<\/p>\n\n\n
EvoDA<\/a>\u00a0is an R program that implements discriminant analysis for evolutionary model selection\u00a0(Lozano et al. 2026)<\/a>.<\/p>\n ANDES<\/a> is an implementation of six machine learning algorithms for identifying anomalous genomic regions with functional data analysis (Kanjilal et al. 2025)<\/a>.<\/p>\n GAP<\/a> is an R implementation of a neural network for predicting genotype-phenotype associations from gaps in multi-species sequence alignments (Islam et al. 2025)<\/a>.<\/p>\n CLOUDe<\/a> is an R implementation of three machine learning methods for predicting evolutionary targets and parameters of gene deletion events from expression data in two species (Campelo dos Santos et al. 2024)<\/a>.<\/p>\n ROBRT i<\/a>s an R implementation of robust L1, M, S, and MM phylogenetic independent contrast regression estimators for evaluating trait associations on phylogenetic trees (Adams et al. 2023<\/a>).<\/p>\n PiXi<\/a> is an R implementation of three machine learning methods for predicting gene expression divergence between single-copy orthologs in two species (Piya et al. 2023)<\/a>.<\/p>\n CLOUD<\/a> is an R implementation of a neural network for predicting duplicate gene retention mechanisms and their evolutionary parameters from expression data in two species (DeGiorgio and Assis 2021)<\/a>.<\/p>\n CDROM<\/a> is an R implementation of a decision tree for classifying duplicate gene retention mechanisms from gene expression data in two species (Assis and Bachtrog 2013, Perry and Assis 2016)<\/a>.<\/p>\n Bridges<\/a>\u00a0is a C program that implements a heuristic search algorithm for identifying local similarities in long DNA sequences\u00a0(Assis and Kondrashov 2010)<\/a>.<\/p>","protected":false},"excerpt":{"rendered":" EvoDA\u00a0is an R program that implements discriminant analysis for evolutionary model selection\u00a0(Lozano et al. 2026). 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