Provides three main categories of functions: construction of composite indicators, imputation and evaluation of missing data, and data normalization.
Gianmarco Borrata <gianmarco.borrata@unina.it> and Pasquale Pipiciello <pasqualepipiciello24@gmail.com>
Key features include:
Construction of composite indicators, such as Mazziotta-Pareto Index, Adjusted Mazziotta-Pareto Index, Geometric aggregation, Linear aggregation, and other functions;
Imputation of missing data through techniques such as Linear Regression Imputation, Hot Deck Imputation, etc;
Evaluation of missing data imputation using metrics such as R^2, RMSE, and MAE;
Several functions to standardize and normalize data, such as Standardization by Adjusted Mazziotta-Pareto method, Normalization by Adjusted Mazziotta-Pareto method, and other functions.
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>
Useful links: