Simplifies the creation and customization of forest plots (alternatively called dot-and-whisker plots). Input classes accepted by forplo are data.frame, matrix, lm, glm, and coxph. forplo was written in base R and does not depend on other packages.

Probabilistic distance clustering (PD-clustering) is an iterative, distribution-free, probabilistic clustering method. PD-clustering assigns units to a cluster according to their probability of membership under the constraint that the product of the probability and the distance of each point to any cluster center is a constant. PD-clustering is a flexible method that can be used with elliptical clusters, outliers, or noisy data. PDQ is an extension of the algorithm for clusters of different sizes. GPDC and TPDC use a dissimilarity measure based on densities. Factor PD-clustering (FPDC) is a factor clustering method that involves a linear transformation of variables and a cluster optimizing the PD-clustering criterion. It works on high-dimensional data sets.

Quantify the serial correlation across lags of a given functional time series using the autocorrelation function and a partial autocorrelation function for functional time series proposed in Mestre et al. (2021) <doi:10.1016/j.csda.2020.107108>. The autocorrelation functions are based on the L2 norm of the lagged covariance operators of the series. Functions are available for estimating the distribution of the autocorrelation functions under the assumption of strong functional white noise.

This package provides tools for training and analysing fairness-aware gated neural networks for subgroup-aware prediction and interpretation in clinical datasets. Methods draw on prior work in mixture-of-experts neural networks by Jordan and Jacobs (1994) <doi:10.1007/978-1-4471-2097-1_113>, fairness-aware learning by Hardt, Price, and Srebro (2016) <doi:10.48550/arXiv.1610.02413>, and personalised treatment prediction for depression by Iniesta, Stahl, and McGuffin (2016) <doi:10.1016/j.jpsychires.2016.03.016>.

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 dynamic programming algorithm for the fast segmentation of univariate signals into piecewise constant profiles. The fpop package is a wrapper to a C++ implementation of the fpop (Functional Pruning Optimal Partioning) algorithm described in Maidstone et al. 2017 <doi:10.1007/s11222-016-9636-3>. The problem of detecting changepoints in an univariate sequence is formulated in terms of minimising the mean squared error over segmentations. The fpop algorithm exactly minimizes the mean squared error for a penalty linear in the number of changepoints.

Support the extraction and seamless integration of species ecological traits or preferences from the www.freshwaterecology.info into several ecological model workflows. During data extraction, different taxonomic levels are acceptable, including species, genus, and family, based on the availability of data in the database. The data is cached after the first search and can be accessed during and after online interactions. Only scientific names are acceptable in the search; local or English names are not allowed. A user API key is required to start using the package.

Robust analysis using forward search in linear and generalized linear regression models, as described in Atkinson, A.C. and Riani, M. (2000), Robust Diagnostic Regression Analysis, First Edition. New York: Springer.

The fastai <https://docs.fast.ai/index.html> library simplifies training fast and accurate neural networks using modern best practices. It is based on research in to deep learning best practices undertaken at fast.ai', including out of the box support for vision, text, tabular, audio, time series, and collaborative filtering models.

Test function arguments with a wide array of inputs, and produce reports summarizing messages, warnings, errors, and returned values.

This package provides core computational operations in C++ via RcppArmadillo', enabling faster performance than pure R, improved numerical stability, and parallel execution with OpenMP where available. On systems without OpenMP support, the package automatically falls back to single-threaded execution with no user configuration required. For efficient model selection, it integrates with CVST to provide sequential-testing cross-validation that identifies competitive hyperparameters without exhaustive grid search. The package offers a unified interface for exact kernel ridge regression and three scalable approximationsâ Nyström, Pivoted Cholesky, and Random Fourier Featuresâ allowing analyses with substantially larger sample sizes than are feasible with exact KRR. It also integrates with the tidymodels ecosystem via the parsnip model specification krr_reg', and the S3 method tunable.krr_reg(). To understand the theoretical background, one can refer to Wainwright (2019) <doi:10.1017/9781108627771>.

