Neural network has potential in forestry modelling. This package is designed to create and assess Artificial Intelligence based Neural Networks with varying architectures for prediction of volume of forest trees using two input features: height and diameter at breast height, as they are the key factors in predicting volume, therefore development and validation of efficient volume prediction neural network model is necessary. This package has been developed using the algorithm of Tabassum et al. (2022) <doi:10.18805/ag.D-5555>.

Contains functions for evaluating & comparing the performance of Binary classification models. Functions can be called either statically or interactively (as Shiny Apps).

Functions to analyse missing value mechanisms and to impute data sets in the context of bottom-up MS-based proteomics.

Implement a multivariate analysis of the impact of items to identify a bias in the questionnaire validation of Likert-type scale variables. The items requires considering a null value (category doesn't have tendency). Offering frequency, importance and impact of the items.

A non-parametric effect size measure capturing changes in central tendency or shape of data distributions. The package provides the necessary functions to calculate and plot the Impact effect size measure between two groups.

Implements the compartment model from Tokars (2018) 
  <doi:10.1016/j.vaccine.2018.10.026>. This enables quantification of 
  population-wide impact of vaccination against vaccine-preventable 
  diseases such as influenza.

Functions to read, process and analyse accelerometer
    data related to mechanical loading variables. This package is
    developed and tested for use with raw accelerometer data from
    triaxial 'ActiGraph' <https://theactigraph.com> accelerometers.

Package provides tools for modular Bayesian model calibration. these tools allow for 
  posterior exploration with sampling methods including tempering and adaptive Markov Chain Monte Carlo (MCMC).
  Allows for pooled calibration or hierarchal calibration of parameters.
  For more information see Francom et al., 2025 <DOI:10.1137/24M1644092>.