Fit occupancy models in Stan via brms'. The full variety of brms formula-based effects structures are available to use in multiple classes of occupancy model, including single-season models, models with data augmentation for never-observed species, dynamic (multiseason) models with explicit colonization and extinction processes, and dynamic models with autologistic occupancy dynamics. Formulas can be specified for all relevant distributional terms, including detection and one or more of occupancy, colonization, extinction, and autologistic depending on the model type. Several important forms of model post-processing are provided. References: Bürkner (2017) <doi:10.18637/jss.v080.i01>; Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>; Socolar & Mills (2023) <doi:10.1101/2023.10.26.564080>.

Around 10% of almost any predictive modeling project is spent in predictive modeling, funModeling and the book Data Science Live Book (<https://livebook.datascienceheroes.com/>) are intended to cover remaining 90%: data preparation, profiling, selecting best variables dataViz', assessing model performance and other functions.

Fast functions for timestamp manipulation that avoid system calls and take shortcuts to facilitate operations on very large data.

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>.

This package provides allele frequency data for Short Tandem Repeat human genetic markers commonly used in forensic genetics for human identification and kinship analysis. Includes published population frequency data from the US National Institute of Standards and Technology, Federal Bureau of Investigation and the UK government.

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

Processes data from The Social Networks and Fertility Survey, downloaded from <https://dataarchive.lissdata.nl>, including correcting respondent errors and transforming network data into network objects to facilitate analyses and visualisation.

Average rating and number of votes reported by IMDb for films and shorts with over 100 votes in 2022. The data are analysed in Chapter 3 of the Book Getting (more out of) Graphics (Antony Unwin, CRC Press 2024).

Useful functions to translate text for multiple languages using online translators. For example, by translating error messages and descriptive analysis results into a language familiar to the user, it enables a better understanding of the information, thereby reducing the barriers caused by language. It offers several helper functions to query gene information to help interpretation of interested genes (e.g., marker genes, differential expression genes), and provides utilities to translate ggplot graphics. This package is not affiliated with any of the online translators. The developers do not take responsibility for the invoice it incurs when using this package, especially for exceeding the free quota.

This package provides tools to work with the Flexible Dirichlet distribution. The main features are an E-M algorithm for computing the maximum likelihood estimate of the parameter vector and a function based on conditional bootstrap to estimate its asymptotic variance-covariance matrix. It contains also functions to plot graphs, to generate random observations and to handle compositional data.

Defines a collection of functions to compute average power and sample size for studies that use the false discovery rate as the final measure of statistical significance.

This package provides a collection of features, decomposition methods, statistical summaries and graphics functions for the analysing tidy time series data. The package name feasts is an acronym comprising of its key features: Feature Extraction And Statistics for Time Series.

Generates RProtobuf classes for FactSet STACH V2 tabular format which represents complex multi-dimensional array of data. These classes help in the serialization and deserialization of STACH V2 formatted data. See GitHub repository documentation for more information.

This package implements a novel approach for measuring feature importance in k-means clustering. Importance of a feature is measured by the misclassification rate relative to the baseline cluster assignment due to a random permutation of feature values. An explanation of permutation feature importance in general can be found here: <https://christophm.github.io/interpretable-ml-book/feature-importance.html>.

Fuzzy clustering of species in an ecological community as common or rare based on their abundance and occupancy. It also includes functions to compute confidence intervals of classification metrics and plot results. See Balbuena et al. (2020, <doi:10.1101/2020.08.12.247502>).

Reads cell contents plus formatting from a spreadsheet file and creates an editable gt object with the same data and formatting. Supports the most commonly-used cell and text styles including colors, fills, font weights and decorations, and borders.

Some basic procedures for dealing with log maximally skew stable distributions, which are also called finite moment log stable distributions.

The funFEM algorithm (Bouveyron et al., 2014) allows to cluster functional data by modeling the curves within a common and discriminative functional subspace.

Provide functions for forest inventory calculations. Common volumetric equations (Smalian, Newton and Huber) as well stacking factor and form.

SHE, FORAM Index and ABC Method analyses and custom plot functions for community data.

This package provides functional control charts for statistical process monitoring of functional data, using the methods of Capezza et al. (2020) <doi:10.1002/asmb.2507>, Centofanti et al. (2021) <doi:10.1080/00401706.2020.1753581>, Capezza et al. (2024) <doi:10.1080/00224065.2024.2383674>, Capezza et al. (2024) <doi:10.1080/00401706.2024.2327346>, Centofanti et al. (2025) <doi:10.1080/00224065.2024.2430978>, Capezza et al. (2025) <doi:10.48550/arXiv.2410.20138>. The package is thoroughly illustrated in the paper of Capezza et al (2023) <doi:10.1080/00224065.2023.2219012>.

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'.

Implementation of the Interval Testing Procedure for functional data in different frameworks (i.e., one or two-population frameworks, functional linear models) by means of different basis expansions (i.e., B-spline, Fourier, and phase-amplitude Fourier). The current version of the package requires functional data evaluated on a uniform grid; it automatically projects each function on a chosen functional basis; it performs the entire family of multivariate tests; and, finally, it provides the matrix of the p-values of the previous tests and the vector of the corrected p-values. The functional basis, the coupled or uncoupled scenario, and the kind of test can be chosen by the user. The package provides also a plotting function creating a graphical output of the procedure: the p-value heat-map, the plot of the corrected p-values, and the plot of the functional data.

Converts R data frames and sf spatial objects into JSON and GeoJSON strings. The core encoders are implemented in Rust using the extendr framework and are designed to efficiently serialize large tabular and spatial datasets. Returns serialized JSON text, allowing applications such as shiny or web APIs to transfer data to client-side JavaScript libraries without additional encoding overhead.