Packaged: Fri Oct  5 07:51:56 2007; kuhna03

Title: Classification and Regression Training in Parallel Using NetworkSpaces
Description: Augment some caret functions using parallel processing
Packaged: Tue Oct  9 16:29:57 2007; kuhna03

Packaged: Thu Jan 10 11:54:02 2008; kuhna03

Packaged: Mon Jan 14 13:51:41 2008; kuhna03

Packaged: Tue Jun 10 15:52:28 2008; kuhna03

Packaged: Fri Sep  5 04:51:34 2008; theussl

Title: Continuous-Time Fractionally Integrated ARMA Process for
Irregularly Spaced Long-Memory Time Series Data
Description: We provide a toolbox to fit a continuous-time fractionally integrated ARMA process (CARFIMA) on univariate and irregularly spaced time series data via frequentist or Bayesian machinery. A general-order CARFIMA(p, H, q) model for p>q is specified in Tsai and Chan (2005)<doi:10.1111/j.1467-9868.2005.00522.x> and it involves p+q+2 unknown model parameters, i.e., p AR parameters, q MA parameters, Hurst parameter H, and process uncertainty (standard deviation) sigma. The package produces their maximum likelihood estimates and asymptotic uncertainties using a global optimizer called the differential evolution algorithm. It also produces their posterior distributions via Metropolis within a Gibbs sampler equipped with adaptive Markov chain Monte Carlo for posterior sampling. These fitting procedures, however, may produce numerical errors if p>2. The toolbox also contains a function to simulate discrete time series data from CARFIMA(p, H, q) process given the model parameters and observation times.
Packaged: 2017-10-21 16:47:01 UTC; hyungsuktak

person("Henghsiu", "Tsai", role = "aut", email="henghsiu.tsai@gmail.com"),
person("Kisung", "You", role = c("aut", "cre"),email = "kyou@nd.edu",comment=c(ORCID="0000-0002-8584-459X")))
Description: We provide a toolbox to fit a continuous-time fractionally integrated ARMA process (CARFIMA) on univariate and irregularly spaced time series data via frequentist or Bayesian machinery. A general-order CARFIMA(p, H, q) model for p>q is specified in Tsai and Chan (2005) <doi:10.1111/j.1467-9868.2005.00522.x> and it involves (p+q+2) unknown model parameters, i.e., p AR parameters, q MA parameters, Hurst parameter H, and process uncertainty (standard deviation) sigma. The package produces their maximum likelihood estimates and asymptotic uncertainties using a global optimizer called the differential evolution algorithm. It also produces their posterior distributions via Metropolis within a Gibbs sampler equipped with adaptive Markov chain Monte Carlo for posterior sampling. These fitting procedures, however, may produce numerical errors if p>2. The toolbox also contains a function to simulate discrete time series data from CARFIMA(p, H, q) process given the model parameters and observation times.
Imports: Rcpp, DEoptim, Rdpack, MASS, cubature, numDeriv, stats, utils
Author: Hyungsuk Tak [aut] (<https://orcid.org/0000-0003-0334-8742>),