Imports: spatstat (>= 1.42-2), methods, stats, utils

Imports: spatstat (>= 1.42-2), methods, stats, utils, mvtnorm, ks,
an R package' Seibold (2012) <http://highriskzone.r-forge.r-project.org/Bachelorarbeit.pdf>
and 'Determining high-risk zones for unexploded World War II bombs by using point

Imports: spatstat (>= 1.54-0), methods, stats, utils, mvtnorm, ks,

deldir, Matrix, maps, spatstat.geom, spatstat.core

deldir, Matrix, maps, spatstat.random, spatstat.geom,

Authors@R: c(person("Florian", "Schwendinger", email = "florianschwendinger@gmx.at", role = c("aut", "cre")),
person("Dirk", "Schumacher", role = "aut"),
person("Ivet", "Galabova", role = "cph"),
person("Leona", "Gottwald", role = "cph"),
person("Michael", "Feldmeier", role = "cph"))
Description: R interface to 'HiGHS', an optimization solver for solving mixed integer
optimization problems with quadratic or linear objective and linear constraints.
SystemRequirements: Bash, PkgConfig, ZLIB (>=1.2.3), CMAKE (>=3.15),

SystemRequirements: Bash, PkgConfig, CMAKE (>=3.16), C++11

Title: Hierarchical Integrative Group LASSO
Environmental health studies are increasingly measuring multiple pollutants
to characterize the joint health effects attributable to exposure mixtures.
However, the underlying dose-response relationship between toxicants and
health outcomes of interest may be highly nonlinear, with possible nonlinear
interaction effects. Hierarchical integrative group least absolute shrinkage
and selection operator (HiGLASSO), developed by Boss et al (2020)
<arXiv:2003.12844>, is a general framework to identify noteworthy nonlinear
main and interaction effects in the presence of group structures among a set
Imports: gcdnet, gglasso, purrr, splines, Rcpp

Title: Statistical Inference for Online Learning and Stochastic
person("Weijie", "Su", email = "suw@wharton.upenn.edu", role = "aut"),
person("Yuancheng", "Zhu", email = "yuancheng.zhu@gmail.com", role = c("aut", "cre")))
Description: Implements the Hierarchical Incremental GRAdient Descent (HiGrad) algorithm,
a first-order algorithm for finding the minimizer of a function in online learning just like stochastic gradient descent (SGD).
In addition, this method attaches a confidence interval to assess the uncertainty of its predictions.
See Su and Zhu (2018) <arXiv:1802.04876> for details.
Packaged: 2018-03-14 13:29:18 UTC; Yuancheng