Bundles functions used to analyze the harmfulness of trial errors in criminal trials.
    Functions in the Scientific Analysis of Trial Errors ('sate') package help users estimate the 
    probability that a jury will find a defendant guilty given jurors' preferences for a guilty 
    verdict and the uncertainty of that estimate. Users can also compare actual and hypothetical 
    trial conditions to conduct harmful error analysis. The conceptual framework is discussed
    by Barry Edwards, A Scientific Framework for Analyzing the Harmfulness of Trial Errors,
    UCLA Criminal Justice Law Review (2024) <doi:10.5070/CJ88164341> and Barry Edwards, 
    If The Jury Only Knew: The Effect Of Omitted Mitigation Evidence On The Probability Of A Death Sentence,
    Virginia Journal of Social Policy & the Law (2025)
    <https://vasocialpolicy.org/wp-content/uploads/2025/05/Edwards-If-The-Jury-Only-Knew.pdf>.
    The relationship between individual jurors' 
    verdict preferences and the probability that a jury returns a guilty verdict has been studied 
    by Davis (1973) <doi:10.1037/h0033951>; MacCoun & Kerr (1988) <doi:10.1037/0022-3514.54.1.21>, 
    and Devine et el. (2001) <doi:10.1037/1076-8971.7.3.622>, among others.

Herein, we provide a broad variety of functions which are useful
    for handling, manipulating, and visualizing satellite-based remote sensing 
    data. These operations range from mere data import and layer handling (eg 
    subsetting), over Raster* typical data wrangling (eg crop, extend), to more 
    sophisticated (pre-)processing tasks typically applied to satellite imagery 
    (eg atmospheric and topographic correction). This functionality is 
    complemented by a full access to the satellite layers' metadata at any 
    stage and the documentation of performed actions in a separate log file. 
    Currently available sensors include Landsat 4-5 (TM), 7 (ETM+), and 8 
    (OLI/TIRS Combined), and additional compatibility is ensured for the Landsat 
    Global Land Survey data set.