Interactive Processing and Segmentation of Forest TLS Point-Cloud Data

Tools for the processing, segmentation, and analysis of terrestrial laser scanning (TLS and MLS) forest point-cloud data. The package provides fast voxel-based processing, classification of point clouds into forest floor, understory, canopy, and woody components, and algorithms for single-tree analysis and structural characterization. Methods are designed to handle large and dense point-cloud datasets efficiently, supporting applications in forest structure assessment, connectivity analysis, and fire-risk evaluation. Input data are provided as '.xyz', '.txt', '.las', or '.laz' point-cloud files. The circle-fitting routines used for diameter estimation are adapted, in base R, from the 'conicfit' package (GPL-3) by Jose Gama, based on the original algorithms and code by Nikolai Chernov. For methodological details, see Ferrara and Arrizza (2025) < https://hdl.handle.net/20.500.14243/533471> and Ferrara et al. (2018) .

Carbon-Related Assessment of Silvicultural Concepts

A simulation model and accompanying functions that support assessing silvicultural concepts on the forest estate level with a focus on the CO2 uptake by wood growth and CO2 emissions by forest operations. For achieving this, a virtual forest estate area is split into the areas covered by typical phases of the silvicultural concept of interest. Given initial area shares of these phases, the dynamics of these areas is simulated. The typical carbon stocks and flows which are known for all phases are attributed post-hoc to the areas and upscaled to the estate level. CO2 emissions by forest operations are estimated based on the amounts and dimensions of the harvested timber. Probabilities of damage events are taken into account.

Incidence Estimation Tools

Tools for estimating incidence from biomarker data in cross- sectional surveys, and for calibrating tests for recent infection. Implements and extends the method of Kassanjee et al. (2012) .

The Maraca Plot: Visualizing Hierarchical Composite Endpoints

Supports visual interpretation of hierarchical composite endpoints (HCEs). HCEs are complex constructs used as primary endpoints in clinical trials, combining outcomes of different types into ordinal endpoints, in which each patient contributes the most clinically important event (one and only one) to the analysis. See Karpefors M et al. (2022) .

Discriminant Non-Negative Matrix Factorization

Discriminant Non-Negative Matrix Factorization aims to extend the Non-negative Matrix Factorization algorithm in order to extract features that enforce not only the spatial locality, but also the separability between classes in a discriminant manner. It refers to three article, Zafeiriou, Stefanos, et al. "Exploiting discriminant information in nonnegative matrix factorization with application to frontal face verification." Neural Networks, IEEE Transactions on 17.3 (2006): 683-695. Kim, Bo-Kyeong, and Soo-Young Lee. "Spectral Feature Extraction Using dNMF for Emotion Recognition in Vowel Sounds." Neural Information Processing. Springer Berlin Heidelberg, 2013. and Lee, Soo-Young, Hyun-Ah Song, and Shun-ichi Amari. "A new discriminant NMF algorithm and its application to the extraction of subtle emotional differences in speech." Cognitive neurodynamics 6.6 (2012): 525-535.

Probabilistic Reconciliation via Conditioning

Provides methods for probabilistic reconciliation of hierarchical forecasts of time series. The available methods include analytical Gaussian reconciliation (Corani et al., 2021) , MCMC reconciliation of count time series (Corani et al., 2024) , Bottom-Up Importance Sampling (Zambon et al., 2024) , methods for the reconciliation of mixed hierarchies (Mix-Cond and TD-cond) (Zambon et al., 2024) < https://proceedings.mlr.press/v244/zambon24a.html>, analytical reconciliation with Bayesian treatment of the covariance matrix (Carrara et al., 2025) .

Bayesian Reconciliation in the 'fable' Framework

Implements the 'bayesRecon' probabilistic reconciliation methods within the 'fable' framework for hierarchical time series forecasting. Bayesian reconciliation (bayesRecon) methods are accessed via the 'reconcile' verb, following 'fable' conventions. For methodological background, see Corani et al. (2021) , Zambon et al. (2024a) , Zambon et al. (2024b) < https://proceedings.mlr.press/v244/zambon24a.html>, and Carrara et al. (2026) .

Multistage Sampling Allocation and Sample Selection

Multivariate optimal allocation for different domains in one and two stages stratified sample design. 'R2BEAT' extends the Neyman (1934) – Tschuprow (1923) allocation method to the case of several variables, adopting a generalization of the Bethel’s proposal (1989). 'R2BEAT' develops this methodology but, moreover, it allows to determine the sample allocation in the multivariate and multi-domains case of estimates for two-stage stratified samples. It also allows to perform both Primary Stage Units and Secondary Stage Units selection. This package requires the availability of 'ReGenesees', that can be installed from < https://github.com/DiegoZardetto/ReGenesees>.

Article Formats for R Markdown

A suite of custom R Markdown formats and templates for authoring journal articles and conference submissions.

k-Nearest Neighbor Mutual Information Estimator

This is a 'C++' mutual information (MI) library based on the k-nearest neighbor (KNN) algorithm. There are three functions provided for computing MI for continuous values, mixed continuous and discrete values, and conditional MI for continuous values. They are based on algorithms by A. Kraskov, et. al. (2004) , BC Ross (2014), and A. Tsimpiris (2012) , respectively.