Package: Indicator 0.1.3

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]

Indicator_0.1.3.tar.gz
Indicator_0.1.3.zip(r-4.7)Indicator_0.1.3.zip(r-4.6)Indicator_0.1.3.zip(r-4.5)
Indicator_0.1.3.tgz(r-4.6-any)Indicator_0.1.3.tgz(r-4.5-any)
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Indicator_0.1.3.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
Indicator/json (API)

# Install 'Indicator' in R:

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

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

Datasets:

Education - Education

On CRAN:

2.70 score 1 stars 3 scripts 239 downloads 21 exports 122 dependencies

Last updated from:bf5e544827. Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel-x86_64	OK	180
source / vignettes	OK	210
linux-release-x86_64	OK	156
macos-release-arm64	OK	238
macos-oldrel-arm64	OK	166
windows-devel	OK	120
windows-release	OK	211
windows-oldrel	OK	255
wasm-release	OK	160

Exports:columns_with_nan compute_CI geometric_aggregation get_all_performance get_all_performance_boot Jevons_aggregation linear_aggregation linear_aggregation_AMPI linear_aggregation_MPI lm_imputation MAD min_max min_max_GM normalization_abov_below_mean pca_weighting performance_nan_imputation rank_aggregation rank_normalisation standardization Standardization_AMPI standardization_MPI

Dependencies:abind backports base64enc boot broom bslib cachem car carData cli cluster colorspace commonmark cowplot cpp11 crosstalk curl Deriv digest doBy dplyr DT ellipse emmeans estimability evaluate FactoMineR farver fastmap flashClust fontawesome forecast Formula fracdiff fs generics ggplot2 ggrepel ggtext glue gridtext gtable highr htmltools htmlwidgets irlba isoband jpeg jquerylib jsonlite knitr labeling later lattice lazyeval leaps lifecycle litedown lme4 lmtest magrittr markdown MASS Matrix MatrixModels memoise mgcv mime minqa missMethods modelr multcompView mvtnorm nlme nloptr nnet norm numDeriv otel pbkrtest pillar pkgconfig png promises purrr quantreg R6 rappdirs rbibutils RColorBrewer Rcpp RcppArmadillo RcppEigen Rdpack reformulas rlang rmarkdown S7 sass scales scatterplot3d showtext showtextdb SparseM stringi stringr survival sysfonts tibble tidyr tidyselect timeDate tinytex urca utf8 vctrs viridisLite withr xfun xml2 yaml zoo

Help page	Topics
Indicator: A package for constructing composite indicators, imputing and evaluating missing data imputation	Indicator-package Indicator
Function to get the names of the columns with NAN values	columns_with_nan
Calculation of Condition Indices	compute_CI
Education	Education
Geometric Aggregation	geometric_aggregation
Function to evaluate different nan imputation methods	get_all_performance
Function to evaluate different nan imputation methods with bootstrap	get_all_performance_boot
Jevons static aggregation	Jevons_aggregation
Linear Aggregation	linear_aggregation
Adjusted Mazziotta-Pareto index	linear_aggregation_AMPI
Mazziotta-Pareto index	linear_aggregation_MPI
Function to apply nan inputation with linear regression	lm_imputation
Mean absolute difference of rank	MAD
Min-max normalization	min_max
Normalization for the Geometric Mean	min_max_GM
Normalization above or below the mean	normalization_abov_below_mean
Function that weight the quantitative variable by PCA method	pca_weighting
Function to evaluate nan imputation method's performance	performance_nan_imputation
Ranking Aggregation	rank_aggregation
Rank normalization	rank_normalisation
Standardization	standardization
Standardization of data with Adjusted Maziotta-Pareto index	Standardization_AMPI
Standardization of data with Maziotta-Pareto index	standardization_MPI

Package: Indicator 0.1.3

Indicator: Composite 'Indicator' Construction and Imputation Data

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