Fitting and validation of machine learning algorithms
             for volume prediction of trees, currently for conifer trees based on
			 diameter at breast height and height as explanatory variables.

A comprehensive framework for bioinformatics exploratory analysis of bulk and single-cell
    T-cell receptor and antibody repertoires. It provides seamless data loading, analysis and
    visualisation for AIRR (Adaptive Immune Receptor Repertoire) data, both bulk immunosequencing (RepSeq)
    and single-cell sequencing (scRNAseq). Immunarch implements most of the widely used AIRR analysis methods,
    such as: clonality analysis, estimation of repertoire similarities in distribution of clonotypes
    and gene segments, repertoire diversity analysis, annotation of clonotypes using external immune receptor
    databases and clonotype tracking in vaccination and cancer studies. A successor to our
    previously published 'tcR' immunoinformatics package (Nazarov 2015) <doi:10.1186/s12859-015-0613-1>.

Simulate full B-cell and T-cell receptor repertoires using an in silico 
    recombination process that includes a wide variety of tunable parameters to introduce noise and biases. 
    Additional post-simulation modification functions allow the user to implant motifs or codon biases as 
    well as remodeling sequence similarity architecture. The output repertoires contain records of all 
    relevant repertoire dimensions and can be analyzed using provided repertoire analysis functions.
    Preprint is available at bioRxiv (Weber et al., 2019 <doi:10.1101/759795>).

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>.