ldt: Automated Uncertainty Analysis

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: 0.5.2
Depends: R (≥ 3.5.0)
Imports: Rcpp, tdata, Rdpack (≥ 0.7), MASS
LinkingTo: BH, Rcpp
Suggests: knitr, testthat, rmarkdown, kableExtra, moments, systemfit
Published: 2023-12-21
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)
Copyright: see file COPYRIGHTS
URL: https://github.com/rmojab63/LDT
NeedsCompilation: yes
SystemRequirements: C++17
Materials: NEWS
CRAN checks: ldt results


Reference manual: ldt.pdf
Vignettes: Binary Regression (loan default)
SUR (longrun output growth)
VARMA (commodity prices)


Package source: ldt_0.5.2.tar.gz
Windows binaries: r-devel: ldt_0.5.2.zip, r-release: ldt_0.5.2.zip, r-oldrel: ldt_0.5.2.zip
macOS binaries: r-release (arm64): ldt_0.5.2.tgz, r-oldrel (arm64): ldt_0.5.2.tgz, r-release (x86_64): ldt_0.5.2.tgz
Old sources: ldt archive


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