Interfacing to Nocedal et al. L-BFGS-B.3.0
(See <http://users.iems.northwestern.edu/~nocedal/lbfgsb.html>)
limited memory BFGS minimizer with bounds on parameters.
This is a fork of 'lbfgsb3'.
This registers a 'R' compatible 'C' interface to L-BFGS-B.3.0 that uses the same
function types and optimization as the optim() function (see writing 'R' extensions
and source for details).  This package also adds more stopping criteria as well
as allowing the adjustment of more tolerances.
optimx, microbenchmark, testthat, knitr, pkgbuild,
RcppArmadillo, rmarkdown

microbenchmark, testthat, knitr, pkgbuild, RcppArmadillo,
rmarkdown

optimx, microbenchmark, testthat, knitr, pkgbuild, rmarkdown,
RcppArmadillo

Length-biased correction to survival curve estimation.
Pierre-Jerome Bergeron and Vahid Partovi Nia
The package offers various length-bias corrections to
survival curve estimation.

Log Binomial Regression Model in Exact Method
Fit the log binomial regression model (LBM) by Exact method. Limited parameter space of
LBM causes trouble to find admissible estimates and fail to converge when MLE is close to or on
the boundary of space. Exact method utilizes the property of boundary vectors to re-parametrize
the model without losing any information, and fits the model on the standard fitting algorithm
with no convergence issues.

Implementation of Logistic Box-Cox Regression
c(
person("Li", "Xing", email="sfulxing@gmail.com", role=c("cre", "aut")),
person("Kohlton", "Booth", email="booth.kohl@gmail.com", role="aut"),
person("Xuekui", "Zhang", role="aut"),
person("Igor", "Burstyn", role="aut"),
person("Paul", "Gustafson", role="aut"))
Implements a logistic box-cox model. This model is fully described in Xing, L. et al. (2021) <doi:10.1002/cjs.11587>.
survey, maxLik, doParallel, parallel, foreach
Li Xing [cre, aut],
Kohlton Booth [aut],
Xuekui Zhang [aut],
Igor Burstyn [aut],
Paul Gustafson [aut]

survey, maxLik, doParallel, foreach, MASS

Log-Binomial Regression with Constrained Optimization
Maximum likelihood estimation of log-binomial regression with special functionality when the MLE is on (or close to) the boundary of the parameter space.
Mateus Carbone Ananias and Bernardo B. Andrade