To create demographic table with simple summary statistics,
        with optional comparison(s) over one or more groups.

Functions for demographic analysis including lifetable
        calculations; Lee-Carter modelling; functional data analysis of
        mortality rates, fertility rates, net migration numbers; and
        stochastic population forecasting.

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.

Estimate population kin counts and its distribution by type, age and sex.
    The package implements one-sex and two-sex framework for studying living-death availability,
    with time varying rates or not, and multi-stage model.

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

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>.