Calculate text polarity sentiment at the sentence level and
         optionally aggregate by rows or grouping variable(s).

Implements parsimonious mixtures of MSEN and MTIN distributions via expectation-
    maximization based algorithms for model-based clustering. For each mixture
    component, parsimony is reached via the eigen-decomposition of the scale 
    matrices and by imposing a constraint on the tailedness parameter. This produces
    a family of 28 parsimonious mixture models for each distribution.

Optimized prediction based on textual sentiment, accounting for the intrinsic challenge that sentiment can be computed and pooled across texts and time in various ways. See Ardia et al. (2021) <doi:10.18637/jss.v099.i02>.

A framework that joins topic modeling and sentiment analysis of
  textual data. The package implements a fast Gibbs sampling estimation of
  Latent Dirichlet Allocation (Griffiths and Steyvers (2004)
  <doi:10.1073/pnas.0307752101>) and Joint Sentiment/Topic Model (Lin, He,
  Everson and Ruger (2012) <doi:10.1109/TKDE.2011.48>). It offers a variety of
  helpers and visualizations to analyze the result of topic modeling. The
  framework also allows enriching topic models with dates and externally
  computed sentiment measures. A flexible aggregation scheme enables the
  creation of time series of sentiment or topical proportions from the enriched
  topic models. Moreover, a novel method jointly aggregates topic proportions
  and sentiment measures to derive time series of topical sentiment.

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>.

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>.