumc.edu",role="ctb"))
parallel, methods, tibble, rPref, tidyr, hash

c(
person(given="Joshua",family="Lambert",email="joshua.lambert@uky.edu",role=c("aut","cre")),
person(given="Liyu",family="Gong",email="liyu.gong@uky.edu",role="aut"),
person(given="Corrine",family="Elliott",email="corrine.elliott@uky.edu",role="ctb"),
person(given="Sarah",family="Janse",email="sawi234@g.uky.edu",role="ctb"))
Joshua Lambert [aut, cre],
Liyu Gong [aut],
Corrine Elliott [ctb],
Sarah sawi234@g.uky.edu Janse [ctb]

An efficient C++ based implementation of "Follow The (Proximally) Regularized Leader" online learning algorithm.
This algorithm was proposed in McMahan et al. (2013) <DOI:10.1145/2487575.2488200>.
stats, utils, data.table (>= 1.9.6), Matrix, FeatureHashing,
magrittr, foreach, Rcpp, RcppProgress