This package provides tools for describing and analysing free sorting data. Main methods are computation of consensus partition and factorial analysis of the dissimilarity matrix between stimuli (using multidimensional scaling approach).

Create local, regional, and global explanations for any machine learning model with forward marginal effects. You provide a model and data, and fmeffects computes feature effects. The package is based on the theory in: C. A. Scholbeck, G. Casalicchio, C. Molnar, B. Bischl, and C. Heumann (2022) <doi:10.48550/arXiv.2201.08837>.

This package provides a collection of functions for outlier detection in functional data analysis. Methods implemented include directional outlyingness by Dai and Genton (2019) <doi:10.1016/j.csda.2018.03.017>, MS-plot by Dai and Genton (2018) <doi:10.1080/10618600.2018.1473781>, total variation depth and modified shape similarity index by Huang and Sun (2019) <doi:10.1080/00401706.2019.1574241>, and sequential transformations by Dai et al. (2020) <doi:10.1016/j.csda.2020.106960 among others. Additional outlier detection tools and depths for functional data like functional boxplot, (modified) band depth etc., are also available.

Implement and fit a variety of short-memory (SM) and long-memory (LM) models from a very broad family of exponential generalized autoregressive conditional heteroskedasticity (EGARCH) models, such as a MEGARCH (modified EGARCH), FIEGARCH (fractionally integrated EGARCH), FIMLog-GARCH (fractionally integrated modulus Log-GARCH), and more. The FIMLog-GARCH as part of the EGARCH family is discussed in Feng et al. (2023) <https://econpapers.repec.org/paper/pdnciepap/156.htm>. For convenience and the purpose of comparison, a variety of other popular SM and LM GARCH-type models, like an APARCH model, a fractionally integrated APARCH (FIAPARCH) model, standard GARCH and fractionally integrated GARCH (FIGARCH) models, GJR-GARCH and FIGJR-GARCH models, TGARCH and FITGARCH models, are implemented as well as dual models with simultaneous modelling of the mean, including dual long-memory models with a fractionally integrated autoregressive moving average (FARIMA) model in the mean and a long-memory model in the variance, and semiparametric volatility model extensions. Parametric models and parametric model parts are fitted through quasi-maximum-likelihood estimation. Furthermore, common forecasting and backtesting functions for value-at-risk (VaR) and expected shortfall (ES) based on the package's models are provided.

This package implements the Factor-Augmented Clustering Tree (FACT) algorithm for clustering time series data. The method constructs a classification tree where splits are determined by covariates, and the splitting criterion is based on a group factor model representation of the time series within each node. Both threshold-based and permutation-based tests are supported for splitting decisions, with an option for parallel computation. For methodological details, see Hu, Li, Luo, and Wang (2025, in preparation), Factor-Augmented Clustering Tree for Time Series.

Package for parametric relative survival analyses. It allows to model non-linear and non-proportional effects and both non proportional and non linear effects, using splines (B-spline and truncated power basis), Weighted Cumulative Index of Exposure effect, with correction model for the life table. Both non proportional and non linear effects are described in Remontet, L. et al. (2007) <doi:10.1002/sim.2656> and Mahboubi, A. et al. (2011) <doi:10.1002/sim.4208>.

Random simulations of fuzzy numbers are still a challenging problem. The aim of this package is to provide the respective procedures to simulate fuzzy random variables, especially in the case of the piecewise linear fuzzy numbers (PLFNs, see Coroianua et al. (2013) <doi:10.1016/j.fss.2013.02.005> for the further details). Additionally, the special resampling algorithms known as the epistemic bootstrap are provided (see Grzegorzewski and Romaniuk (2022) <doi:10.34768/amcs-2022-0021>, Grzegorzewski and Romaniuk (2022) <doi:10.1007/978-3-031-08974-9_39>, Romaniuk et al. (2024) <doi:10.32614/RJ-2024-016>) together with the functions to apply statistical tests and estimate various characteristics based on the epistemic bootstrap. The package also includes real-life datasets of epistemic fuzzy triangular and trapezoidal numbers. The fuzzy numbers used in this package are consistent with the FuzzyNumbers package.

Functions, S4 classes/methods and a graphical user interface (GUI) to design surveys to substantiate freedom from disease using a modified hypergeometric function (see Cameron and Baldock, 1997, <doi:10.1016/s0167-5877(97)00081-0>). Herd sensitivities are computed according to sampling strategies "individual sampling" or "limited sampling" (see M. Ziller, T. Selhorst, J. Teuffert, M. Kramer and H. Schlueter, 2002, <doi:10.1016/S0167-5877(01)00245-8>). Methods to compute the a-posteriori alpha-error are implemented. Risk-based targeted sampling is supported.

