Author: Guang Yang <gyang.rutgers@gmail.com>, Jerry Q. Cheng <jcheng18@nyit.edu>,
Maintainer: Jerry Q. Cheng <jcheng18@nyit.edu>
Packaged: 2021-03-07 04:24:07 UTC; Yingying Chen

Title: Inference and Clustering of Functional Data
Authors@R: c( person("Andrea", "Martino", role = c("aut", "cre"),
person("Andrea", "Ghiglietti", role = "aut",
person("Francesca", "Ieva", role = "aut",
person("Anna Maria", "Paganoni", role = "aut",
Description: Some methods for the inference and clustering of univariate and
multivariate functional data, using a generalization of Mahalanobis
distance, along with some functions useful for the analysis of functional data.
For further details, see Martino A., Ghiglietti, A., Ieva, F. and Paganoni A. M. (2017) <arXiv:1708.00386>.

Title: Gaussian Mixture Graphical Model Learning and Inference
person("Jérémy", "Roos", email = "jeremy.roos@gmail.com",
person("RATP Group", role = c("fnd", "cph")))
Description: Gaussian mixture graphical models include Bayesian networks and
dynamic Bayesian networks (their temporal extension) whose local probability
distributions are described by Gaussian mixture models. They are powerful
tools for graphically and quantitatively representing nonlinear dependencies
between continuous variables. This package provides a complete framework to
create, manipulate, learn the structure and the parameters, and perform
inference in these models. Most of the algorithms are described in the PhD
thesis of Roos (2018) <https://tel.archives-ouvertes.fr/tel-01943718>.

Description: It estimates the parameters based on moment conditions using the Generalized method of moments (GMM) of Hansen(1982)
Packaged: Fri Oct 17 21:17:21 2008; pierre

Title: Generalized Method of Moments and Generalized Empirical Likelihood
Description: It is a complete suite to estimate models based on moment conditions. It includes the  two step Generalized method of moments (GMM) of Hansen(1982), the iterated GMM and continuous updated estimator (CUE) of Hansen-Eaton-Yaron(1996) and several methods that belong to the Generalized Empirical Likelihood (GEL) family of estimators, as presented by Smith(1997), Kitamura(1997), Newey-Smith(2004) and Anatolyev(2005).
Depends: R (>= 2.0.0), sandwich, tseries
Packaged: Mon Nov 24 01:45:33 2008; pierre