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Description

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>.

Indicator Package

The Indicator package is a versatile tool designed for constructing composite indicators, imputing missing data, evaluating imputation results, and normalizing data. It offers a range of functions to streamline the process of handling complex datasets, making it an essential resource for researchers, analysts, and data scientists.

Key Features

  • Composite Indicator Construction: Implement various composite indicators such as the Mazziotta-Pareto Index, Adjusted Mazziotta-Pareto Index, Geometric aggregation, Linear aggregation, and more.
  • Missing Data Imputation: Utilize techniques like Linear Regression Imputation, Hot Deck Imputation, etc., to fill in missing values effectively.
  • Evaluation Metrics: Assess the quality of missing data imputation using metrics like R^2, RMSE, and MAE for informed decision-making.
  • Data Normalization: Standardize and normalize data using methods like Standardization by Adjusted Mazziotta-Pareto method, Normalization by Adjusted Mazziotta-Pareto method, and others.

Installation

You can install the Indicator package from CRAN using: https://CRAN.R-project.org/package=Indicator

install.packages(“devtools”)

devtools::install_github(“username/Indicator”)

References

  • 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”, Social Indicators Research, Springer, vol. 136(3), pages 967-976, April, DOI:10.1007/s11205-017-1577-5
  • De Muro P., Mazziotta M., Pareto A. (2011), “Composite Indices of Development and Poverty: An Application to MDGs”, Social Indicators Research, Volume 104, Number 1, pp. 1-18, DOI:10.1007/s11205-010-9727-z.
Metadata

Version

0.1.2

License

Unknown

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