Software and research data

The MUR-FRES Macro interactive dashboard (https://feem-eet-macrodashboard.share.connect.posit.cloud/) enables users to explore labour market, wealth, and social condition indicators across European regions over the period 1980–2024 at three territorial granularities (NUTS-0, NUTS-1, NUTS-2). The dashboard provides a flexible and reproducible tool for monitoring economic and social disparities across European territories, supporting both descriptive analysis and preliminary convergence assessment for research and policy purposes. 

The MUR-FRES Micro interactive dashboard (https://feem-eet-microdashboard.share.connect.posit.cloud/) is an interactive platform designed to explore Italy's intricate economic, social, and energy dynamics. Built entirely upon public datasets, this tool offers a ground-level perspective of the country by featuring data at the municipal level. This data also provides birds-eye overviews at the provincial, regional and national level through seamless aggregation.

ARPALData is an R package (https://cran.r-project.org/web/packages/ARPALData/index.html) which contains functions for retrieving, managing and analysing air quality and weather data from Regione Lombardia open database (<https://www.dati.lombardia.it/>). Data are collected by ARPA Lombardia (Lombardia Environmental Protection Agency), Italy, through its ground monitoring network. See the webpage <https://www.arpalombardia.it/> for further information on ARPA Lombardia's activities and history. Data quality (e.g. missing values, exported values, graphical mapping) has been checked involving members of the ARPA Lombardia's office for air quality control. The package makes available observations since 1989 (for weather) and 1968 (for air quality) and are updated with daily frequency by the regional agency. Full description of the package can be retrieved in the companion paper Maranzano & Algieri (2024), "ARPALData: an R package for retrieving and analyzing air quality and weather data from ARPA Lombardia (Italy)", Environmental and Ecological Statistics, <DOI:10.1007/s10651-024-00599-6>

EEAaq is an R package (https://cran.r-project.org/web/packages/EEAaq/index.html) which downloads and manages air quality data at the European level from the European Environmental Agency (EEA) dataflows (<https://www.eea.europa.eu/data-and-maps/data/aqereporting-9>). The package allows dynamically mapping the stations, summarising and time aggregating the measurements and building spatial interpolation maps. See the webpage <https://www.eea.europa.eu/en> for further information on EEA's activities and history. 

ISTADFuels is a dashboard (https://ale-ch.shinyapps.io/it-fuel-dashboard/) which provides monthly data from 2015 to 2024 on fuel sales organized by fuel type, use, and point of sale (highway, municipal road, extra-network road) at the Italian provincial level (NUTS-3). Fuel data are augmented by a set of socio-economic, environmental, and geographical variables able to explain the impact of economic phenomena and topography on fuel sales.

jumps is an R package (https://cran.r-project.org/web/packages/jumps/index.html) which contains A set of functions to compute the Hodrick-Prescott (HP) filter with automatically selected jumps. The original HP filter extracts a smooth trend from a time series, and our version allows for a small number of automatically identified jumps. Full description of the package can be retrieved in the companion paper Maranzano & Pelagatti (2025) "A Hodrick-Prescott filter with automatically selected jumps", FEEM Working Paper No. 18.2024, Available at SSRN: https://ssrn.com/abstract=4896170 or http://dx.doi.org/10.2139/ssrn.4896170 

SCDA is an R package (https://cran.r-project.org/web/packages/SCDA/index.html) which contains functions for statistical data analysis based on spatially-clustered techniques. The package allows estimating the spatially-clustered spatial regression models (Cerqueti, Maranzano & Mattera, 2025) and spatially-clustered GLMs (Sugasawa & Murakami, 2021) .Also, the library contains functions to estimate spatial clustering based on Adiajacent Matrix K-Means (Zhou, Liu & Zhu, 2019).

Full description of the package can be retrieved in the companion paper Cerqueti, Maranzano & Matterai (2025) "Spatially-Clustered Spatial Autoregressive Models with Application to Agricultural Market Concentration in Europe", Journal of Agricultural, Biological and Environmental Statistics, DOI: 10.1007/s13253-024-00672-4