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Overview of each module’s analyses, including relevant references and the corresponding R packages used.

Journal: PeerJ

Article Title: EcoNicheS: enhancing ecological niche modeling, niche overlap and connectivity analysis using the shiny dashboard and R package

doi: 10.7717/peerj.19136

Figure Lengend Snippet: Overview of each module’s analyses, including relevant references and the corresponding R packages used.

Article Snippet: The plataform employs the biomod2 R package, a widely used ecological niche modeling framework that supports 12 correlative algorithms: generalized linear models (GLMs), generalized additive models (GAMs), generalized boosting models (often called boosted regression trees; GBM), classification tree analysis (CTA), artificial neural networks (ANNs), surface range envelopes or BIOCLIM (SRE), flexible discriminant analysis (FDA), multiple adaptive regression splines (MARS), random forest (RF), and maximum entropy (MAXENT) using the MIAmaxent ( ) R package MAXNET and extreme gradient boosting (XGBOOST) ( ).

Techniques: Sampling, Generated, Functional Assay

Comparison of analyses performed by different software packages for ecological niche modeling.

Journal: PeerJ

Article Title: EcoNicheS: enhancing ecological niche modeling, niche overlap and connectivity analysis using the shiny dashboard and R package

doi: 10.7717/peerj.19136

Figure Lengend Snippet: Comparison of analyses performed by different software packages for ecological niche modeling.

Article Snippet: The plataform employs the biomod2 R package, a widely used ecological niche modeling framework that supports 12 correlative algorithms: generalized linear models (GLMs), generalized additive models (GAMs), generalized boosting models (often called boosted regression trees; GBM), classification tree analysis (CTA), artificial neural networks (ANNs), surface range envelopes or BIOCLIM (SRE), flexible discriminant analysis (FDA), multiple adaptive regression splines (MARS), random forest (RF), and maximum entropy (MAXENT) using the MIAmaxent ( ) R package MAXNET and extreme gradient boosting (XGBOOST) ( ).

Techniques: Comparison, Software, Selection, Generated, Sampling