Marco Sandri

Scientific Software

R Packages

MPhS: Molecular Phenology Scale for Grape Berry Development

The MPhS package enables researchers to map their transcriptomic datasets onto the Molecular Phenology Scale (MPhS), a novel framework for standardizing grape berry developmental stages across different cultivars and environmental conditions. This tool facilitates comparative studies by providing a unified molecular timeline for grape berry development, enhancing reproducibility and data integration in viticulture research.

Reference: Tornielli GB, Sandri M, Fasoli M, Amato A, Pezzotti M, Zuccolotto P, Zenoni S (2023) A molecular phenology scale of grape berry development. Horticulture Research, Volume 10, Issue 5:uhad048. doi:10.1093/hr/uhad048

BasketballAnalyzeR

GitHub Repository | CRAN Package

Contains data and code to accompany the book P. Zuccolotto and M. Manisera (2020) Basketball Data Science. Applications with R. CRC Press. ISBN 9781138600799. This package provides comprehensive tools for basketball data analysis, including functions for shot chart visualization, player performance metrics, team statistics, and advanced analytics commonly used in sports science research.

Reference: P. Zuccolotto and M. Manisera (2020) Basketball Data Science. Applications with R. CRC Press. ISBN 9781138600799

GCEmodels

GitHub Repository

Generalized Cross Entropy Linear Regression Models. This package provides implementations of linear regression models based on generalized cross entropy principles, offering robust statistical modeling approaches for complex data analysis scenarios.

NLCUB

GitHub Repository

NLCUB - Estimation of nonlinear CUB models. Nonlinear CUB models have been recently introduced in the literature to model ordinal data taking into account the unequal spacing among response categories.

Documentation

MATLAB Software

Dynamical Systems

2-dimensional Iterated Function Systems (IFS)

ifs.m

Plots the attractor of a generic 2-dimensional (linear) IFS by chaos game.

[X,h] = ifs(A, p, N, opt1)

Examples (ifs.zip)

Self-organized criticality in a model of production and inventory dynamics

bcsw.m

A raw and simple program to numerically simulate the model developed by P. Bak, K. Chen, J. Scheinkman and M. Woodford (1993).

Reference: P. Bak, K. Chen, J. Scheinkman and M. Woodford (1993), "Aggregate Fluctuations from Independent Sectoral Shocks: Self-Organized Criticality in a Model of Production and Inventory Dynamics", Ricerche Economiche, 47 (1), 3-30.

Screenshot

Endogenous preferences and relational dynamics

biavati_sandri_zarri.m

The simulation software of the model presented in the paper by Biavati M., Sandri M., Zarri L. (2002).

Reference: Biavati M., Sandri M., Zarri L. (2002), "Preferenze endogene e dinamiche relazionali: un modello co-evolutivo", in Sacco P.L. e Zamagni S. (a cura di), Complessità relazionale e comportamento economico. Verso un nuovo paradigma di razionalità, Il Mulino, Bologna

Plot of High Dimensional Data

Andrews Diagrams

andrews.m

Plots high-dimensional data using the method proposed by Andrews (1972). Y = ANDREWS(X,N) plots each row of the matrix X as a trigonometric function defined in the interval (-π,π). N is the number of points taken into account in the interval(-π,π). The default value for N is 100. Y is the matrix containing the values f(t) of the trigonometric functions.

Reference: D.F. Andrews (1972), "Plots of High-Dimensional data", Biometrics, pp. 125-136

Example (testandr.m)

Mathematica Packages

Nonlinear dynamical systems

Numerical Calculation of Lyapunov Exponents

The Lyapunov characteristic exponents play a crucial role in the description of the behavior of dynamical systems. They measure the average rate of divergence or convergence of orbits starting from nearby initial points. Therefore, they can be used to analyze the stability of limit sets and to check sensitive dependence on initial conditions, that is, the presence of chaotic attractors.

This package shows how to use Mathematica to compute the Lyapunov spectrum of a smooth dynamical system.

Paper Package (Mathematica < 5.2) Package (Mathematica 7) Package (Mathematica 12) EU Open Research Repository

Time series analysis

MELISSA: a Mathematica package for Singular Spectrum Analysis

melissa.m

Singular spectrum analysis (SSA) is a relatively recent technique for time series analysis. The idea behind SSA was originally purposed as a data adaptive method for choosing an optimal embedding dimension for attractor reconstruction. Later the technique was developed as a "stand alone" time series analysis technique.

During the last decade it has been very successful and has become a standard tool in many different scientific fields, such as climatic, meteorological, geophysical, and astronomical time series analysis.

Tutorial (Italian)