Methods and tools for model selection and multi-model inference (Burnham and Anderson (2002) <doi:10.1007/b97636>, among others). 'SUR' (for parameter estimation), 'logit'/'probit' (for binary classification), and 'VARMA' (for time-series forecasting) are implemented. Evaluations are both in-sample and out-of-sample. It is designed to be efficient in terms of CPU usage and memory consumption.
Version: 0.5.3 Depends: R (≥ 3.5.0) Imports: Rcpp, tdata, Rdpack (≥ 0.7), MASS LinkingTo: BH, Rcpp Suggests: knitr, testthat, rmarkdown, kableExtra, moments, systemfit Published: 2024-07-06 DOI: 10.32614/CRAN.package.ldt Author: Ramin Mojab [aut, cre], 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) Maintainer: Ramin Mojab <rmojab63 at gmail.com> License: GPL (≥ 3) URL: https://github.com/rmojab63/LDT NeedsCompilation: yes SystemRequirements: C++17 Materials: NEWS CRAN checks: ldt results Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=ldt to link to this page.
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