Allows maximum likelihood fitting of cluster-weighted models, a class of mixtures of regression models with random covariates. Methods are described in Angelo Mazza, Antonio Punzo, Salvatore Ingrassia (2018) <doi:10.18637/jss.v086.i02>.

Similar to base's unique function, only optimized for working with data frames, especially those that contain date-time columns.

Computes and plots prediction intervals for numerical data or prediction sets for categorical data using prior information. Empirical Bayes procedures to estimate the prior information from multi-group data are included. See, e.g.,Bersson and Hoff (2022) <arXiv:2204.08122> "Optimal Conformal Prediction for Small Areas".

Estimation of functional spaces based on traits of organisms. The package includes functions to impute missing trait values (with or without considering phylogenetic information), and to create, represent and analyse two dimensional functional spaces based on principal components analysis, other ordination methods, or raw traits. It also allows for mapping a third variable onto the functional space. See Carmona et al. (2021) <doi:10.1038/s41586-021-03871-y>, Puglielli et al. (2021) <doi:10.1111/nph.16952>, Carmona et al. (2021) <doi:10.1126/sciadv.abf2675>, Carmona et al. (2019) <doi:10.1002/ecy.2876> for more information.

The main goal of this package is drawing the membership function of the fuzzy p-value which is defined as a fuzzy set on the unit interval for three following problems: (1) testing crisp hypotheses based on fuzzy data, (2) testing fuzzy hypotheses based on crisp data, and (3) testing fuzzy hypotheses based on fuzzy data. In all cases, the fuzziness of data or/and the fuzziness of the boundary of null fuzzy hypothesis transported via the p-value function and causes to produce the fuzzy p-value. If the p-value is fuzzy, it is more appropriate to consider a fuzzy significance level for the problem. Therefore, the comparison of the fuzzy p-value and the fuzzy significance level is evaluated by a fuzzy ranking method in this package.

Compute inbreeding coefficients using the method of Meuwissen and Luo (1992) <doi:10.1186/1297-9686-24-4-305>, and numerator relationship coefficients between individuals using the method of Van Vleck (2007) <https://pubmed.ncbi.nlm.nih.gov/18050089/>.

Estimation of mixed models including a subject-specific variance which can be time and covariate dependent. In the joint model framework, the package handles left truncation and allows a flexible dependence structure between the competing events and the longitudinal marker. The estimation is performed under the frequentist framework, using the Marquardt-Levenberg algorithm. (Courcoul, Tzourio, Woodward, Barbieri, Jacqmin-Gadda (2023) <arXiv:2306.16785>).

Feature Ordering by Integrated R square Dependence (FORD) is a variable selection algorithm based on the new measure of dependence: Integrated R2 Dependence Coefficient (IRDC). For more information, see the paper: Azadkia and Roudaki (2025),"A New Measure Of Dependence: Integrated R2" <doi:10.48550/arXiv.2505.18146>.

Fast and flexible Kalman filtering and smoothing implementation utilizing sequential processing, designed for efficient parameter estimation through maximum likelihood estimation. Sequential processing is a univariate treatment of a multivariate series of observations and can benefit from computational efficiency over traditional Kalman filtering when independence is assumed in the variance of the disturbances of the measurement equation. Sequential processing is described in the textbook of Durbin and Koopman (2001, ISBN:978-0-19-964117-8). FKF.SP was built upon the existing FKF package and is, in general, a faster Kalman filter/smoother.

This package provides tools to quickly compile taxonomic and distribution data from the Brazilian Flora 2020.

This package provides a game for two players: Who gets first four in a row (horizontal, vertical or diagonal) wins. As board game published by Milton Bradley, designed by Howard Wexler and Ned Strongin.

