Composite Indicators Functions

Contains functions to enhance approaches to the Composite Indicators methods, focusing, in particular, on the normalisation and weighting-aggregation steps.


News

Compind v2.0 (Release date: 2018-2-13)

Changes:

New functions:

  • ci_bod_constr_bad: Constrained Benefit of the Doubt approach (BoD) in presence of undesirable indicators
  • ci_rbod_constr_bad: Robust constrained Benefit of the Doubt approach (BoD) in presence of undesirable indicators.
  • ci_rbod_constr_bad_Q: Conditional robust constrained Benefit of the Doubt approach (BoD) in presence of undesirable indicators
  • ci_rbod_spatial: Spatial robust Benefit of the Doubt approach (Sp-RBoD)
  • ci_smaa_constr: Constrained stochastic multi-objective acceptability analysis (C-SMAA)
  • ci_ampi: Adjusted Mazziotta-Pareto Index (AMPI) method

Compind v1.2.1 (Release date: 2017-07-12)

Changes:

  • ci_geom_bod_intertemp: correction due to new R functionalities

Compind v1.2 (Release date: 2017-16-06)

Changes:

New functions:

  • ci_bod_constr: Constrained Benefit of the Doubt approach
  • ci_generalized_mean: Weighting method based on generalized mean
  • ci_geom_bod_intertemp: Intertemporal analysis for geometric mean quantity index numbers
  • ci_geom_gen: generalization of the previous function "ci_mean_geom"

Deleted

  • ci_bod_vrs

Compind v1.1.2 (Release date: 2016-06-27)

Changes:

  • Compind::Compind_vignette has been included.

Compind v1.1 (Release date: 2015-09-07)

Changes:

  • ci_factor function: new method (CH), modified old function
  • ci_bod : Bod weights are avalaible
  • ci_mean_min: Mean-Min Function (MMF)

Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.

install.packages("Compind")

2.0 by Francesco Vidoli, 10 months ago


Browse source code at https://github.com/cran/Compind


Authors: Francesco Vidoli , Elisa Fusco


Documentation:   PDF Manual  


GPL-3 license


Imports Hmisc, MASS, GPArotation, nonparaeff, smaa, np

Depends on Benchmarking, psych, boot, lpSolve, spdep

Suggests R.rsp


See at CRAN