This tree-based method deals with high dimensional longitudinal data with correlated features through the use of a piecewise random effect model. FREE tree also exploits the network structure of the features, by first clustering them using Weighted Gene Co-expression Network Analysis ('WGCNA'). It then conducts a screening step within each cluster of features and a selecting step among the surviving features, which provides a relatively unbiased way to do feature selection. By using dominant principle components as regression variables at each leaf and the original features as splitting variables at splitting nodes, FREE tree delivers easily interpretable results while improving computational efficiency.

This package implements the formulae required to calculate freedom from disease according to Cameron and Baldock (1998) <doi:10.1016/S0167-5877(97)00081-0>. These are the methods used at the Swedish national veterinary institute (SVA) to evaluate the performance of our nation animal disease surveillance programmes.

The FLEX method, developed by Yoon and Choi (2013) <doi:10.1007/978-3-642-33042-1_21>, performs least squares estimation for fuzzy predictors and outcomes, generating crisp regression coefficients by minimizing the distance between observed and predicted outcomes. It also provides functions for fuzzifying data and inference tasks, including significance testing, fit indices, and confidence interval estimation.

This package provides a shiny design of experiments (DOE) app that aids in the creation of traditional, un-replicated, augmented and partially-replicated designs applied to agriculture, plant breeding, forestry, animal and biological sciences.

This package provides a model-independent factor importance ranking and selection procedure based on total Sobol indices. Please see Huang and Joseph (2025) <doi:10.1080/00401706.2025.2483531>. This research is supported by U.S. National Science Foundation grants DMS-2310637 and DMREF-1921873.

Efficient approximation of first passage time densities for diffusion processes based on the First Passage Time Location (FPTL) function.

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.

Access data from the Federal Register API <https://www.federalregister.gov/developers/api/v1>.

Time-based joins to analyze sequence of events, both in memory and out of memory. after_join() joins two tables of events, while funnel_start() and funnel_step() join events in the same table. With the type argument, you can switch between different funnel types, like first-first and last-firstafter.

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).

Calculation of Evapotranspiration by FAO Penman-Monteith equation based on Allen, R. G., Pereira, L. S., Raes, D., Smith, M. (1998, ISBN:92-5-104219-5) "Crop evapotranspiration - Guidelines for computing crop water requirements - FAO Irrigation and drainage paper 56".

Basic analysis of all penalties taken in the German men's Bundesliga between the start of its inaugural season and May 2017. The main functions are suitable printing and plotting functions. Flexible selection of a player is supported via grep. Missed penalties can easily be included or excluded, depending on the user's wishes.

Supports the use of standardized folder names.

Recent years have seen significant interest in neighborhood-based structural parameters that effectively represent the spatial characteristics of tree populations and forest communities, and possess strong applicability for guiding forestry practices. This package provides valuable information that enhances our understanding and analysis of the fine-scale spatial structure of tree populations and forest stands. Reference: Yan L, Tan W, Chai Z, et al (2019) <doi:10.13323/j.cnki.j.fafu(nat.sci.).2019.03.007>.

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.

This package provides functions for finding smooth interpolating curves connecting a series of points in the plane. Curves may be open or closed, that is, with the first and last point of the curve at the initial point.

Automatically perform a reanalysis series on a data set using CNA, and calculate the fit-robustness of the resulting models, as described in Parkkinen and Baumgartner (2021) <doi:10.1177/0049124120986200>.

An R interface to FLINT <https://flintlib.org/>, a C library for number theory. FLINT extends GNU MPFR <https://www.mpfr.org/> and GNU MP <https://gmplib.org/> with support for operations on standard rings (the integers, the integers modulo n, finite fields, the rational, p-adic, real, and complex numbers) as well as matrices and polynomials over rings. FLINT implements midpoint-radius interval arithmetic, also known as ball arithmetic, in the real and complex numbers, enabling computation in arbitrary precision with rigorous propagation of rounding and other errors; see Johansson (2017) <doi:10.1109/TC.2017.2690633>. Finally, FLINT provides ball arithmetic implementations of many special mathematical functions, with high coverage of reference works such as the NIST Digital Library of Mathematical Functions <https://dlmf.nist.gov/>. The R interface defines S4 classes, generic functions, and methods for representation and basic operations as well as plain R functions mirroring and vectorizing entry points in the C library.

Latent process embedding for functional network data with the Functional Adjacency Spectral Embedding. Fits smooth latent processes based on cubic spline bases. Also generates functional network data from three models, and evaluates a network generalized cross-validation criterion for dimension selection. For more information, see MacDonald, Zhu and Levina (2022+) <arXiv:2210.07491>.

This package provides tools for preprocessing, feature extraction, and segmentation of three-dimensional forest point clouds derived from terrestrial laser scanning. Functions support creating height-above-ground (HAG) metrics, tiling, and sampling point clouds, generating training datasets, applying trained models to new point clouds, and producing per-point fuel classes such as stems, branches, foliage, and surface fuels. These tools support workflows for forest structure analysis, wildfire behavior modeling, and fuel complexity assessment. Deep learning segmentation relies on the PointNeXt architecture described by Qian et al. (2022) <doi:10.48550/arXiv.2206.04670>, while ground classification utilizes the Cloth Simulation Filter algorithm by Zhang et al. (2016) <doi:10.3390/rs8060501>.

This package provides a research estimation tool for analysts that work with sample-based inventory data from the U.S. Department of Agriculture, Forest Service, Forest Inventory and Analysis (FIA) Program.

The FastRCS algorithm of Vakili and Schmitt (2014) for robust fit of the multivariable linear regression model and outliers detection.

Perform robust inference based on applying Fast and Robust Bootstrap on robust estimators (Van Aelst and Willems (2013) <doi:10.18637/jss.v053.i03>). This method constitutes an alternative to ordinary bootstrap or asymptotic inference. procedures when using robust estimators such as S-, MM- or GS-estimators. The available methods are multivariate regression, principal component analysis and one-sample and two-sample Hotelling tests. It provides both the robust point estimates and uncertainty measures based on the fast and robust bootstrap.

Data and functions for the book "Multivariate Statistical Modelling Based on Generalized Linear Models", first edition, by Ludwig Fahrmeir and Gerhard Tutz. Useful when using the book.