\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Software** & **Year** & **Authors** & **Platform** & **Key Innovations** \\ \hline MultBipidot & 2008 & [PERSON] [16] & R, Matlab & First comprehensive GUI for HJ-Biplot; basic PCA and clustering integration. \\ \hline BPCA & 2011 & [PERSON], [PERSON], and [PERSON] [17] & R & Biplots based on principal components; 2D/3D visualization. \\ \hline GGEBiplotGUI & 2013 & [PERSON], [PERSON], and [PERSON] [18] & R & Specialized for genotype-environment analysis using biplots. \\ \hline MultibiplotGUI & 2015 & [PERSON], [PERSON], [PERSON], and [PERSON] [19] & R & Multibiplot analysis with bootstrap-based inferential results. \\ \hline DynBiplotGUI & 2014 & [PERSON] [20] & R & GUI for dynamic and classical biplots with 2-way and 3-way matrices. \\ \hline TextMiningGUI & 2021 & [PERSON] and [PERSON] [21] & R & Integration of text mining with HJ-Biplot for semantic analysis. \\ \hline PyBiplots & 2021 & [PERSON] [22] & Python & First Python implementation; compatible with ML workflows. \\ \hline SparseBiplots & 2022 & [PERSON], [PERSON], [PERSON], and [PERSON] [23] & R & Implements three methods of regularization in each case of the HJ-Biplot: Ridge, LASSO and Elastic Net. \\ \hline \end{tabular}
\end{table} 
Table 2: Software implementing the HJ-Biplot technique. As shown in Table 2, various programs and packages have been developed to support the HJ-Biplot technique, providing tools for its implementation and visualization. These include R packages such as BPCA and MultibilotGUI as well as more recent applications like SparseBiplots and LDABiplots. These programs allow for an in-depth analysis and the generation of interactive graphics that facilitate data interpretation, reflecting the growing importance of the HJ-Biplot in research and professional practice. Each software tool implementing the HJ-Biplot technique offers unique advantages and limitations, making them suitable for different research contexts. For instance, MultBiplot [16] is widely recognized for its user-friendly interface and robust handling of structured datasets, making it an excellent choice for researchers new to multivariate analysis. However, its performance can be limited when dealing with very large datasets or high-dimensional data. Another relevant software is BPCA (Biplot based on principal components analysis) [17], which allows the creation of 2D and 3D Biplots using the HJ-Biplot method based on principal components. On the other hand, SparseBiplots [23] addresses these limitations by incorporating penalization methods like LASSO and Elastic Net, which are particularly effective for reducing dimensionality and improving interpretability in genomics and big data applications. Despite its advanced capabilities, SparseBiplots requires a deeper understanding of penalization techniques, which may pose a challenge for less experienced users. Similarly, PyBiplots [22] stands out for its compatibility with Python, which is a programming language widely used in machine learning and data science. This makes it an attractive option for researchers looking to integrate HJ-Biplot analysis into broader machine learning workflows. However, the lack of extensive documentation and community support for PyBiplots can be a drawback for users unfamiliar with Python. Meanwhile, GGEBiplotGUI [18] is specifically designed for agricultural research, offering specialized tools for analyzing genotype-by-environment interactions. While it excels in this niche, its applicability to other fields may be limited. These examples highlight the importance of selecting the right tool based on the specific requirements of the research project, balancing ease of use, functionality, and compatibility with existing workflows. In this context, the present systematic review and meta-analysis seek to provide a comprehensive synthesis of the studies utilizing the HJ-Biplot technique from its inception in 1986 to December 2024.