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Multiples Comparisons Procedures
Performs the execution of the main procedures of multiple comparisons in the literature, Scott-Knott (1974) < http://www.jstor.org/stable/2529204>, Batista (2016) < http://repositorio.ufla.br/jspui/handle/1/11466>, including graphic representations and export to different extensions of its results. An additional part of the package is the presence of the performance evaluation of the tests (Type I error per experiment and the power). This will assist the user in making the decision for the chosen test.
Incorporate Expert Opinion with Parametric Survival Models
Enables users to incorporate expert opinion with parametric survival analysis using a Bayesian or frequentist approach. Expert Opinion can be provided on the survival probabilities at certain time-point(s) or for the difference in mean survival between two treatment arms. Please reference it's use as Cooney, P., White, A. (2023)
Analysing 'SNP' and 'Silicodart' Data Generated by Genome-Wide Restriction Fragment Analysis
Facilitates the analysis of SNP (single nucleotide polymorphism)
and silicodart (presence/absence) data. 'dartR.popgen' provides a suit of
functions to analyse such data in a population genetics context. It provides
several functions to calculate population genetic metrics and to study
population structure. Quite a few functions need additional software to be
able to run (gl.run.structure(), gl.blast(), gl.LDNe()). You find detailed description
in the help pages how to download and link the packages so the function can
run the software. 'dartR.popgen' is part of the the 'dartRverse' suit of packages.
Gruber et al. (2018)
Estimating Aboveground Biomass and Its Uncertainty in Tropical Forests
Contains functions for estimating above-ground biomass/carbon and its uncertainty in tropical forests. These functions allow to (1) retrieve and correct taxonomy, (2) estimate wood density and its uncertainty, (3) build height-diameter models, (4) manage tree and plot coordinates, (5) estimate above-ground biomass/carbon at stand level with associated uncertainty. To cite ‘BIOMASS’, please use citation(‘BIOMASS’). For more information, see Réjou-Méchain et al. (2017)
Estimate Quantiles Curves
Non-parametric methods as local normal regression, polynomial local regression and penalized cubic B-splines regression are used to estimate quantiles curves. See Fan and Gijbels (1996)
Deal with Check Outputs
Deal with packages 'check' outputs and reduce the risk of rejection by 'CRAN' by following policies.
Applying Landscape Genomic Methods on 'SNP' and 'Silicodart' Data
Provides landscape genomic functions to analyse 'SNP' (single nuclear polymorphism) data, such as least cost path analysis and isolation by distance. Therefore each sample needs to have coordinate data attached (lat/lon) to be able to run most of the functions. 'dartR.spatial' is a package that belongs to the 'dartRverse' suit of packages and depends on 'dartR.base' and 'dartR.data'.
Install and Load the 'dartRverse' Suits of Packages
Provides a single function that supports the installation of all packages belonging to the 'dartRverse'. The 'dartRverse' is a set of packages that work together to analyse SNP (single nuclear polymorphism) data. All packages aim to have a similar 'look and feel' and are based on the same type of data structure ('genlight'), with additional metadata for loci and individuals (samples). For more information visit the 'GitHub' pages < https://github.com/green-striped-gecko/dartRverse>.
Computer Simulations of 'SNP' Data
Allows to simulate SNP data using genlight objects. For example, it is straight forward to simulate a simple drift scenario with exchange of individuals between two populations or create a new genlight object based on allele frequencies of an existing genlight object.
Sequential Outlier Identification for Model-Based Clustering
Sequential outlier identification for Gaussian mixture models using
the distribution of Mahalanobis distances. The optimal number
of outliers is chosen based on the dissimilarity between the theoretical and
observed distributions of the scaled squared sample Mahalanobis distances.
Also includes an extension for Gaussian linear cluster-weighted models using
the distribution of studentized residuals.
Doherty, McNicholas, and White (2025)