Information-Based Stability and Synchrony Measures

Provides functions to to compute a continuum of information-based measures for quantifying the temporal stability of populations, communities, and ecosystems, as well as their associated synchrony, based on species (or species assemblage) biomass or other key variables. When biodiversity data are available, the package also enables the assessment of the corresponding diversity–stability relationships. All measures are applicable in both temporal and spatial contexts. The theoretical and methodological background is detailed in Chao et al. (2025) .

Species-Richness Prediction and Diversity Estimation with R

Estimation of various biodiversity indices and related (dis)similarity measures based on individual-based (abundance) data or sampling-unit-based (incidence) data taken from one or multiple communities/assemblages.

Tools for Exploring Multivariate Data via ICS/ICA

Implementation of Tyler, Critchley, Duembgen and Oja's (JRSS B, 2009, ) and Oja, Sirkia and Eriksson's (AJS, 2006, < https://www.ajs.or.at/index.php/ajs/article/view/vol35,%20no2%263%20-%207>) method of two different scatter matrices to obtain an invariant coordinate system or independent components, depending on the underlying assumptions.

MARS Based ANN Hybrid Model

Multivariate Adaptive Regression Spline (MARS) based Artificial Neural Network (ANN) hybrid model is combined Machine learning hybrid approach which selects important variables using MARS and then fits ANN on the extracted important variables.

Integrative Lasso with Penalty Factors

The core of the package is cvr2.ipflasso(), an extension of glmnet to be used when the (large) set of available predictors is partitioned into several modalities which potentially differ with respect to their information content in terms of prediction. For example, in biomedical applications patient outcome such as survival time or response to therapy may have to be predicted based on, say, mRNA data, miRNA data, methylation data, CNV data, clinical data, etc. The clinical predictors are on average often much more important for outcome prediction than the mRNA data. The ipflasso method takes this problem into account by using different penalty parameters for predictors from different modalities. The ratio between the different penalty parameters can be chosen from a set of optional candidates by cross-validation or alternatively generated from the input data.

Comparison of Variance - Covariance Patterns

Comparison of variance - covariance patterns using relative principal component analysis (relative eigenanalysis), as described in Le Maitre and Mitteroecker (2019) . Also provides functions to compute group covariance matrices, distance matrices, and perform proportionality tests. A worked sample on the body shape of cichlid fishes is included, based on the dataset from Kerschbaumer et al. (2013) .

'REPPlab' via a Shiny Application

Performs exploratory projection pursuit via 'REPPlab' (Daniel Fischer, Alain Berro, Klaus Nordhausen & Anne Ruiz-Gazen (2019) ) using a Shiny app.

Time Series Forecasting using ARIMA-ANN Hybrid Model

Testing, Implementation, and Forecasting of the ARIMA-ANN hybrid model. The ARIMA-ANN hybrid model combines the distinct strengths of the Auto-Regressive Integrated Moving Average (ARIMA) model and the Artificial Neural Network (ANN) model for time series forecasting.For method details see Zhang, GP (2003) .

Generalized Spline Mixed Effect Models for Longitudinal Breath Data

Automated analysis and modeling of longitudinal 'omics' data (e.g. breath 'metabolomics') using generalized spline mixed effect models. Including automated filtering of noise parameters and determination of breakpoints.

Stratified-Petersen Analysis System

The Stratified-Petersen Analysis System (SPAS) is designed to estimate abundance in two-sample capture-recapture experiments where the capture and recaptures are stratified. This is a generalization of the simple Lincoln-Petersen estimator. Strata may be defined in time or in space or both, and the s strata in which marking takes place may differ from the t strata in which recoveries take place. When s=t, SPAS reduces to the method described by Darroch (1961) . When s. Schwarz and Taylor (1998) describe the use of SPAS in estimating return of salmon stratified by time and geography. A related package, BTSPAS, deals with temporal stratification where a spline is used to model the distribution of the population over time as it passes the second capture location. This is the R-version of the (now obsolete) standalone Windows program of the same name.