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.

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>.

Tools to Download and Explore Datasets from Data.gouv.fr

Provides a client for the public API of data.gouv.fr, the French government's open data platform. It helps you find a dataset that matches your interests, judge whether it is usable, download it, and re-fetch the exact same table later in a reproducible way. You can search the catalog and filter by producer or theme (dg_find_datasets(), dg_find_organization(), dg_find_topics()), pull a dataset's tabular resources into tidy tibbles (dg_pull_dataset()), inspect the documented variables of its data schema (dg_schema()), and compute summary metrics such as size, number of columns and missing-value rate (dg_summary(), dg_summarise()). Each returned table carries a stable identifier (dg_table_id(), dg_refetch()) so it can be re-fetched later. Requests are built on top of 'httr2'.

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 .

Datasets for 'spatstat' Family

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

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>.