Tools to set up, train, store, load, investigate and analyze
generative neural networks. In particular, functionality for
generative moment matching networks is provided.
See Hofert, Prasad, Zhu (2018) <arXiv:1811.00683>
for more details.

Tools to set up, train, store, load, investigate and analyze
generative neural networks. In particular, functionality for
generative moment matching networks is provided.
keras, tensorflow, methods, R6, qrng, tools, copula

Provides a console for R under Unix-alikes based on GNOME
GNOME development libraries, R built as a shared object.

GNOME development libraries,
R built as a shared or static (R >= 2.7.0) library

GNOME development libraries, R built as a shared or
static library

Estimate Natural Mortality for Different Life Stages
c(person("Josymar", "Torrejon-Magallanes", email = "ejosymart@gmail.com", role = c("cre", "aut")))
Estimate natural mortality (M) throughout the life history for organisms, mainly fish and invertebrates, based on gnomonic interval approach proposed by Caddy (1996) <doi:10.1051/alr:1996023> and Martinez-Aguilar et al. (2005) <doi:10.1016/j.fishres.2004.04.008>.
It includes estimation of duration of each gnomonic interval (life stage), the constant probability of death (G), and some basic plots.
minqa, triangle, grDevices, graphics, stats, utils

minqa, triangle, grDevices, graphics, stats, utils, kableExtra

Functions for obtaining generalized normal/exponential power distribution probabilities, quantiles, densities and random deviates. The generalized normal/exponential power distribution was introduced by Subbotin (1923) and rediscovered by Nadarajah (2005). The parametrization given by Nadarajah (2005) <doi:10.1080/02664760500079464> is used.

person(given = "David",
family = "Jin",
role = c("aut", "cre"),
email = "djin6817@gmail.com")
Estimation of gross output production functions and productivity in the presence of numerous fixed (nonflexible) and a single flexible input using the nonparametric identification strategy specified in Gandhi, Navarro, and Rivers (2020) <doi:10.1086/707736>. Monte Carlo evidence from the paper demonstrates high performance in estimating production function elasticities.