Offers tools for visualizing and analyzing size and power properties of tests for equal predictive accuracy, including Diebold-Mariano and related procedures. Provides multiple Diebold-Mariano test implementations based on fixed-smoothing approaches, including fixed-b methods such as Kiefer and Vogelsang (2005) <doi:10.1017/S0266466605050565>, and applications to tests for equal predictive accuracy as in Coroneo and Iacone (2020) <doi:10.1002/jae.2756>, alongside conventional large-sample approximations. HAR inference involves nonparametric estimation of the long-run variance, and a key tuning parameter (the truncation parameter) trades off size and power. Lazarus, Lewis, and Stock (2021) <doi:10.3982/ECTA15404> theoretically characterize the size-power frontier for the Gaussian multivariate location model. ForeComp computes and visualizes the finite-sample size-power frontier of the Diebold-Mariano test based on fixed-b asymptotics together with the Bartlett kernel. To compute finite-sample size and power, it fits a best approximating ARMA process to the input data and reports how the truncation parameter performs and how robust testing outcomes are to its choice.

Collect your data on digital marketing campaigns from Salesforce using the Windsor.ai API <https://windsor.ai/api-fields/>.

Reads and writes ARFF files. ARFF (Attribute-Relation File Format) files are like CSV files, with a little bit of added meta information in a header and standardized NA values. They are quite often used for machine learning data sets and were introduced for the WEKA machine learning Java toolbox. See <https://waikato.github.io/weka-wiki/formats_and_processing/arff_stable/> for further info on ARFF and for <http://www.cs.waikato.ac.nz/ml/weka/> for more info on WEKA'. farff gets rid of the Java dependency that RWeka enforces, and it is at least a faster reader (for bigger files). It uses readr as parser back-end for the data section of the ARFF file. Consistency with RWeka is tested on Github and Travis CI with hundreds of ARFF files from OpenML'.

Compares how well different models estimate a quantity of interest (the "focus") so that different models may be preferred for different purposes. Comparisons within any class of models fitted by maximum likelihood are supported, with shortcuts for commonly-used classes such as generalised linear models and parametric survival models. The methods originate from Claeskens and Hjort (2003) <doi:10.1198/016214503000000819> and Claeskens and Hjort (2008, ISBN:9780521852258).

Computes different multidimensional FD indices. Implements a distance-based framework to measure FD that allows any number and type of functional traits, and can also consider species relative abundances. Also contains other useful tools for functional ecology.

An R client for the freecurrencyapi.com currency conversion API. The API requires registration of an API key. You can find the full API documentation at <https://freecurrencyapi.com/docs> .

This package implements the statistic FAVA, an Fst-based Assessment of Variability across vectors of relative Abundances, as well as a suite of helper functions which enable the visualization and statistical analysis of relative abundance data. The FAVA R package accompanies the paper, â Quantifying compositional variability in microbial communities with FAVAâ by Morrison, Xue, and Rosenberg (2025) <doi:10.1073/pnas.2413211122>.

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.

Fits the lifespan datasets of biological systems such as yeast, fruit flies, and other similar biological units with well-known finite mixture models introduced by Farewell et al. (1982) <doi:10.2307/2529885> and Al-Hussaini et al. (2000) <doi:10.1080/00949650008812033>. Estimates parameter space fitting of a lifespan dataset with finite mixtures of parametric distributions. Computes the following tasks; 1) Estimates parameter space of the finite mixture model by implementing the expectation maximization (EM) algorithm. 2) Finds a sequence of four goodness-of-fit measures consist of Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Kolmogorov-Smirnov (KS), and log-likelihood (log-likelihood) statistics. 3)The initial values is determined by k-means clustering.

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.

Compare variables of interest between (potentially large numbers of) spatial interactions and meta-variables. Spatial variables are summarized using K, or other, functions, and projected for use in a modified random forest model. The model allows comparison of functional and non-functional variables to each other and to noise, giving statistical significance to the results. Included are preparation, modeling, and interpreting tools along with example datasets, as described in VanderDoes et al., (2023) <doi:10.1101/2023.07.18.549619>.

Implementation of color palettes based on fish species.

Regular and non-regular Fractional Factorial 2-level designs can be created. Furthermore, analysis tools for Fractional Factorial designs with 2-level factors are offered (main effects and interaction plots for all factors simultaneously, cube plot for looking at the simultaneous effects of three factors, full or half normal plot, alias structure in a more readable format than with the built-in function alias).