Package: Indicator 0.1.2

Gianmarco Borrata

Indicator: Composite 'Indicator' Construction and Imputation Data

Different functions includes constructing composite indicators, imputing missing data, and evaluating imputation techniques. Additionally, different tools for data normalization. Detailed methodologies of 'Indicator' package are: OECD/European Union/EC-JRC (2008), "Handbook on Constructing Composite Indicators: Methodology and User Guide", OECD Publishing, Paris, <doi:10.1787/533411815016>, Matteo Mazziotta & Adriano Pareto, (2018) "Measuring Well-Being Over Time: The Adjusted Mazziotta–Pareto Index Versus Other Non-compensatory Indices" <doi:10.1007/s11205-017-1577-5> and De Muro P., Mazziotta M., Pareto A. (2011), "Composite Indices of Development and Poverty: An Application to MDGs" <doi:10.1007/s11205-010-9727-z>.

Authors:Gianmarco Borrata [aut, cre], Pasquale Pipiciello [aut]

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Indicator.pdf |Indicator.html✨
Indicator/json (API)
NEWS

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

Peer review:

Bug tracker:https://github.com/gianmarcoborrata/indicator/issues

Datasets:

On CRAN:

2.70 score 1 stars 486 downloads 21 exports 101 dependencies

Last updated 5 months agofrom:bf49216372. Checks:OK: 3 NOTE: 4. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 10 2024
R-4.5-winOKNov 10 2024
R-4.5-linuxOKNov 10 2024
R-4.4-winNOTENov 10 2024
R-4.4-macNOTENov 10 2024
R-4.3-winNOTENov 10 2024
R-4.3-macNOTENov 10 2024

Exports:columns_with_nancompute_CIgeometric_aggregationget_all_performanceget_all_performance_bootJevons_aggregationlinear_aggregationlinear_aggregation_AMPIlinear_aggregation_MPIlm_imputationMADmin_maxmin_max_GMnormalization_abov_below_meanpca_weightingperformance_nan_imputationrank_aggregationrank_normalisationstandardizationStandardization_AMPIstandardization_MPI

Dependencies:abindbackportsbase64encbootbroombslibcachemcarcarDatacliclustercolorspacecowplotcpp11crosstalkDerivdigestdoBydplyrDTellipseemmeansestimabilityevaluateFactoMineRfansifarverfastmapflashClustfontawesomeFormulafsgenericsggplot2ggrepelgluegtablehighrhtmltoolshtmlwidgetshttpuvisobandjquerylibjsonliteknitrlabelinglaterlatticelazyevalleapslifecyclelme4magrittrMASSMatrixMatrixModelsmemoisemgcvmicrobenchmarkmimeminqamissMethodsmodelrmultcompViewmunsellmvtnormnlmenloptrnnetnormnumDerivpbkrtestpillarpkgconfigpromisespurrrquantregR6rappdirsRColorBrewerRcppRcppEigenrlangrmarkdownsassscalesscatterplot3dSparseMstringistringrsurvivaltibbletidyrtidyselecttinytexutf8vctrsviridisLitewithrxfunyaml