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  "Package": "carfima",
  "Version": "2.0.2",
  "Date": "2020-03-20",
  "Title": "Continuous-Time Fractionally Integrated ARMA Process for\nIrregularly Spaced Long-Memory Time Series Data",
  "Author": "Hyungsuk Tak, Henghsiu Tsai, and Kisung You",
  "Maintainer": "Hyungsuk Tak <hyungsuk.tak@gmail.com>",
  "Description": "We provide a toolbox to fit a continuous-time fractionally\nintegrated ARMA process (CARFIMA) on univariate and irregularly\nspaced time series data via both frequentist and Bayesian\nmachinery. A general-order CARFIMA(p, H, q) model for p>q is\nspecified in Tsai and Chan (2005)\n<doi:10.1111/j.1467-9868.2005.00522.x> and it involves p+q+2\nunknown model parameters, i.e., p AR parameters, q MA\nparameters, Hurst parameter H, and process uncertainty\n(standard deviation) sigma. Also, the model can account for\nheteroscedastic measurement errors, if the information about\nmeasurement error standard deviations is known. The package\nproduces their maximum likelihood estimates and asymptotic\nuncertainties using a global optimizer called the differential\nevolution algorithm. It also produces posterior samples of the\nmodel parameters via Metropolis-Hastings within a Gibbs sampler\nequipped with adaptive Markov chain Monte Carlo. These fitting\nprocedures, however, may produce numerical errors if p>2. The\ntoolbox also contains a function to simulate discrete time\nseries data from CARFIMA(p, H, q) process given the model\nparameters and observation times.",
  "License": "GPL-2",
  "NeedsCompilation": "no",
  "Packaged": {
    "Date": "2026-05-31 07:48:23 UTC",
    "User": "root"
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  "Repository": "https://hyungsuktak.r-universe.dev",
  "Date/Publication": "2020-03-21 16:50:02 UTC",
  "RemoteUrl": "https://github.com/cran/carfima",
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      "title": "Continuous-Time Fractionally Integrated ARMA Process for Irregularly Spaced Long-Memory Time Series Data",
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