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.

Time Series Prediction with Integrated Tuning

Time series prediction is a critical task in data analysis, requiring not only the selection of appropriate models, but also suitable data preprocessing and tuning strategies. TSPredIT (Time Series Prediction with Integrated Tuning) is a framework that provides a seamless integration of data preprocessing, decomposition, model training, hyperparameter optimization, and evaluation. Unlike other frameworks, TSPredIT emphasizes the co-optimization of both preprocessing and modeling steps, improving predictive performance. It supports a variety of statistical and machine learning models, filtering techniques, outlier detection, data augmentation, and ensemble strategies. More information is available in Salles et al. .

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>.

Simplify Access to Brazilian Social Data

Provides tools for downloading and processing microdata from the PNAD Contínua (PNADC, Continuous National Household Sample Survey), a rotating panel survey published quarterly by IBGE (Brazilian Institute of Geography and Statistics). Includes panel identification algorithms for linking individuals across survey waves.

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>.

Datasets for 'spatstat' Family

Contains all the datasets for the 'spatstat' family of packages.

Importing and Analysing 'SNP' and 'Silicodart' Data Generated by Genome-Wide Restriction Fragment Analysis

Functions are provided that facilitate the import and analysis of 'SNP' (single nucleotide polymorphism) and 'silicodart' (presence/absence) data. The main focus is on data generated by 'DarT' (Diversity Arrays Technology), however, data from other sequencing platforms can be used once 'SNP' or related fragment presence/absence data from any source is imported. Genetic datasets are stored in a derived 'genlight' format (package 'adegenet'), that allows for a very compact storage of data and metadata. Functions are available for importing and exporting of 'SNP' and 'silicodart' data, for reporting on and filtering on various criteria (e.g. 'CallRate', heterozygosity, reproducibility, maximum allele frequency). Additional functions are available for visualization (e.g. Principle Coordinate Analysis) and creating a spatial representation using maps. 'dartR' supports also the analysis of 3rd party software package such as 'newhybrid', 'structure', 'NeEstimator' and 'blast'. Since version 2.0.3 we also implemented simulation functions, that allow to forward simulate 'SNP' dynamics under different population and evolutionary dynamics. Comprehensive tutorials and support can be found at our 'github' repository: github.com/green-striped-gecko/dartR/. If you want to cite 'dartR', you find the information by typing citation('dartR') in the console.