Demonstration code showing how (univariate) kernel
	     density estimates are computed, at least conceptually,
	     and allowing users to experiment with different kernels,
	     should they so wish.  The method used follows directly
	     the definition, but gains efficiency by replacing the
	     observations by frequencies in a very fine grid covering
	     the sample range.  A canonical reference is
	     B. W. Silverman, (1998) <doi: 10.1201/9781315140919>.
	     NOTE: the density function in the
	     stats package uses a more sophisticated method based on the
	     fast Fourier transform and that function should be used if
	     computational efficiency is a prime consideration.

Implements life tables, fertility and mortality indicators,
    decomposition methods, Lee-Carter mortality forecasting, Leslie
    matrices, and population pyramids for demographic analysis. Methods
    are described in Preston et al. (2001, ISBN:1557864519) and
    Ustyuzhanin (2025) <doi:10.17323/demreview.v12i4.30415>.

Mimics the demo functionality for 'Shiny' apps in a package. Apps stored to the package subdirectory inst/shiny can be called by demoShiny(topic).

Simulates temporally structured perturbations in matrix population
    models and computes population reduction and integrated demographic
    vulnerability across perturbation regimes. Perturbations can be applied to
    adult survival, juvenile survival, fecundity, all demographic entries, or
    user-defined matrix elements. The package provides tools to simulate
    individual perturbation trajectories, evaluate perturbation grids, and
    summarize demographic vulnerability in structured populations.

A comprehensive set of wrapper functions for the analysis of multiplex metabarcode data. It includes robust wrappers for 'Cutadapt' and 'DADA2' to trim primers, filter reads, perform amplicon sequence variant (ASV) inference, and assign taxonomy. The package can handle single metabarcode datasets, datasets with two pooled metabarcodes, or multiple datasets simultaneously. The final output is a matrix per metabarcode, containing both ASV abundance data and associated taxonomic assignments. An optional function converts these matrices into 'phyloseq' and 'taxmap' objects. For more information on 'DADA2', including information on how DADA2 infers samples sequences, see Callahan et al. (2016) <doi:10.1038/nmeth.3869>. For more details on the demulticoder R package see Sudermann et al. (2025) <doi:10.1094/PHYTO-02-25-0043-FI>.