Tools created for image analysis in researches. There are functions associated with image editing, segmentation, and obtaining biometric measurements (Este pacote foi idealizado para para a analise de imagens em pesquisas. Ha funcoes associadas a edicao de imagens, segmentacao, e obtencao de medidas biometricas) <https://www.expstat.com/pacotes-do-r/expimage>.

The exponential integrals E_1(x), E_2(x), E_n(x) and
  Ei(x), and the incomplete gamma function G(a, x) defined for
  negative values of its first argument. The package also gives easy
  access to the underlying C routines through an API; see the package
  vignette for details. A test package included in sub-directory
  example_API provides an implementation. C routines derived from the
  GNU Scientific Library <https://www.gnu.org/software/gsl/>.

The Australian Regulatory Guidelines for Prescription
    Medicines (ARGPM), guidance on "Stability testing for prescription
    medicines", recommends to predict the shelf life of chemically derived
    medicines from stability data by taking the worst case situation at batch
    release into account. Consequently, if a change over time is observed,
    a release limit needs to be specified. Finding a release limit and the
    associated shelf life is supported, as well as the standard approach
    that is recommended by guidance Q1E "Evaluation of stability data"
    from the International Council for Harmonisation (ICH).

It enables detailed interpretation of complex classification and regression models through Shapley analysis including data-driven characterization of subgroups of individuals. Furthermore, it facilitates multi-measure model evaluation, model fairness, and decision curve analysis. Additionally, it offers enhanced visualizations with interactive elements.

Generates explanations for classification and regression models and visualizes them.
 Explanations are generated for individual predictions as well as for models as a whole. Two explanation methods
 are included, EXPLAIN and IME. The EXPLAIN method is fast but might miss explanations expressed redundantly
 in the model. The IME method is slower as it samples from all feature subsets.
 For the EXPLAIN method see Robnik-Sikonja and Kononenko (2008) <doi:10.1109/TKDE.2007.190734>, 
 and the IME method is described in Strumbelj and Kononenko (2010, JMLR, vol. 11:1-18).
 All models in package 'CORElearn' are natively supported, for other prediction models a wrapper function is provided 
 and illustrated for models from packages 'randomForest', 'nnet', and 'e1071'.