Title: Nonlinear Mixed Effects Models in Population PK/PD, Plot
person("Bill", "Denney", , "wdenney@humanpredictions.com", role = "ctb",
'ggplot2' plotting methods for 'nlmixr2' objects.
Imports: ggplot2, nlmixr2est, nlmixr2extra, rxode2, utils, vpc, xgxr
Suggests: testthat (>= 3.0.0), dplyr, withr, nlmixr2data

person("Bill", "Denney", email="wdenney@humanpredictions.com", role = "ctb",
person("Wenping", "Wang", email="wwang8198@gmail.com", role = "aut"),
person("Vipul", "Mann", email="vm2583@columbia.edu", role = "aut")

Title: Generalized Laplace Mixed-Effects Models
Authors@R: c(person(given="Marco", family="Geraci", role=c("aut", "cph", "cre"), email="geraci@mailbox.sc.edu", comment = c(ORCID = "0000-0002-6311-8685")), person(given="Alessio", family="Farcomeni", role=c("ctb"), email="alessio.farcomeni@uniroma1.it"))
Imports: utils, lqmm, HI, mvtnorm, MASS, numDeriv, statmod, stats,
Description: Provides functions to fit linear mixed models
based on convolutions of the generalized Laplace (GL) distribution.
The GL mixed-effects model includes four special cases with normal random
effects and normal errors (NN), normal random effects and Laplace errors (NL),
Laplace random effects and normal errors (LN), and Laplace random effects
and Laplace errors (LL). The methods are described in Geraci and Farcomeni (2020,
Statistical Methods in Medical Research) <doi:10.1177/0962280220903763>.

Package: nlmrt
Type: Package
Title: Functions for nonlinear least squares solutions
Version: 2012-12.16
Date: 2012-12-16
Author: John C. Nash
Maintainer: John C. Nash <nashjc@uottawa.ca>
Description: nlmrt provides tools for working with nonlinear least
squares problems using a calling structure similar to, but much
simpler than, that of the nls() function. Moreover, where nls()
specifically does NOT deal with small or zero residual
problems, nlmrt is quite happy to solve them. It also attempts
to be more robust in finding solutions, thereby avoiding
'singular gradient' messages that arise in the Gauss-Newton
method within nls(). The Marquardt-Nash approach in nlmrt
generally works more reliably to get a solution, though this
may be one of a set of possibilities, and may also be
statistically unsatisfactory.  Added print and summary as of
August 28, 2012.
License: GPL-2
Depends: R (>= 2.15.0)
Suggests: minpack.lm, optimx, Rvmmin, Rcgmin, numDeriv
Packaged: 2012-12-16 17:28:40 UTC; john
Repository: CRAN
Date/Publication: 2012-12-17 10:47:47