A toolset for Geometric Morphometrics and mesh processing. This
    includes (among other stuff) mesh deformations based on reference points,
    permutation tests, detection of outliers, processing of sliding
    semi-landmarks and semi-automated surface landmark placement.

Extract cross sections from long bone meshes at specified intervals along the diaphysis. Calculate two and three-dimensional morphometric maps, cross-sectional geometric parameters, and semilandmarks on the periosteal and endosteal contours of each cross section.

Implements adaptive landscape methods first described by Polly et al. (2016) <doi:10.1080/02724634.2016.1111225> for the integration, analysis and visualization of biological trait data on a phenotypic morphospace - typically defined by shape metrics.

Tools for multivariate analyses of morphological data, wrapped in one package, to make the workflow convenient and fast. Statistical and graphical tools provide a comprehensive framework for checking and manipulating input data, statistical analyses, and visualization of results. Several methods are provided for the analysis of raw data, to make the dataset ready for downstream analyses. Integrated statistical methods include hierarchical classification, principal component analysis, principal coordinates analysis, non-metric multidimensional scaling, and multiple discriminant analyses: canonical, stepwise, and classificatory (linear, quadratic, and the non-parametric k nearest neighbours). The philosophy of the package is described in Šlenker et al. 2022.

Agricultural data for 1888-2021 from the Morrow Plots at the 
    University of Illinois. The world's second oldest ongoing agricultural 
    experiment, the Morrow Plots measure the impact of crop rotation and 
    fertility treatments on corn yields. The data includes planting information
    and annual yield measures for corn grown continuously and in rotation with
    other crops, in treated and untreated soil.

Advanced methods for a valuable quantitative environmental risk 
   assessment using Bayesian inference of survival and reproduction Data. Among
   others, it facilitates Bayesian inference of the general unified
   threshold model of survival (GUTS). See our companion paper 
   Baudrot and Charles (2021) <doi:10.21105/joss.03200>,
   as well as complementary details in Baudrot et al. (2018)
   <doi:10.1021/acs.est.7b05464> and Delignette-Muller et al.
   (2017) <doi:10.1021/acs.est.6b05326>.