This algorithm provides a numerical solution to the
problem of unconstrained local minimization (or maximization). It is particularly suited for complex problems and more efficient than
the Gauss-Newton-like algorithm when starting from points very
far from the final minimum (or maximum). Each iteration is parallelized and convergence relies on a stringent stopping criterion based on the first and second derivatives. See Philipps et al, 2020 <arXiv:2009.03840>.

This algorithm provides a numerical solution to the
problem of unconstrained local minimization (or maximization). It is particularly suited for complex problems and more efficient than
the Gauss-Newton-like algorithm when starting from points very
far from the final minimum (or maximum). Each iteration is parallelized and convergence relies on a stringent stopping criterion based on the first and second derivatives. See Philipps et al, 2021 <doi:10.32614/RJ-2021-089>.

c(person("Alencar", "Xavier", role=c("aut", "cre"), email = "alenxav@gmail.com"),person("Shizhong", "Xu", role=c("aut", "ctb")))
Alencar Xavier [aut, cre],
Shizhong Xu [aut, ctb]
Genome-wide association analysis with membership information, variance adjustment, and correlated responses.

c(person("Kelly", "McConville", role = c("aut", "cre", "cph"),
email = "mcconville@reed.edu"),
person("Becky", "Tang", role = "aut"),
person("George", "Zhu", role = "aut"),
person("Sida", "Li", role = "ctb"),
person("Shirley", "Chueng", role = "ctb"),
person("Daniell", "Toth", role = c("ctb", "cph"),
comment = "Author and copyright holder of treeDesignMatrix helper function")
)
A set of model-assisted survey estimators and corresponding
variance estimators for single stage, unequal probability, without replacement
sampling designs.  All of the estimators can be written as a generalized
regression estimator with the Horvitz-Thompson, ratio, post-stratified, and
regression estimators summarized by Sarndal et al. (1992, ISBN:978-0-387-40620-6).
Two of the estimators employ a statistical learning model as the assisting model:
the elastic net regression estimator, which is an extension of the lasso regression
estimator given by McConville et al. (2017) <doi:10.1093/jssam/smw041>, and the
regression tree estimator described in McConville and Toth (2017) <arXiv:1712.05708>.
The variance estimators which approximate the joint inclusion probabilities can
be found in Berger and Tille (2009) <doi:10.1016/S0169-7161(08)00002-3> and the
bootstrap variance estimator is presented in Mashreghi et al. (2016)
<doi:10.1214/16-SS113>.
MASS, glmnet, Matrix, foreach, survey, dplyr, magrittr, rpms,
boot, stats, Rdpack
'gregt.R' 'varMase.R' 'GREG.R' 'gregElasticNett.R'
'gregElasticNet.R' 'gregTree.R' 'gregtreet.R' 'htt.R'
'horvitzThompson.R' 'logisticGregElasticNett.R'
'logisticGregt.R' 'postStratt.R' 'postStrat.R'
'ratioEstimatort.R' 'ratioEstimator.R' 'treeDesignMatrix.R'
Kelly McConville [aut, cre, cph],
Becky Tang [aut],
George Zhu [aut],
Sida Li [ctb],
Shirley Chueng [ctb],
Daniell Toth [ctb, cph] (Author and copyright holder of
treeDesignMatrix helper function)