Optimal experimental designs for functional linear and functional generalised linear models, for scalar responses and profile/dynamic factors. The designs are optimised using the coordinate exchange algorithm. The methods are discussed by Michaelides (2023) <https://eprints.soton.ac.uk/474982/1/Thesis_DamianosMichaelides_Final_pdfa_1_.pdf>.

Processing of large-in-memory/large-on disk rasters and spatial vectors using GRASS <https://grass.osgeo.org/>. Most functions in the terra package are recreated. Processing of medium-sized and smaller spatial objects will nearly always be faster using terra or sf', but for large-in-memory/large-on-disk objects, fasterRaster may be faster. To use most of the functions, you must have the stand-alone version (not the OSGeoW4 installer version) of GRASS 8.0 or higher.

Designing experimental plans that involve both discrete and continuous factors with general parametric statistical models using the ForLion algorithm and EW ForLion algorithm. The algorithms will search for locally optimal designs and EW optimal designs under the D-criterion. Reference: Huang, Y., Li, K., Mandal, A., & Yang, J., (2024)<doi:10.1007/s11222-024-10465-x>.

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>.

Exports flextable objects to xlsx files, utilizing functionalities provided by flextable and openxlsx2'.

Work with configs with a source precedence. Either create own R6 instance or work with convenient functions at a package level.

An implementation of regression models with partial differential regularizations, making use of the Finite Element Method. The models efficiently handle data distributed over irregularly shaped domains and can comply with various conditions at the boundaries of the domain. A priori information about the spatial structure of the phenomenon under study can be incorporated in the model via the differential regularization. See Sangalli, L. M. (2021) <doi:10.1111/insr.12444> "Spatial Regression With Partial Differential Equation Regularisation" for an overview. The release 1.1-9 requires R (>= 4.2.0) to be installed on windows machines.

To help you access, transform, analyze, and visualize ForestGEO data, we developed a collection of R packages (<https://forestgeo.github.io/fgeo/>). This package, in particular, helps you to install and load the entire package-collection with a single R command, and provides convenient ways to find relevant documentation. Most commonly, you should not worry about the individual packages that make up the package-collection as you can access all features via this package. To learn more about ForestGEO visit <http://www.forestgeo.si.edu/>.

Create and visualize fractal trees and fractal forests, based on the Lindenmayer system (L-system). For more details see Lindenmayer (1968a) <doi:10.1016/0022-5193(68)90079-9> and Lindenmayer (1968b) <doi:10.1016/0022-5193(68)90080-5>.

Estimates Filtered Monotonic Polynomial IRT Models as described by Liang and Browne (2015) <DOI:10.3102/1076998614556816>.

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/>.

Log-ratio Lasso regression for continuous, binary, and survival outcomes with (longitudinal) compositional features. See Fei and others (2024) <doi:10.1016/j.crmeth.2024.100899>.

This package provides a handy tool to calculate carbon footprints from air travel based on three-letter International Air Transport Association (IATA) airport codes or latitude and longitude. footprint first calculates the great-circle distance between departure and arrival destinations. It then uses the Department of Environment, Food & Rural Affairs (DEFRA) greenhouse gas conversion factors for business air travel to estimate the carbon footprint. These conversion factors consider trip length, flight class (e.g. economy, business), and emissions metric (e.g. carbon dioxide equivalent, methane).

Generates a frequency distribution. The frequency distribution includes raw frequencies, percentages in each category, and cumulative frequencies. The frequency distribution can be stored as a data frame.

This package contains regional Floristic Quality Assessment databases that have been approved or approved with reservations by the U.S. Army Corps of Engineers (USACE). Paired with the fqacalc R package, these data sets allow for Floristic Quality Assessment metrics to be calculated. For information on FQA see Spyreas (2019) <doi:10.1002/ecs2.2825>. Both packages were developed for the USACE by the U.S. Army Engineer Research and Development Center's Environmental Laboratory.

Extends the capabilities for flexible partitioning and model-based clustering available in the packages flexclust and flexmix to handle ordinal and mixed-with-ordinal data types via new distance, centroid and driver functions that make various assumptions regarding ordinality. Using them within the flex-scheme allows for easy comparisons across methods.