person(given = c("Jose", "Luis"), family = "Morales", role = "aut"))

Title: Limited Memory BFGS Minimizer with Bounds on Parameters with
Authors@R: c(person(given = c("Matthew", "L"), family = "Fidler", role =
c("aut", "cre"), email = "matthew.fidler@gmail.com"),
person(given = c("John", "C"), family = "Nash", role =
Maintainer: Matthew L Fidler <matthew.fidler@gmail.com>
Description: Interfacing to Nocedal et al. L-BFGS-B.3.0 (2011 <doi:10.1145/2049662.2049669>) 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).  Ths package also adds more stopping criterion as well as allows adjusting more tolerances.
Imports: Rcpp (>= 0.12.3), numDeriv, methods

Description: Interfacing to Nocedal et al. L-BFGS-B.3.0
limited memory BFGS minimizer with bounds on parameters.
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.
Suggests: optimx, microbenchmark, testthat, knitr, pkgbuild,

Suggests: microbenchmark, testthat, knitr, pkgbuild, RcppArmadillo,

Suggests: optimx, microbenchmark, testthat, knitr, pkgbuild, rmarkdown,

Title: Length-biased correction to survival curve estimation.
Author: Pierre-Jerome Bergeron and Vahid Partovi Nia
Maintainer: Vahid Partovi Nia <vahid.partovinia@polymtl.ca>
Description: The package offers various length-bias corrections to

Title: Log Binomial Regression Model in Exact Method
Description: 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