Methods and tools for creating a model set and estimating and evaluating the explanation or prediction power of its members. 'SUR' modelling (for parameter estimation), 'logit'/'probit' modelling (for binary classification), and 'VARMA' modelling (for time-series forecasting) are implemented. Evaluations are both in-sample and out-of-sample. It can be used for stepwise regression analysis <https://en.wikipedia.org/wiki/Stepwise_regression>, automatic model selection and model averaging (Claeskens and Hjort (2008, ISBN:1139471805, 9781139471800)), calculating benchmarks, and doing sensitivity analysis (Leamer (1983) <https://www.jstor.org/stable/1803924> proposal).

Version: | 0.2.0.0 |

Depends: | R (≥ 3.5.0) |

Imports: | Rcpp, readxl, jsonlite |

LinkingTo: | BH, Rcpp |

Suggests: | knitr, testthat, rmarkdown, kableExtra, MASS |

Published: | 2023-03-01 |

Author: | Ramin Mojab [aut, cre], Michael Hutt [cph] (MIT license. Original code for Nelder-Mead algorithm.), Stephen Becker [cph] (BSD 3-clause license. Original code for L-BFGS-B algorithm. The L-BFGS-B algorithm was written in the 1990s (mainly 1994, some revisions 1996) by Ciyou Zhu (in collaboration with R.H. Byrd, P. Lu-Chen and J. Nocedal). L-BFGS-B Version 3.0 is an algorithmic update from 2011, with coding changes by J. L. Morales), Math.NET [cph] (MIT license. Code from the 'Math.NET Numerics' library is used in calculating running statistics.) |

Maintainer: | Ramin Mojab <rmojab63 at gmail.com> |

License: | GPL (≥ 3) |

Copyright: | see file COPYRIGHTS |

URL: | https://github.com/rmojab63/LDT |

NeedsCompilation: | yes |

SystemRequirements: | C++17 |

CRAN checks: | ldt results |

Package source: | ldt_0.2.0.0.tar.gz |

Windows binaries: | r-devel: ldt_0.2.0.0.zip, r-release: ldt_0.2.0.0.zip, r-oldrel: ldt_0.2.0.0.zip |

macOS binaries: | r-release (arm64): ldt_0.2.0.0.tgz, r-oldrel (arm64): ldt_0.2.0.0.tgz, r-release (x86_64): ldt_0.2.0.0.tgz, r-oldrel (x86_64): ldt_0.2.0.0.tgz |

Old sources: | ldt archive |

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