Data sets and utilities to accompany the second edition of "Foundations and Applications of Statistics: an Introduction using R" (R Pruim, published by AMS, 2017), a text covering topics from probability and mathematical statistics at an advanced undergraduate level. R is integrated throughout, and access to all the R code in the book is provided via the snippet() function.

Infrastrcture for creating rich, dynamic web content using R scripts while maintaining very fast response time.

Frequentist assisted by Bayes (FAB) confidence interval construction. See Adaptive multigroup confidence intervals with constant coverage by Yu and Hoff <DOI:10.1093/biomet/asy009> and Exact adaptive confidence intervals for linear regression coefficients by Hoff and Yu <DOI:10.1214/18-EJS1517>.

Computes functional rarity indices as proposed by Violle et al. (2017) <doi:10.1016/j.tree.2017.02.002>. Various indices can be computed using both regional and local information. Functional Rarity combines both the functional aspect of rarity as well as the extent aspect of rarity. funrar is presented in Grenié et al. (2017) <doi:10.1111/ddi.12629>.

Brings a set of tools to help and automatically realise the description of principal component analyses (from FactoMineR functions). Detection of existing outliers, identification of the informative components, graphical views and dimensions description are performed threw dedicated functions. The Investigate() function performs all these functions in one, and returns the result as a report document (Word, PDF or HTML).

Efficient computation of the Liu regression coefficient paths, Liu-related statistics and information criteria for a grid of the regularization parameter. The computations are based on the C++ library Armadillo through the R package Rcpp'.

Feature flags allow developers to turn features of their software on and off in form of configuration. This package provides functions for creating feature flags in code. It exposes an interface for defining own feature flags which are enabled based on custom criteria.

This package provides a friendly (flexible) Markov Chain Monte Carlo (MCMC) framework for implementing Metropolis-Hastings algorithm in a modular way allowing users to specify automatic convergence checker, personalized transition kernels, and out-of-the-box multiple MCMC chains using parallel computing. Most of the methods implemented in this package can be found in Brooks et al. (2011, ISBN 9781420079425). Among the methods included, we have: Haario (2001) <doi:10.1007/s11222-011-9269-5> Adaptive Metropolis, Vihola (2012) <doi:10.1007/s11222-011-9269-5> Robust Adaptive Metropolis, and Thawornwattana et al. (2018) <doi:10.1214/17-BA1084> Mirror transition kernels.

This package provides a population genetic simulator, which is able to generate synthetic datasets for single-nucleotide polymorphisms (SNP) for multiple populations. The genetic distances among populations can be set according to the Fixation Index (Fst) as explained in Balding and Nichols (1995) <doi:10.1007/BF01441146>. This tool is able to simulate outlying individuals and missing SNPs can be specified. For Genome-wide association study (GWAS), disease status can be set in desired level according risk ratio.

An efficient algorithm to fit and tune kernel quantile regression models based on the majorization-minimization (MM) method. It can also fit multiple quantile curves simultaneously without crossing.

Likelihood-free inference method for stochastic models. Uses a deterministic optimizer on simple simulations of the model that are performed with a prior drawn randomness by applying the inverse transform method. Is designed to work on its own and also by using the Julia package Jflimo available on the git page of the project: <https://metabarcoding.org/flimo>.

Perform factorial analysis with a menu and draw graphs interactively thanks to FactoMineR and a Shiny application.

Allows ATA (Automatic Time series analysis using the Ata method) models from the ATAforecasting package to be used in a tidy workflow with the modeling interface of fabletools'. This extends ATAforecasting to provide enhanced model specification and management, performance evaluation methods, and model combination tools. The Ata method (Yapar et al. (2019) <doi:10.15672/hujms.461032>), an alternative to exponential smoothing (described in Yapar (2016) <doi:10.15672/HJMS.201614320580>, Yapar et al. (2017) <doi:10.15672/HJMS.2017.493>), is a new univariate time series forecasting method which provides innovative solutions to issues faced during the initialization and optimization stages of existing forecasting methods. Forecasting performance of the Ata method is superior to existing methods both in terms of easy implementation and accurate forecasting. It can be applied to non-seasonal or seasonal time series which can be decomposed into four components (remainder, level, trend and seasonal).

Read and process a large delimited file block by block. A block consists of all the contiguous rows that have the same value in the first field. The result can be returned as a list or a data.table, or even directly printed to an output file.

