This is a Neural Network regression model implementation using 'Keras', consisting of 10 Long Short-Term Memory layers that are fully connected along with the rest of the inputs.

Finding Locally D-optimal optimal designs for some nonlinear and
generalized linear models.
Ehsan Masoudi, Majid Sarmad and Hooshang Talebi
this package provides functions for Finding Locally
D-optimal designs for Logistic, Negative Binomial, Poisson,
Michaelis-Menten, Exponential, Log-Linear, Emax, Richards,
Weibull and Inverse Quadratic regression models and also
functions for auto-constructing Fisher information matrix and
Frechet derivative based on some input variables and without
user-interfere.

Implementation of most popular approaches to PD (probability of default) calibration: Quasi Moment Matching algorithm (D.Tasche), algorithm proposed by M.van der Burgt, K.Pluto and D.Tasche's most prudent estimation methodology.

Implementation of most popular approaches to PD (probability of default) calibration: Quasi Moment Matching algorithm (D. Tasche), algorithm proposed by M. van der Burgt, K. Pluto and D. Tasche's most prudent estimation methodology.

c(person("Eero", "Liski", role = c("aut", "cre"), email =
"liskieero@gmail.com"), person("Klaus",
"Nordhausen", role = "aut"), person("Hannu", "Oja", role =
"aut"), person("Anne", "Ruiz-Gazen", role = "aut"))
Linear dimension reduction and the corresponding subspaces can be uniquely defined using orthogonal projection matrices. This package provides tools to compute distances between such subspaces and to compute the average subspace.
Eero Liski [aut, cre],
Klaus Nordhausen [aut],
Hannu Oja [aut],
Anne Ruiz-Gazen [aut]

c(person("Eero", "Liski", role = "aut"), person("Klaus",
"Nordhausen", role = c("aut", "cre"), email =
"klaus.nordhausen@tuwien.ac.at"), person("Hannu", "Oja", role =
"aut"), person("Anne", "Ruiz-Gazen", role = "aut"))
Linear dimension reduction subspaces can be uniquely defined using orthogonal projection matrices. This package provides tools to compute distances between such subspaces and to compute the average subspace. For details see Liski, E.Nordhausen K., Oja H., Ruiz-Gazen A. (2016) Combining Linear Dimension Reduction Subspaces <doi:10.1007/978-81-322-3643-6_7>.
Eero Liski [aut],
Klaus Nordhausen [aut, cre],
Hannu Oja [aut],
Anne Ruiz-Gazen [aut]