Provides a unified data layer for single-cell, spatial and bulk
 T-cell and B-cell immune receptor repertoire data. Think AnnData or SeuratObject,
 but for AIRR data, a.k.a. Adaptive Immune Receptor Repertoire, VDJ-seq, RepSeq, or 
 VDJ sequencing data.

A comprehensive toolkit for clinical Human Leukocyte Antigen (HLA) informatics, built on 'tidyverse' <https://tidyverse.tidyverse.org/> principles and making use of genotype list string (GL string, Mack et al. (2023) <doi:10.1111/tan.15126>) for storing and computing HLA genotype data.  Specific functionalities include: coercion of HLA data in tabular format to and from GL string; calculation of matching and mismatching in all directions, with multiple output formats; automatic formatting of HLA data for searching within a GL string; truncation of molecular HLA data to a specific number of fields; and reading HLA genotypes in HML files and extracting the GL string. This library is intended for research use. Any application making use of this package in a clinical setting will need to be independently validated according to local regulations.

It offers a sophisticated and versatile tool for creating and evaluating artificial intelligence based neural network models tailored for regression analysis on datasets with continuous target variables. Leveraging the power of neural networks, it allows users to experiment with various hidden neuron configurations across two layers, optimizing model performance through "5 fold"" or "10 fold"" cross validation. The package normalizes input data to ensure efficient training and assesses model accuracy using key metrics such as R squared (R2), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Percentage Error (PER). By storing and visualizing the best performing models, it provides a comprehensive solution for precise and efficient regression modeling making it an invaluable tool for data scientists and researchers aiming to harness AI for predictive analytics.

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>.