Measures mobility in a population through transition matrices and mobility indices. Relative, mixed, and absolute transition matrices are supported. The Prais-Bibby, Absolute Movement, Origin Specific, and Weighted Group Mobility indices are supported. Example income and grade data are included.

Framework for the simulation framework for the simulation of complex breeding programs and compare their economic and genetic impact. The package is also used as the background simulator for our a web-based interface <http:www.mobps.de>. Associated publication: Pook et al. (2020) <doi:10.1534/g3.120.401193>.

Functions for calculating metrics for the measurement biodiversity
    and its changes across scales, treatments, and gradients. The methods 
    implemented in this package are described in:
    Chase, J.M., et al. (2018) <doi:10.1111/ele.13151>, 
    McGlinn, D.J., et al. (2019) <doi:10.1111/2041-210X.13102>,
    McGlinn, D.J., et al. (2020) <doi:10.1101/851717>, and
    McGlinn, D.J., et al. (2023) <doi:10.1101/2023.09.19.558467>.

Simulation, analysis and sampling of spatial
    biodiversity data (May, Gerstner, McGlinn, Xiao & Chase 2017)
    <doi:10.1111/2041-210x.12986>.
    In the simulation tools user define the numbers of
    species and individuals, the species abundance distribution and species
    aggregation. Functions for analysis include species rarefaction 
    and accumulation curves, species-area relationships and the distance
    decay of similarity.

Implements the Multi-Objective Clustering Algorithm Guided by a-Priori Biological Knowledge (MOC-GaPBK) which was proposed by Parraga-Alava, J. et. al. (2018) <doi:10.1186/s13040-018-0178-4>.

Provides methods to analyze cluster alternatives based on multi-objective optimization of cluster validation indices. For details see Kraus et al. (2011) <doi:10.1007/s00180-011-0244-6>.

The two main functionalities of this package are creating
    mock objects (functions) and selectively intercepting calls to a given
    function that originate in some other function. It can be used with
    any testing framework available for R. Mock objects can be injected
    with either this package's own stub() function or a similar
    with_mocked_binding() facility present in the 'testthat' package.