This package provides design-based and model-based estimators for the population average marginal component effects in general factorial experiments, including conjoint analysis. The package also implements a series of recommendations offered in de la Cuesta, Egami, and Imai (2022) <doi:10.1017/pan.2020.40>, and Egami and Imai (2019) <doi:10.1080/01621459.2018.1476246>.

Fits models to catch and effort data. Single-species models are 1) delta log-normal, 2) Tweedie, or 3) Poisson-gamma (G)LMs.

Transform output files of some tools to the microtable object of microtable class in microeco package. The microtable class is the basic class in microeco package and is necessary for the downstream microbial community data analysis.

An easy-to-use web client/wrapper for the Figma API <https://www.figma.com/developers/api>. It allows you to bring all data from a Figma file to your R session. This includes the data of all objects that you have drawn in this file, and their respective canvas/page metadata.

This package provides functions to automate the detection and resolution of taxonomic and stratigraphic errors in fossil occurrence datasets. Functions were developed using data from the Paleobiology Database.

We consider optimal subset selection in the setting that one needs to use only one data subset to represent the whole data set with minimum information loss, and devise a novel intersection-based criterion on selecting optimal subset, called as the FPC criterion, to handle with the optimal sub-estimator in distributed principal component analysis; That is, the FPCdpca. The philosophy of the package is described in Guo G. (2025) <doi:10.1016/j.physa.2024.130308>.

This package contains a set of functions that can be used to apply formats to data frames or vectors. The package aims to provide functionality similar to that of SAS® formats. Formats are assigned to the format attribute on data frame columns. Then when the fdata() function is called, a new data frame is created with the column data formatted as specified. The package also contains a value() function to create a user-defined format, similar to a SAS® user-defined format.

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).

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 implementation of statistical methods for random objects lying in various metric spaces, which are not necessarily linear spaces. The core of this package is Fréchet regression for random objects with Euclidean predictors, which allows one to perform regression analysis for non-Euclidean responses under some mild conditions. Examples include distributions in 2-Wasserstein space, covariance matrices endowed with power metric (with Frobenius metric as a special case), Cholesky and log-Cholesky metrics, spherical data. References: Petersen, A., & Müller, H.-G. (2019) <doi:10.1214/17-AOS1624>.

Fresh biomass determination is the key to evaluating crop genotypes response to diverse input and stress conditions and forms the basis for calculating net primary production. However, as conventional phenotyping approaches for measuring fresh biomass is time-consuming, laborious and destructive, image-based phenotyping methods are being widely used now. In the image-based approach, the fresh weight of the above-ground part of the plant depends on the projected area. For determining the projected area, the visual image of the plant is converted into the grayscale image by simply averaging the Red(R), Green (G) and Blue (B) pixel values. Grayscale image is then converted into a binary image using Otsuâ s thresholding method Otsu, N. (1979) <doi:10.1109/TSMC.1979.4310076> to separate plant area from the background (image segmentation). The segmentation process was accomplished by selecting the pixels with values over the threshold value belonging to the plant region and other pixels to the background region. The resulting binary image consists of white and black pixels representing the plant and background regions. Finally, the number of pixels inside the plant region was counted and converted to square centimetres (cm2) using the reference object (any object whose actual area is known previously) to get the projected area. After that, the projected area is used as input to the machine learning model (Linear Model, Artificial Neural Network, and Support Vector Regression) to determine the plant's fresh weight.

An implementation of sparsity-ranked lasso and related methods for time series data. This methodology is especially useful for large time series with exogenous features and/or complex seasonality. Originally described in Peterson and Cavanaugh (2022) <doi:10.1007/s10182-021-00431-7> in the context of variable selection with interactions and/or polynomials, ranked sparsity is a philosophy with methods useful for variable selection in the presence of prior informational asymmetry. This situation exists for time series data with complex seasonality, as shown in Peterson and Cavanaugh (2024) <doi:10.1177/1471082X231225307>, which also describes this package in greater detail. The sparsity-ranked penalization methods for time series implemented in fastTS can fit large/complex/high-frequency time series quickly, even with a high-dimensional exogenous feature set. The method is considerably faster than its competitors, while often producing more accurate predictions. Also included is a long hourly series of arrivals into the University of Iowa Emergency Department with concurrent local temperature.