Used for the design and analysis of a 2x2 factorial trial for a time-to-event endpoint. It performs power calculations and significance testing as well as providing estimates of the relevant hazard ratios and the corresponding 95% confidence intervals. Important reference papers include Slud EV. (1994) <https://www.ncbi.nlm.nih.gov/pubmed/8086609> Lin DY, Gong J, Gallo P, Bunn PH, Couper D. (2016) <DOI:10.1111/biom.12507> Leifer ES, Troendle JF, Kolecki A, Follmann DA. (2020) <https://github.com/EricSLeifer/factorial2x2/blob/master/Leifer%20et%20al.%20paper.pdf>.

An implementation of various learning algorithms based on fuzzy rule-based systems (FRBSs) for dealing with classification and regression tasks. Moreover, it allows to construct an FRBS model defined by human experts. FRBSs are based on the concept of fuzzy sets, proposed by Zadeh in 1965, which aims at representing the reasoning of human experts in a set of IF-THEN rules, to handle real-life problems in, e.g., control, prediction and inference, data mining, bioinformatics data processing, and robotics. FRBSs are also known as fuzzy inference systems and fuzzy models. During the modeling of an FRBS, there are two important steps that need to be conducted: structure identification and parameter estimation. Nowadays, there exists a wide variety of algorithms to generate fuzzy IF-THEN rules automatically from numerical data, covering both steps. Approaches that have been used in the past are, e.g., heuristic procedures, neuro-fuzzy techniques, clustering methods, genetic algorithms, squares methods, etc. Furthermore, in this version we provide a universal framework named frbsPMML', which is adopted from the Predictive Model Markup Language (PMML), for representing FRBS models. PMML is an XML-based language to provide a standard for describing models produced by data mining and machine learning algorithms. Therefore, we are allowed to export and import an FRBS model to/from frbsPMML'. Finally, this package aims to implement the most widely used standard procedures, thus offering a standard package for FRBS modeling to the R community.

Many Fitbit users, and R-friendly Fitbit users especially, have found themselves curious about their Fitbit data. Fitbit aggregates a large amount of personal data, much of which is interesting for personal research and to satisfy curiosity, and is even potentially useful in medical settings. The goal of fitbitr is to make interfacing with the Fitbit API as streamlined as possible, to make it simple for R users of all backgrounds and comfort levels to analyze their Fitbit data and do whatever they want with it! Currently, fitbitr includes methods for pulling data on activity, sleep, and heart rate, but this list is likely to grow in the future as the package gains more traction and more requests for new methods to be implemented come in. You can find details on the Fitbit API at <https://dev.fitbit.com/build/reference/web-api/>.

This package provides very fast logistic regression with coefficient inferences plus other useful methods such as a forward stepwise model generator (see the benchmarks by visiting the github page at the URL below). The inputs are flexible enough to accomodate GPU computations. The coefficient estimation employs the fastLR() method in the RcppNumerical package by Yixuan Qiu et al. This package allows their work to be more useful to a wider community that consumes inference.

An implementation of revised functional regression models for multiple genetic variation data, such as single nucleotide polymorphism (SNP) data, which provides revised functional linear regression models, partially functional interaction regression analysis with penalty-based techniques and corresponding drawing functions, etc.(Ruzong Fan, Yifan Wang, James L. Mills, Alexander F. Wilson, Joan E. Bailey-Wilson, and Momiao Xiong (2013) <doi:10.1002/gepi.21757>).

Perform frequency distribution tables, associated histograms and polygons from vector, data.frame and matrix objects for numerical and categorical variables.

Statistical methods and simulation tools for the interpretation of forensic DNA mixtures. The methods implemented are described in Haned et al. (2011) <doi:10.1111/j.1556-4029.2010.01550.x>, Haned et al. (2012) <doi:10.1016/j.fsigen.2012.11.002> and Gill & Haned (2013) <doi:10.1016/j.fsigen.2012.08.008>.

Enables the construction of flexible urban delineations that can be tailored to specific applications or research questions, see Van Migerode et al. (2024) <DOI:10.1177/23998083241262545> and Van Migerode et al. (2025) <DOI:10.5281/zenodo.15173220>. Originally developed to flexibly reconstruct the Degree of Urbanisation classification of cities, towns and rural areas developed by Dijkstra et al. (2021) <DOI:10.1016/j.jue.2020.103312>. Now it also support a broader range of delineation approaches, using multiple datasets â including population, built-up area, and night-time light grids â and different thresholding methods.

Create fake datasets that can be used for prototyping and teaching. This package provides a set of functions to generate fake data for a variety of data types, such as dates, addresses, and names. It can be used for prototyping (notably in shiny') or as a tool to teach data manipulation and data visualization.

Social Relations Analysis with roles ("Family SRM") are computed, using a structural equation modeling approach. Groups ranging from three members up to an unlimited number of members are supported and the mean structure can be computed. Means and variances can be compared between different groups of families and between roles.