Display a 2D-matrix data as a interactive zoomable gray-scale image viewer, providing tools for manual data inspection. The viewer window shows cursor guiding lines and a corresponding data slices for both axes at the current cursor position. A tool-bar allows adjusting image display brightness/contrast through WebGL filters and performing basic high-pass/low-pass filtering.

Provides functions to measure and test imaginary cognitive social structure (CSS) motifs, which are patterns of perceived relationships among individuals in a social network. Includes tools for calculating motif frequencies, comparing observed motifs to expected distributions, and visualizing motif structures. Implements methods described in Tanaka and Vega Yon (2023) <doi:10.1016/j.socnet.2023.11.005>.

Simulate general insurance policies, losses and loss emergence. The functions contemplate 
  deterministic and stochastic policy retention and growth scenarios. Retention and growth rates are percentages relative
  to the expiring portfolio. Claims are simulated for each policy. This is accomplished either be assuming a frequency
  distribution per development lag or by generating random wait times until claim emergence and settlement. Loss simulation 
  uses standard loss distributions for claim amounts.

Provides fast application of image filters to data matrices,
    using R and C++ algorithms.

This is an Automatic Item Generator for Psychological Assessment. Items created with the 'IMak' package should not be used in applied settings as part of the working protocol without ensuring first that the items meet the required psychometric quality standards (see Blum & Holling, 2018) <DOI:10.3389/fpsyg.2018.01286>.

A model that provides researchers with a powerful tool for the classification
    and study of native corn by aiding in the identification of racial complexes
    which are fundamental to Mexico's agriculture and culture. This package has been
    developed based on data collected by "Proyecto Global de Maíces Nativos México",
    which has conducted exhaustive surveys across the country to document the
    qualitative and quantitative characteristics of different types of native maize.
    The trained model uses a robust and diverse dataset, enabling it to achieve an
    80% accuracy in classifying maize racial complexes. The characteristics included
    in the analysis comprise geographic location, grain and cob colors, as well as
    various physical measurements, such as lengths and widths.