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DJL: Distance Measure Based Judgment and Learning

Implements various decision support tools related to the Econometrics & Technometrics. Subroutines include correlation reliability test, Mahalanobis distance measure for outlier detection, combinatorial search (all possible subset regression), non-parametric efficiency analysis measures: DDF (directional distance function), DEA (data envelopment analysis), HDF (hyperbolic distance function), SBM (slack-based measure), and SF (shortage function), benchmarking, Malmquist productivity analysis, risk analysis, technology adoption model, new product target setting, network DEA, dynamic DEA, intertemporal budgeting, etc.

Version: 3.9
Depends: R (≥ 3.4.0), car, lpSolveAPI
Published: 2023-03-16
DOI: 10.32614/CRAN.package.DJL
Author: Dong-Joon Lim, Ph.D. <technometrics.org>
Maintainer: Dong-Joon Lim <tgno3.com at gmail.com>
License: GPL-2
NeedsCompilation: no
Materials: NEWS
CRAN checks: DJL results

Documentation:

Reference manual: DJL.pdf

Downloads:

Package source: DJL_3.9.tar.gz
Windows binaries: r-devel: DJL_3.9.zip, r-release: DJL_3.9.zip, r-oldrel: DJL_3.9.zip
macOS binaries: r-release (arm64): DJL_3.9.tgz, r-oldrel (arm64): DJL_3.9.tgz, r-release (x86_64): DJL_3.9.tgz, r-oldrel (x86_64): DJL_3.9.tgz
Old sources: DJL archive

Linking:

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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.