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'.

Multidimensional scaling (MDS) functions for various tasks that are beyond the beta stage and way past the alpha stage. Currently, options are available for weights, restrictions, classical scaling or principal coordinate analysis, transformations (linear, power, Box-Cox, spline, ordinal), outlier mitigation (rdop), out-of-sample estimation (predict), negative dissimilarities, fast and faster executions with low memory footprints, penalized restrictions, cross-validation-based penalty selection, supplementary variable estimation (explain), additive constant estimation, mixed measurement level distance calculation, restricted classical scaling, etc. More will come in the future. References. Busing (2024) "A Simple Population Size Estimator for Local Minima Applied to Multidimensional Scaling". Manuscript submitted for publication. Busing (2025) "Node Localization by Multidimensional Scaling with Iterative Majorization". Manuscript submitted for publication. Busing (2025) "Faster Multidimensional Scaling". Manuscript in preparation. Barroso and Busing (2025) "e-RDOP, Relative Density-Based Outlier Probabilities, Extended to Proximity Mapping". Manuscript submitted for publication.

The function estimates a multivariate regression model for outcomes with network dependence.

Exchange rate regression and structural change tools for estimating, testing, dating, and monitoring (de facto) exchange rate regimes.

This package provides a collection of commonly used univariate and multivariate time series forecasting models including automatically selected exponential smoothing (ETS) and autoregressive integrated moving average (ARIMA) models. These models work within the fable framework provided by the fabletools package, which provides the tools to evaluate, visualise, and combine models in a workflow consistent with the tidyverse.

Feature flags allow developers to turn features of their software on and off in form of configuration. This package provides functions for creating feature flags in code. It exposes an interface for defining own feature flags which are enabled based on custom criteria.

Finds features through a detailed analysis of model residuals using rpart classification and regression trees. Scans the residuals of a model across subsets of the data to identify areas where the model differs from the actual data.

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 drop-in replacement for flexdashboard Rmd documents, which implements an after-knit-hook to split the generated single page application in one document per main section to reduce rendering load in the web browser displaying the document. Put all JavaScript stuff needed in all sections before the first headline featuring navigation menu attributes. This package is experimental and maybe replaced by a solution inside flexdashboard'.

This package implements a very fast C++ algorithm to quickly bootstrap receiver operating characteristics (ROC) curves and derived performance metrics, including the area under the curve (AUC) and the partial area under the curve as well as the true and false positive rate. The analysis of paired receiver operating curves is supported as well, so that a comparison of two predictors is possible. You can also plot the results and calculate confidence intervals. On a typical desktop computer the time needed for the calculation of 100000 bootstrap replicates given 500 observations requires time on the order of magnitude of one second.

This package provides functions to implement the Flexible cFDR (Hutchinson et al. (2021) <doi:10.1371/journal.pgen.1009853>) and Binary cFDR (Hutchinson et al. (2021) <doi:10.1101/2021.10.21.465274>) methodologies to leverage auxiliary data from arbitrary distributions, for example functional genomic data, with GWAS p-values to generate re-weighted p-values.

Extends data.table join functionality, lets it work with any data frame class, and provides a familiar x'/'y'-style interface, enabling broad use across R. Offers NA-safe matching by default, on-the-fly column selection, multiple match-handling on both sides, x or y row order, and a row origin indicator. Performs inner, left, right, full, semi- and anti-joins with equality and inequality conditions, plus cross joins. Specific support for data.table', (grouped) tibble, and sf'/'sfc objects and their attributes; returns a plain data frame otherwise. Avoids data-copying of inputs and outputs. Allows displaying the data.table code instead of (or as well as) executing it.