Contains a variety of functions, based around
    regime shift analysis of paleoecological data.
    Citations:
    Rodionov() from Rodionov (2004) <doi:10.1029/2004GL019448>
    Lanzante() from Lanzante (1996) <doi:10.1002/(SICI)1097-0088(199611)16:11%3C1197::AID-JOC89%3E3.0.CO;2-L>
    Hellinger_trans from Numerical Ecology, Legendre & Legendre (ISBN 9780444538680)
    rolling_autoc from Liu, Gao & Wang (2018) <doi:10.1016/j.scitotenv.2018.06.276>
    Sample data sets lake_data & lake_RSI processed from Bush, Silman & Urrego (2004) <doi:10.1126/science.1090795>
    Sample data set January_PDO from NOAA: <https://www.ncei.noaa.gov/access/monitoring/pdo/>.

Downloads spatial data from spatiotemporal asset catalogs 
    ('STAC'), computes standard spectral indices from the Awesome Spectral 
    Indices project (Montero et al. (2023) <doi:10.1038/s41597-023-02096-0>) 
    against raster data, and glues the outputs together into predictor bricks. 
    Methods focus on interoperability with the broader spatial ecosystem; 
    function arguments and outputs use classes from 'sf' and 'terra', and data 
    downloading functions support complex 'CQL2' queries using 'rstac'.

The main purpose of this package is to perform simulation-based
   estimation of stochastic actor-oriented models for longitudinal network
   data collected as panel data. Dependent variables can be single or
   multivariate networks, which can be directed, non-directed, or two-mode;
   and associated actor variables.
   There are also functions for testing parameters and checking goodness of fit.
   An overview of these models is given in Snijders (2017),
   <doi:10.1146/annurev-statistics-060116-054035>.

Provides a way to transform an image into a mosaic composed from a set of smaller images (tiles). It also contains a simple function for creating the tiles from a folder of images directly through R, without the need of any external code. At this moment only the JPEG format is supported, either as input (image and tiles) or output (mosaic transformed image).