Multi-Layer Decomposition of Functional Community Restructuring

Implements the 'ASC-CFD' (Assemblage Shift Characterization - Community Functional Dynamics) framework for decomposing functional community restructuring into positional (centroid displacement), dispersive (functional dispersion), and boundary (convex hull volume) components. Tests each component against two null models: a sampling null model (individuals or replicates reassigned between communities) and a stratified trait-permutation null model, with a single global test when many communities are compared. Functional entities (species with similar traits) help detect replacement among similar species, and species-level leverage identifies the taxa driving functional shifts. Supports both temporal (paired) and spatial (pairwise) comparisons.

Masked User Input

A micro-package for reading "passwords", i.e. reading user input with masking, so that the input is not displayed as it is typed. Currently we have support for 'RStudio', the command line (every OS), and any platform where 'tcltk' is present.

Infer Cell-Cell Communication from Spatial Transcriptomics

Identifies cell-cell communication hotspots in spatial transcriptomics data using bivariate Local Moran's I statistics on hexagonally binned cells. Provides functions for spatial weighting, ligand-receptor pair filtering, hotspot detection, and visualisation of sender-receiver cell-type interactions.

Social Representation Analysis via Semantic Network Mapping

Implements the ThemeScope framework for detecting and visualising social representations in large-scale digital text corpora. From raw documents it builds, via 'udpipe' annotation, sentence-level word co-occurrence networks and derives two community-level indicators grounded in Social Representation Theory: the Prototypical Salience Index (PSI) for anchoring and the Concreteness Score (CS) for objectification. Communities are located in a two-dimensional, theoretically grounded representational map. The whole pipeline is usable from the R console; an optional 'shiny' graphical interface calls the same exported functions. The method is described in Misuraca, Spano and D'Aniello (2026) .

Site Occupancy Modeling for Environmental DNA Metabarcoding

Fits community site occupancy models to environmental DNA metabarcoding data collected using spatially-replicated survey design. Model fitting results can be used to evaluate and compare the effectiveness of species detection to find an efficient survey design. Reference: Fukaya et al. (2022) , Fukaya and Hasebe (2025) .

Threshold Indicator Taxa Analysis

Uses indicator species scores across binary partitions of a sample set to detect congruence in taxon-specific changes of abundance and occurrence frequency along an environmental gradient as evidence of an ecological community threshold. Relevant references include Baker and King (2010) , King and Baker (2010) , and Baker and King (2013) .

Mechanistic Metacommunity Simulator

A forward simulator for generating synthetic metacommunity data. As an in silico experimental platform, it enables researchers to simulate community shifts, test theoretical frameworks, and benchmark analytical algorithms prior to empirical application. Key capabilities include mechanistic simulations driven by demography, dispersal, and interactions, GIS interoperability via the 'terra' package for dynamic environments, and a virtual ecologist module that simulates imperfect detection and survey errors to mimic real-world biodiversity data.

Camera Trap Data Management and Analysis Framework

Management and analysis of camera trap wildlife data through an integrated workflow. Provides functions for image/video organization and metadata extraction, species/individual identification. Creates detection histories for occupancy and spatial capture-recapture analyses, with support for multi-season studies. Includes tools for fitting community occupancy models in JAGS and NIMBLE, and an interactive dashboard for survey data visualization and analysis. Features visualization of species distributions and activity patterns, plus export capabilities for GIS and reports. Emphasizes automation and reproducibility while maintaining flexibility for different study designs.

Differential Analysis of Intercellular Communication from scRNA-Seq Data

Analysis tools to investigate changes in intercellular communication from scRNA-seq data. Using a Seurat object as input, the package infers which cell-cell interactions are present in the dataset and how these interactions change between two conditions of interest (e.g. young vs old). It relies on an internal database of ligand-receptor interactions (available for human, mouse and rat) that have been gathered from several published studies. Detection and differential analyses rely on permutation tests. The package also contains several tools to perform over-representation analysis and visualize the results. See Lagger, C. et al. (2023) for a full description of the methodology.

Bayesian Context Trees for Discrete Time Series

An implementation of a collection of tools for exact Bayesian inference with discrete times series. This package contains functions that can be used for prediction, model selection, estimation, segmentation/change-point detection and other statistical tasks. Specifically, the functions provided can be used for the exact computation of the prior predictive likelihood of the data, for the identification of the a posteriori most likely (MAP) variable-memory Markov models, for calculating the exact posterior probabilities and the AIC and BIC scores of these models, for prediction with respect to log-loss and 0-1 loss and segmentation/change-point detection. Example data sets from finance, genetics, animal communication and meteorology are also provided. Detailed descriptions of the underlying theory and algorithms can be found in [Kontoyiannis et al. 'Bayesian Context Trees: Modelling and exact inference for discrete time series.' Journal of the Royal Statistical Society: Series B (Statistical Methodology), April 2022. Available at: [stat.ME], July 2020] and [Lungu et al. 'Change-point Detection and Segmentation of Discrete Data using Bayesian Context Trees' [stat.ME], March 2022].