Functions to Compute Compositional Turnover Using Zeta Diversity
Fitting Generalized Linear Models with constraint on the coefficients ...
Generalized Linear Models fitting method with negative coefficients co...
Transform data using I-splines
Pie Chart, considering negative values as zeros
Plots I-splines for Multi-Site Generalised Dissimilarity Modelling
Zeta distance-decay plotting
Zeta distance-decay plotting for multiple orders
Zeta diversity decline plotting
Plotting of zeta diversity scaling with sample grain dependency based ...
Plotting of zeta diversity scaling with sample grain using hierarchica...
Predict zeta values for new environmental and distance data
Perform an I-spline regression
Rescaling of data based on the minimum distance between sites
Rescaling of data following a hierarchical increase in grain size
Computing splines coordinates from I-spline-based multi-site generalis...
Zeta distance decay for a specific number of assemblages or sites
Zeta distance decay for a range of numbers of assemblages or sites
Expectation of zeta diversity decline
Zeta diversity decline using Monte Carlo sampling
Multi-site generalised dissimilarity modelling for a set of environmen...
Expectation of zeta diversity for a specific number of assemblages or ...
Number of species in common between a specific number of assemblages o...
Zeta diversity for a specific number of assemblages or sites using Mon...
Sensitivity analysis for the sample size of zeta
Zeta diversity scaling with sample grain dependency based on the minim...
Zeta diversity scaling with sample grain using hierarchical increases ...
Variation partitioning for zeta diversity
Functions to compute compositional turnover using zeta-diversity, the number of species shared by multiple assemblages. The package includes functions to compute zeta-diversity for a specific number of assemblages and to compute zeta-diversity for a range of numbers of assemblages. It also includes functions to explain how zeta-diversity varies with distance and with differences in environmental variables between assemblages, using generalised linear models, linear models with negative constraints, generalised additive models,shape constrained additive models, and I-splines.