This package performs backward elimination with similar syntax to the stepAIC() function from the MASS package. A bounding algorithm is used to avoid fitting unnecessary models, making it much faster.

Estimate parameters of univariate probability distributions with maximum likelihood and Bayesian methods.

SHE, FORAM Index and ABC Method analyses and custom plot functions for community data.

Create a flip over style Flash Card with desired data frame for Shiny application.

Implementation of the Interval Testing Procedure for functional data in different frameworks (i.e., one or two-population frameworks, functional linear models) by means of different basis expansions (i.e., B-spline, Fourier, and phase-amplitude Fourier). The current version of the package requires functional data evaluated on a uniform grid; it automatically projects each function on a chosen functional basis; it performs the entire family of multivariate tests; and, finally, it provides the matrix of the p-values of the previous tests and the vector of the corrected p-values. The functional basis, the coupled or uncoupled scenario, and the kind of test can be chosen by the user. The package provides also a plotting function creating a graphical output of the procedure: the p-value heat-map, the plot of the corrected p-values, and the plot of the functional data.

The fftab package stores Fourier coefficients in a tibble and allows their manipulation in various ways. Functions are available for converting between complex, rectangular ('re', im'), and polar ('mod', arg') representations, as well as for extracting components as vectors or matrices. Inputs can include vectors, time series, and arrays of arbitrary dimensions, which are restored to their original form when inverting the transform. Since fftab stores Fourier frequencies as columns in the tibble, many standard operations on spectral data can be easily performed using tidy packages like dplyr'.

The purpose of this package is to tests whether a given moment of the distribution of a given sample is finite or not. For heavy-tailed distributions with tail exponent b, only moments of order smaller than b are finite. Tail exponent and heavy- tailedness are notoriously difficult to ascertain. But the finiteness of moments (including fractional moments) can be tested directly. This package does that following the test suggested by Trapani (2016) <doi:10.1016/j.jeconom.2015.08.006>.

This package provides a collection of functions for testing various aspects of univariate time series including independence and neglected nonlinearities. Further provides functions to investigate the chaotic behavior of time series processes and to simulate different types of chaotic time series maps.

Fit occupancy models in Stan via brms'. The full variety of brms formula-based effects structures are available to use in multiple classes of occupancy model, including single-season models, models with data augmentation for never-observed species, dynamic (multiseason) models with explicit colonization and extinction processes, and dynamic models with autologistic occupancy dynamics. Formulas can be specified for all relevant distributional terms, including detection and one or more of occupancy, colonization, extinction, and autologistic depending on the model type. Several important forms of model post-processing are provided. References: Bürkner (2017) <doi:10.18637/jss.v080.i01>; Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>; Socolar & Mills (2023) <doi:10.1101/2023.10.26.564080>.

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>.

The algorithm assigns rareness/ outlierness score to every sample in voluminous datasets. The algorithm makes multiple estimations of the proximity between a pair of samples, in low-dimensional spaces. To compute proximity, FiRE uses Sketching, a variant of locality sensitive hashing. For more details: Jindal, A., Gupta, P., Jayadeva and Sengupta, D., 2018. Discovery of rare cells from voluminous single cell expression data. Nature Communications, 9(1), p.4719. <doi:10.1038/s41467-018-07234-6>.

This package provides a C++ API for routinely used numerical tools such as integration, root-finding, and optimization, where function arguments are given as lambdas. This facilitates Rcpp programming, enabling the development of R'-like code in C++ where functions can be defined on the fly and use variables in the surrounding environment.

This package implements fast and exact computation of Gaussian stochastic process with the Matern kernel using forward filtering and backward smoothing algorithm. It includes efficient implementations of the inverse Kalman filter, with applications such as estimating particle interaction functions. These tools support models with or without noise. Additionally, the package offers algorithms for fast parameter estimation in latent factor models, where the factor loading matrix is orthogonal, and latent processes are modeled by Gaussian processes. See the references: 1) Mengyang Gu and Yanxun Xu (2020), Journal of Computational and Graphical Statistics; 2) Xinyi Fang and Mengyang Gu (2024), <doi:10.48550/arXiv.2407.10089>; 3) Mengyang Gu and Weining Shen (2020), Journal of Machine Learning Research; 4) Yizi Lin, Xubo Liu, Paul Segall and Mengyang Gu (2025), <doi:10.48550/arXiv.2501.01324>.

This package provides a convenient and user-friendly interface to interact with the Firebase Authentication REST API': <https://firebase.google.com/docs/reference/rest/auth>. It enables R developers to integrate Firebase Authentication services seamlessly into their projects, allowing for user authentication, account management, and other authentication-related tasks.

Use spectrophotometry measurements performed on insects as a way to infer pathogens virulence. Insect movements cause fluctuations in fluorescence signal, and functions are provided to estimate when the insect has died as the moment when variance in autofluorescence signal drops to zero. The package provides functions to obtain this estimate together with functions to import spectrophotometry data from a Biotek microplate reader. Details of the method are given in Parthuisot et al. (2018) <doi:10.1101/297929>.

