Community Detection by Scaled Null-Adjusted Persistence

Finds the vertex partition of an undirected graph that maximises a persistence-based objective, using the Milano local-search algorithm. Three related quality measures are supported: the persistence probability of a community, its null-adjusted persistence (NAP), and the scaled null-adjusted persistence (Scaled-NAP), which interpolates between NAP and modularity. The methods are described in Avellone et al. (2023) and Avellone et al. (2025) .

Optimal Graph Partition using the Persistence

Calculate the optimal vertex partition of a graph using the persistence as objective function. These subroutines have been used in Avellone et al. and Avellone et al. . This package is deprecated and has been superseded by the 'scalednap' package, which provides the same functionality and additional features; new and existing users should install 'scalednap' instead.

The GiViTI Calibration Test and Belt

Functions to assess the calibration of logistic regression models with the GiViTI (Gruppo Italiano per la Valutazione degli interventi in Terapia Intensiva, Italian Group for the Evaluation of the Interventions in Intensive Care Units - see < http://www.giviti.marionegri.it/>) approach. The approach consists in a graphical tool, namely the GiViTI calibration belt, and in the associated statistical test. These tools can be used both to evaluate the internal calibration (i.e. the goodness of fit) and to assess the validity of an externally developed model.

Intervention in Prediction Measure for Random Forests

Computes intervention in prediction measure for assessing variable importance for random forests. See details at I. Epifanio (2017) .

Environmental Noise Pollution Data Analysis

Provides analyse, interpret and understand noise pollution data. Data are typically regular time series measured with sound meter. The package is partially described in Fogola, Grasso, Masera and Scordino (2023, ).

Interactively Gate Points

Interactively gate points on a scatter plot. Interactively drawn gates are recorded and can be applied programmatically to reproduce results exactly. Programmatic gating is based on the package gatepoints by Wajid Jawaid.

Cointegrated ICU Forecasting

Set of forecasting tools to predict ICU beds using a Vector Error Correction model with a single cointegrating vector. Method described in Berta, P. Lovaglio, P.G. Paruolo, P. Verzillo, S., 2020. "Real Time Forecasting of Covid-19 Intensive Care Units demand" Health, Econometrics and Data Group (HEDG) Working Papers 20/16, HEDG, Department of Economics, University of York, < https://www.york.ac.uk/media/economics/documents/hedg/workingpapers/2020/2016.pdf>.

Constrained Mixture of Generalized Normal Distributions

The 'cmgnd' implements the constrained mixture of generalized normal distributions model, a flexible statistical framework for modelling univariate data exhibiting non-normal features such as skewness, multi-modality, and heavy tails. By imposing constraints on model parameters, the 'cmgnd' reduces estimation complexity while maintaining high descriptive power, offering an efficient solution in the presence of distributional irregularities. For more details see Duttilo and Gattone (2025) and Duttilo et al (2025) .

Cancer RADAR Project Tool

Cancer RADAR is a project which aim is to develop an infrastructure that allows quantifying the risk of cancer by migration background across Europe. This package contains a set of functions cancer registries partners should use to reshape 5 year-age group cancer incidence data into a set of summary statistics (see Boyle & Parkin (1991, ISBN:978-92-832-1195-2)) in lines with Cancer RADAR data protections rules.

Bootstrapping the ARDL Tests for Cointegration

The bootstrap ARDL tests for cointegration is the main functionality of this package. It also acts as a wrapper of the most commond ARDL testing procedures for cointegration: the bound tests of Pesaran, Shin and Smith (PSS; 2001 - ) and the asymptotic test on the independent variables of Sam, McNown and Goh (SMG: 2019 - ). Bootstrap and bound tests are performed under both the conditional and unconditional ARDL models.