Simulates and estimates the Exponential Random Partition Model presented 
    in the paper Hoffman, Block, and Snijders (2023) <doi:10.1177/00811750221145166>. 
    It can also be used to estimate longitudinal partitions, following the model 
    proposed in Hoffman and Chabot (2023) <doi:10.1016/j.socnet.2023.04.002>. 
    The model is an exponential family distribution on the space of partitions 
    (sets of non-overlapping groups) and is called in reference to the Exponential 
    Random Graph Models (ERGM) for networks.

Provides environment hooks that obtain errors and warnings which
    occur during the execution of code to automatically search for solutions.

Errors in data can be located and removed using validation rules from package 
   'validate'. See also Van der Loo and De Jonge (2018) <doi:10.1002/9781118897126>,
   chapter 7.

Support for measurement errors in R vectors, matrices and arrays:
    automatic uncertainty propagation and reporting.
    Documentation about 'errors' is provided in the paper by Ucar, Pebesma &
    Azcorra (2018, <doi:10.32614/RJ-2018-075>), included in this package as a
    vignette; see 'citation("errors")' for details.

Perform a Bayesian estimation of the exploratory reduced
    reparameterized unified model (ErRUM) described by Culpepper and Chen (2018)
    <doi:10.3102/1076998618791306>.

Constructs a 'shiny' app function with interactive displays for summary and analysis of variance regression tables, and parallel coordinate plots of data and residuals.

Creates realistic random trajectories in a 3-D space between two given fix points, so-called conditional empirical random walks (CERWs). The trajectory generation is based on empirical distribution functions extracted from observed trajectories (training data) and thus reflects the geometrical movement characteristics of the mover. A digital elevation model (DEM), representing the Earth's surface, and a background layer of probabilities (e.g. food sources, uplift potential, waterbodies, etc.) can be used to influence the trajectories.
    Unterfinger M (2018). "3-D Trajectory Simulation in Movement Ecology: Conditional Empirical Random Walk". Master's thesis, University of Zurich. <https://www.geo.uzh.ch/dam/jcr:6194e41e-055c-4635-9807-53c5a54a3be7/MasterThesis_Unterfinger_2018.pdf>.
    Technitis G, Weibel R, Kranstauber B, Safi K (2016). "An algorithm for empirically informed random trajectory generation between two endpoints". GIScience 2016: Ninth International Conference on Geographic Information Science, 9, online. <doi:10.5167/uzh-130652>.