This is an extremely fast implementation of a Naive Bayes classifier. This package is currently the only package that supports a Bernoulli distribution, a Multinomial distribution, and a Gaussian distribution, making it suitable for both binary features, frequency counts, and numerical features. Another feature is the support of a mix of different event models. Only numerical variables are allowed, however, categorical variables can be transformed into dummies and used with the Bernoulli distribution. The implementation is largely based on the paper "A comparison of event models for Naive Bayes anti-spam e-mail filtering" written by K.M. Schneider (2003) <doi:10.3115/1067807.1067848>. Any issues can be submitted to: <https://github.com/mskogholt/fastNaiveBayes/issues>.

Quantify variability (such as confidence interval) of fertilizer response curves and optimum fertilizer rates using bootstrapping residuals with several popular non-linear and linear models.

This package provides a guarded resampling workflow for training and evaluating machineâ learning models. When the guarded resampling path is used, preprocessing and model fitting are reâ estimated within each resampling split to reduce leakage risk. Supports multiple resampling schemes, integrates with established engines in the tidymodels ecosystem, and aims to improve evaluation reliability by coordinating preprocessing, fitting, and evaluation within supported workflows. Offers a lightweight AutoMLâ style workflow by automating model training, resampling, and tuning across multiple algorithms, while keeping evaluation design explicit and userâ controlled.

The fxl Charting package is used to prepare and design single case design figures that are typically prepared in spreadsheet software. With fxl', there is no need to leave the R environment to prepare these works and many of the more unique conventions in single case experimental designs can be performed without the need for physically constructing features of plots (e.g., drawing annotations across plots). Support is provided for various different plotting arrangements (e.g., multiple baseline), annotations (e.g., brackets, arrows), and output formats (e.g., svg, rasters).

Fuzzy inference systems are based on fuzzy rules, which have a good capability for managing progressive phenomenons. This package is a basic implementation of the main functions to use a Fuzzy Inference System (FIS) provided by the open source software FisPro <https://www.fispro.org>. FisPro allows to create fuzzy inference systems and to use them for reasoning purposes, especially for simulating a physical or biological system.

Pairwise Hamming distances are computed between the rows of a binary (0/1) matrix using highly optimized C code. The input is an integer matrix where each row represents a binary feature vector and returns a symmetric integer matrix of pairwise distances. Internally, rows are bit-packed into 64-bit words for fast XOR-based comparisons, with hardware-accelerated popcount operations to count differences. OpenMP parallelization ensures efficient performance for large matrices.

This package provides functions that calculates common types of splitting criteria used in random forests for classification problems, as well as functions that make predictions based on a single tree or a Forest-R.K. model; the package also provides functions to generate importance plot for a Forest-R.K. model, as well as the 2D multidimensional-scaling plot of data points that are colour coded by their predicted class types by the Forest-R.K. model. This package is based on: Bernard, S., Heutte, L., Adam, S., (2008, ISBN:978-3-540-85983-3) "Forest-R.K.: A New Random Forest Induction Method", Fourth International Conference on Intelligent Computing, September 2008, Shanghai, China, pp.430-437.

This package performs functional regression, and some related approaches, for intensive longitudinal data (see the book by Walls & Schafer, 2006, Models for Intensive Longitudinal Data, Oxford) when such data is not necessarily observed on an equally spaced grid of times. The approach generally follows the ideas of Goldsmith, Bobb, Crainiceanu, Caffo, and Reich (2011)<DOI:10.1198/jcgs.2010.10007> and the approach taken in their sample code, but with some modifications to make it more feasible to use with long rather than wide, non-rectangular longitudinal datasets with unequal and potentially random measurement times. It also allows easy plotting of the correlation between the smoothed covariate and the outcome as a function of time, which can add additional insights on how to interpret a functional regression. Additionally, it also provides several permutation tests for the significance of the functional predictor. The heuristic interpretation of ``time is used to describe the index of the functional predictor, but the same methods can equally be used for another unidimensional continuous index, such as space along a north-south axis. Note that most of the functionality of this package has been superseded by added features after 2016 in the pfr function by Jonathan Gellar, Mathew W. McLean, Jeff Goldsmith, and Fabian Scheipl, in the refund package built by Jeff Goldsmith and co-authors and maintained by Julia Wrobel. The development of the funreg package in 2015 and 2016 was part of a research project supported by Award R03 CA171809-01 from the National Cancer Institute and Award P50 DA010075 from the National Institute on Drug Abuse. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute on Drug Abuse, the National Cancer Institute, or the National Institutes of Health.

An easy framework to read FDA Adverse Event Reporting System XML/ASCII files <https://www.fda.gov/drugs/questions-and-answers-fdas-adverse-event-reporting-system-faers/fda-adverse-event-reporting-system-faers-latest-quarterly-data-files>.