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>.

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) .

Analysing SNP Data to Identify Sex-Linked Markers

Identifies, filters and exports sex linked markers using 'SNP' (single nucleotide polymorphism) data. To install the other packages, we recommend to install the 'dartRverse' package, that supports the installation of all packages in the 'dartRverse'. If you want understand the applied rational to identify sexlinked markers and/or want to cite 'dartR.sexlinked', you find the information by typing citation('dartR.sexlinked') in the console.

Fits Expectile Regression for Panel Fixed Effect Model

Fits the Expectile Regression for Fixed Effect (ERFE) estimator. The ERFE model extends the within-transformation strategy to solve the incidental parameter problem within the expectile regression framework. The ERFE model estimates the regressor effects on the expectiles of the response distribution. The ERFE estimate corresponds to the classical fixed-effect within-estimator when the asymmetric point is 0.5. The paper by Barry, Oualkacha, and Charpentier (2021, ) gives more details about the ERFE model.

Multiple Comparisons Procedures Based on Studentized Midrange and Range Distributions

Apply tests of multiple comparisons based on studentized 'midrange' and 'range' distributions. The tests are: Tukey Midrange ('TM' test), Student-Newman-Keuls Midrange ('SNKM' test), Means Grouping Midrange ('MGM' test) and Means Grouping Range ('MGR' test). The first two tests were published by Batista and Ferreira (2020) . The last two were published by Batista and Ferreira (2023) .

Attraction Indian Buffet Distribution

An implementation of probability mass function and sampling algorithms is provided for the attraction Indian buffet distribution (AIBD), originally from Dahl (2016) < https://ww2.amstat.org/meetings/jsm/2016/onlineprogram/ActivityDetails.cfm?SessionID=213038>.

Fuzzy and Non-Fuzzy Classifiers

It provides classifiers which can be used for discrete variables and for continuous variables based on the Naive Bayes and Fuzzy Naive Bayes hypothesis. Those methods were developed by researchers belong to the 'Laboratory of Technologies for Virtual Teaching and Statistics (LabTEVE)' and 'Laboratory of Applied Statistics to Image Processing and Geoprocessing (LEAPIG)' at 'Federal University of Paraiba, Brazil'. They considered some statistical distributions and their papers were published in the scientific literature, as for instance, the Gaussian classifier using fuzzy parameters, proposed by 'Moraes, Ferreira and Machado' (2021) .

Thresholded Partial Least Squares Model for Neuroimaging Data

Uses thresholded partial least squares algorithm to create a regression or classification model. For more information, see Lee, Bradlow, and Kable .

Goodness of Fit Tests Based on Empirical Distribution Functions

Routines that allow the user to run goodness of fit tests based on empirical distribution functions for formal model evaluation in a general likelihood model. In addition, functions are provided to test if a sample follows Normal or Gamma distributions, validate the normality assumptions in a linear model, and examine the appropriateness of a Gamma distribution in generalized linear models with various link functions. Michael Arthur Stephens (1976) < http://www.jstor.org/stable/2958206>.

Analysing 'SNP' Data to Support Captive Breeding

Functions are provided that facilitate the analysis of SNP (single nucleotide polymorphism) data to answer questions regarding captive breeding and relatedness between individuals. 'dartR.captive' is part of the 'dartRverse' suit of packages. Gruber et al. (2018) . Mijangos et al. (2022) .