Package: Compind 3.1

Compind: Composite Indicators Functions

A collection of functions to calculate Composite Indicators methods, focusing, in particular, on the normalisation and weighting-aggregation steps, as described in OECD Handbook on constructing composite indicators: methodology and user guide <https://www.oecd-ilibrary.org/economics/handbook-on-constructing-composite-indicators-methodology-and-user-guide_9789264043466-en>, 'Vidoli' and 'Fusco' and 'Mazziotta' <doi:10.1007/s11205-014-0710-y>, 'Mazziotta' and 'Pareto' (2016) <doi:10.1007/s11205-015-0998-2>, 'Van Puyenbroeck and 'Rogge' <doi:10.1016/j.ejor.2016.07.038> and other authors.

Authors:Francesco Vidoli, Elisa Fusco

Compind_3.1.tar.gz
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Compind_3.1.tgz(r-4.4-any)Compind_3.1.tgz(r-4.3-any)
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Compind.pdf |Compind.html
Compind/json (API)
NEWS

# Install 'Compind' in R:
install.packages('Compind', repos = c('https://fvidoli.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Datasets:
  • BLI_2017 - Better Life Index 2017 indicators
  • EU_2020 - Europe 2020 indicators
  • EU_NUTS1 - EU NUTS1 Transportation data
  • data_HPI - Happy Planet Index 2017-2019 indicators

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

3.08 score 40 scripts 482 downloads 26 exports 159 dependencies

Last updated 8 months agofrom:952c099ff0. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 21 2024
R-4.5-winOKNov 21 2024
R-4.5-linuxOKNov 21 2024
R-4.4-winOKNov 21 2024
R-4.4-macOKNov 21 2024
R-4.3-winOKNov 21 2024
R-4.3-macOKNov 21 2024

Exports:bandwidth_CIci_ampici_bodci_bod_constrci_bod_constr_badci_bod_dirci_bod_mdirci_bod_var_wci_factorci_factor_mixedci_generalized_meanci_geom_bod_intertempci_geom_genci_mean_minci_mpici_ogwaci_owaci_rbodci_rbod_constr_badci_rbod_constr_bad_Qci_rbod_dirci_rbod_mdirci_rbod_spatialci_smaa_constrci_wroclawnormalise_ci

Dependencies:abindbackportsbase64encBenchmarkingbootbroombslibcachemcarcarDatacheckmateclassclassIntcliclustercodacodetoolscolorspacecowplotcpp11crosstalkcubaturedata.tableDBIdeldirDEoptimRDerivdigestdoBydplyrDTe1071ellipseemmeansestimabilityevaluateexpmFactoMineRfansifarverfastmapflashClustFNNfontawesomeforeignFormulafsgenericsgeometryggplot2ggrepelglueGPArotationgridExtragtableGWmodelhighrHmischtmlTablehtmltoolshtmlwidgetshttpuvintervalsisobandjquerylibjsonliteKernSmoothknitrlabelinglaterlatticelazyevalleapsLearnBayeslifecyclelinproglme4lpSolvelpSolveAPImagicmagrittrMASSMatrixMatrixModelsmcmcMCMCpackmemoisemgcvmicrobenchmarkmimeminqamnormtmodelrmultcompmultcompViewmunsellmvtnormnlmenloptrnnetnonparaeffnpnumDerivpbkrtestpillarpkgconfigpopdemopromisesproxypsychpurrrquadprogquantregR6rappdirsRColorBrewerRcompadreRcppRcppArmadilloRcppEigenRcppProgressrlangrmarkdownrobustbaserpartrstudioapis2sandwichsassscalesscatterplot3dsfsmaaspspacetimeSparseMspatialregspDataspdepstringistringrsurvivalTH.datatibbletidyrtidyselecttinytexucminfunitsutf8vctrsviridisviridisLitewithrwkxfunxtsyamlzoo

Compind: Composite indicators functions based on frontiers in R

Rendered fromCompind_vignette.pdf.asisusingR.rsp::asison Nov 21 2024.

Last update: 2017-06-20
Started: 2016-06-27

Readme and manuals

Help Manual

Help pageTopics
Composite Indicators - CompindCompind-package
Multivariate mixed bandwidth selection for exogenous variablesbandwidth_CI
Better Life Index 2017 indicatorsBLI_2017
Adjusted Mazziotta-Pareto Index (AMPI) methodci_ampi
Benefit of the Doubt approach (BoD)ci_bod
Constrained Benefit of the Doubt approach (BoD)ci_bod_constr
Constrained Benefit of the Doubt approach (BoD) in presence of undesirable indicatorsci_bod_constr_bad
Directional Benefit of the Doubt (D-BoD) modelci_bod_dir
Multi-directional Benefit of the Doubt approach (MDBoD)ci_bod_mdir
Variance weighted Benefit of the Doubt approach (BoD variance weighted)ci_bod_var_w
Weighting method based on Factor Analysisci_factor
Weighting method based on Factor analysis of mixed data (FAMD)ci_factor_mixed
Weighting method based on generalized meanci_generalized_mean
Intertemporal analysis for geometric mean quantity index numbersci_geom_bod_intertemp
Generalized geometric mean quantity index numbersci_geom_gen
Mean-Min Functionci_mean_min
Mazziotta-Pareto Index (MPI) methodci_mpi
Ordered Geographically Weighted Average (OWA)ci_ogwa
Ordered Weighted Average (OWA)ci_owa
Robust Benefit of the Doubt approach (RBoD)ci_rbod
Robust constrained Benefit of the Doubt approach (BoD) in presence of undesirable indicatorsci_rbod_constr_bad
Conditional robust constrained Benefit of the Doubt approach (BoD) in presence of undesirable indicatorsci_rbod_constr_bad_Q
Directional Robust Benefit of the Doubt approach (D-RBoD)ci_rbod_dir
Robust multi-directional Benefit of the Doubt approach (MDRBoD)ci_rbod_mdir
Spatial robust Benefit of the Doubt approach (Sp-RBoD)ci_rbod_spatial
Constrained stochastic multi-objective acceptability analysis (C-SMAA)ci_smaa_constr
Wroclaw Taxonomic Methodci_wroclaw
Happy Planet Index 2017-2019 indicatorsdata_HPI
Europe 2020 indicatorsEU_2020
EU NUTS1 Transportation dataEU_NUTS1
Normalisation and polarity functionsnormalise_ci