README.md
In Indicator: Composite 'Indicator' Construction and Imputation Data

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(“GianmarcoBorrata/Indicator”)

References

OECD/European Union/EC-JRC (2008), “Handbook on Constructing Composite Indicators: Methodology and User Guide”, OECD Publishing, Paris,
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,
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,