Fuzzy set theory has become increasingly popular for deriving uni- and multi-dimensional poverty estimates. In recent years, various authors have proposed different approaches to defining membership functions, resulting in the development of various fuzzy poverty indices. This paper introduces a new R package called FuzzyPovertyR, designed for estimating fuzzy poverty indices. The package is demonstrated by using it to estimate three fuzzy poverty indices – one multi- and two uni-dimensional – at the regional level (NUTS 2) in Italy. The package allows users to select from a range of membership functions and includes tools for estimating the variance of these indices by the ad-hoc Jack-Knife repeated replication procedure or by naive and calibrated non-parametric bootstrap methods.
Crescenzi, F., Mori, L., Betti, G., Gagliardi, F., D'Agostino, A., Neri, L. (2025). An R tool for computing and evaluating Fuzzy poverty indices: The package FuzzyPovertyR. JOURNAL OF APPLIED STATISTICS, 52(15), 2958-2971 [10.1080/02664763.2025.2481461].
An R tool for computing and evaluating Fuzzy poverty indices: The package FuzzyPovertyR
Mori, L.
;
2025
Abstract
Fuzzy set theory has become increasingly popular for deriving uni- and multi-dimensional poverty estimates. In recent years, various authors have proposed different approaches to defining membership functions, resulting in the development of various fuzzy poverty indices. This paper introduces a new R package called FuzzyPovertyR, designed for estimating fuzzy poverty indices. The package is demonstrated by using it to estimate three fuzzy poverty indices – one multi- and two uni-dimensional – at the regional level (NUTS 2) in Italy. The package allows users to select from a range of membership functions and includes tools for estimating the variance of these indices by the ad-hoc Jack-Knife repeated replication procedure or by naive and calibrated non-parametric bootstrap methods.| File | Dimensione | Formato | |
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An R tool.pdf
Open Access dal 25/03/2026
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Postprint / Author's Accepted Manuscript (AAM) - versione accettata per la pubblicazione dopo la peer-review
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Licenza per Accesso Aperto. Creative Commons Attribuzione - Non commerciale - Non opere derivate (CCBYNCND)
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5.13 MB
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