Discriminant Analysis (DA) for evolutionary inference (Qin, X. et al, 2020, <doi:10.22541/au.159256808.83862168>), especially for population genetic structure and community structure inference. This package incorporates the commonly used linear and non-linear, local and global supervised learning approaches (discriminant analysis), including Linear Discriminant Analysis of Kernel Principal Components (LDAKPC), Local (Fisher) Linear Discriminant Analysis (LFDA), Local (Fisher) Discriminant Analysis of Kernel Principal Components (LFDAKPC) and Kernel Local (Fisher) Discriminant Analysis (KLFDA). These discriminant analyses can be used to do ecological and evolutionary inference, including demography inference, species identification, and population/community structure inference.
|Bioconductor views||BiomedicalInformatics ChIPSeq Clustering Coverage DNAMethylation DifferentialExpression DifferentialMethylation DifferentialSplicing Epigenetics FunctionalGenomics GeneExpression GeneSetEnrichment Genetics ImmunoOncology MultipleComparison Normalization Pathways QualityControl RNASeq Regression SAGE Sequencing Software SystemsBiology TimeCourse Transcription Transcriptomics|
|Package repository||View on GitHub|
Install the latest version of this package by entering the following in R:
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.