This package provides a comprehensive framework in R for modeling and forecasting economic scenarios based on multi-level dynamic factor model. The package enables users to: (i) extract global and group-specific factors using a flexible multi-level factor structure; (ii) compute asymptotically valid confidence regions for the estimated factors, accounting for uncertainty in the factor loadings; (iii) obtain estimates of the parameters of the factor-augmented quantile regressions together with their standard deviations; (iv) recover full predictive conditional densities from estimated quantiles; (v) obtain risk measures based on extreme quantiles of the conditional densities; (vi) estimate the conditional density and the corresponding extreme quantiles when the factors are stressed.

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.

Easily create graphs of the inter-relationships between functions in an environment.

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>.

Fit (generalized) linear regression models in each leaf node of a tree. The tree is constructed using clinical variables only. The linear regression models are constructed using (high-dimensional) omics variables only. The leaf-node-specific regression models are estimated using the penalized likelihood including a standard ridge (L2) penalty and a fusion penalty that links the leaf-node-specific regression models to one another. The intercepts of the leaf nodes reflect the effects of the clinical variables and are left unpenalized. The tree, fitted with the clinical variables only, should be constructed outside of the package with the rpart R package. See Goedhart and others (2024) <doi:10.48550/arXiv.2411.02396> for details on the method.

This package performs dose assignment and trial simulation for the FBCRM (Fully Bayesian Continual Reassessment Method) and MFBCRM (Mixture Fully Bayesian Continual Reassessment Method) phase I clinical trial designs. These trial designs extend the Continual Reassessment Method (CRM) and Bayesian Model Averaging Continual Reassessment Method (BMA-CRM) by allowing the prior toxicity skeleton itself to be random, with posterior distributions obtained from Markov Chain Monte Carlo. On average, the FBCRM and MFBCRM methods outperformed the CRM and BMA-CRM methods in terms of selecting an optimal dose level across thousands of randomly generated simulation scenarios. Details on the methods and results of this simulation study are available on request, and the manuscript is currently under review.

Plotting flood quantiles and their corresponding probabilities (return periods) on the probability papers. The details of relevant methods are available in Chow et al (1988, ISBN: 007070242X, 9780070702424), and Bobee and Ashkar (1991, ISBN: 0918334683, 9780918334688).

The complete scripts from the American sitcom Friends in tibble format. Use this package to practice data wrangling, text analysis and network analysis.

Fuzzy string matching implementation of the fuzzywuzzy <https://github.com/seatgeek/fuzzywuzzy> python package. It uses the Levenshtein Distance <https://en.wikipedia.org/wiki/Levenshtein_distance> to calculate the differences between sequences.

Fast estimation algorithms to implement the Quantile Regression with Selection estimator and the multiplicative Bootstrap for inference. This estimator can be used to estimate models that feature sample selection and heterogeneous effects in cross-sectional data. For more details, see Arellano and Bonhomme (2017) <doi:10.3982/ECTA14030> and Pereda-Fernández (2024) <doi:10.48550/arXiv.2402.16693>.

This package provides a set of analytical tools useful in analysing ecological and geographical data sets, both ancient and modern. The package includes functions for estimating species richness (Chao 1 and 2, ACE, ICE, Jacknife), shared species/beta diversity, species area curves and geographic distances and areas.

For cleaning and analysis of graphs, such as animal closing force measurements. forceR was initially written and optimized to deal with insect bite force measurements, but can be used for any time series. Includes a full workflow to load, plot and crop data, correct amplifier and baseline drifts, identify individual peak shapes (bites), rescale (normalize) peak curves, and find best polynomial fits to describe and analyze force curve shapes.

This package creates a HTML widget which displays the results of searching for a pattern in files in a given folder. The results can be viewed in the RStudio viewer pane, included in a R Markdown document or in a Shiny application. Also provides a Shiny application allowing to run this widget and to navigate in the files found by the search. Instead of creating a HTML widget, it is also possible to get the results of the search in a tibble'. The search is performed by the grep command-line utility.