Unofficial client for 'Sentry' <https://sentry.io>,
  a self-hosted or cloud-based error-monitoring service. It will inform about
  errors in real-time, and includes integration with the 'Plumber' package.

To calculate the standard error of measurement (SEM) to assess the observer variability (inter- and intra-observer variation).
    The methods used in this package are referenced from Zoran B. Popović (2017) <doi:10.21037/cdt.2017.03.12>.

Implements the Segment Profile Extraction via Pattern
  Analysis method for row-mean-centered multivariate data. Core
  capabilities include SVD-based row-isometric biplot
  construction, bias-corrected and accelerated, and percentile
  bootstrap confidence intervals for domain coordinates and
  per-person direction cosines, Procrustes alignment of bootstrap
  replicates across planes, parallel analysis for dimensionality
  selection, and segment profile reconstruction in planes defined
  by pairs of singular dimensions. A synthetic Woodcock-Johnson IV
  look-alike dataset is provided for examples and testing. The
  method is described in Kim and Grochowalski (2019)
  <doi:10.1007/s00357-018-9277-7>.

Regression context for the Partial Least Squares framework for 
    Extreme values. Estimations of the Shrinkage for Extreme Partial Least-Squares 
    (SEPaLS) estimators, an adaptation of the original Partial Least Squares 
    (PLS) method tailored to the extreme-value framework.
    The SEPaLS project is a joint work by Stephane Girard, Hadrien Lorenzo and 
    Julyan Arbel.
    R code to replicate the results of the paper is available at 
    <https://github.com/hlorenzo/SEPaLS_simus>.
    Extremes within PLS was already studied by one of the authors, see M 
    Bousebeta, G Enjolras, S Girard (2023) <doi:10.1016/j.jmva.2022.105101>.

Maximum likelihood estimation of the parameters
    of matrix and 3rd-order tensor normal distributions with unstructured
    factor variance covariance matrices, two procedures, and for unbiased
    modified likelihood ratio testing of simple and double separability
    for variance-covariance structures, two procedures. References:
    Dutilleul P. (1999) <doi:10.1080/00949659908811970>, 
    Manceur AM, Dutilleul P. (2013)
    <doi:10.1016/j.cam.2012.09.017>, and Manceur AM, Dutilleul 
    P. (2013) <doi:10.1016/j.spl.2012.10.020>.