Functions to estimate and plot smooth or linear population trends, or population indices, 
    from animal or plant count survey data.

Tools for constructing detailed synthetic human populations
     from frequency tables. Add ages based on age groups and sex, create households, add 
     students to education facilities, create employers, add employers to employees, and 
     create interpersonal networks.

Run population simulations using an Individual-Based Model (IBM) compiled in C.

Fit linear splines to species time series to detect population growth scenarios based on Hyndman, R J and Mesgaran, M B and Cousens, R D (2015) <doi:10.1007/s10530-015-0962-8>.

Run simulations to assess the impact of various designs features
    and the underlying biological behaviour on the outcome of a Patient Derived
    Xenograft (PDX) population study. This project can either be deployed to a
    server as a 'shiny' app or installed locally as a package and run the app
    using the command 'populationPDXdesignApp()'.

Given a 
    set of source zone polygons such as
    census tracts or city blocks alongside with population counts and a 
    target zone of incogruent yet superimposed polygon features (such as
    individual buildings) populR transforms population counts from the 
    former to the latter using Areal Interpolation methods.

Perform tasks commonly encountered when
    preparing and analysing demographic data.
    Some functions are intended for end users, and
    others for developers. Includes functions for
    working with life tables.

The main attribute of 'PopVar' is the prediction of genetic variance in bi-parental populations, 
    from which the package derives its name. 'PopVar' contains a set of functions that use phenotypic and genotypic
    data from a set of candidate parents to 1) predict the mean, genetic variance, and superior progeny value of all, 
    or a defined set of pairwise bi-parental crosses, and 2) perform cross-validation to estimate genome-wide prediction 
    accuracy of multiple statistical models. More details are available in Mohammadi, Tiede, and Smith (2015, <doi:10.2135/cropsci2015.01.0030>). 
    A dataset 'think_barley.rda' is included for reference and examples.