Forensic applications of pedigree analysis, including likelihood ratios for relationship testing, general relatedness inference, marker simulation, and power analysis. forrel is part of the pedsuite', a collection of packages for pedigree analysis, further described in the book Pedigree Analysis in R (Vigeland, 2021, ISBN:9780128244302). Several functions deal specifically with power analysis in missing person cases, implementing methods described in Vigeland et al. (2020) <doi:10.1016/j.fsigen.2020.102376>. Data import from the Familias software (Egeland et al. (2000) <doi:10.1016/S0379-0738(00)00147-X>) is supported through the pedFamilias package.

Simulates plot data in multi-environment field trials with one or more traits. Its core function generates plot errors that capture spatial trend, random error (noise), and extraneous variation, which are combined at a user-defined ratio. Phenotypes can be generated by combining the plot errors with simulated genetic values that capture genotype-by-environment (GxE) interaction using wrapper functions for the R package `AlphaSimR`.

Flipbooks present code step-by-step and side-by-side with its output. flipbookr helps creators build flipbooks efficiently because code pipelines are automatically parsed and prepped for presentation as flipbooks.

Generate SPSS'/'SAS styled frequency tables. Frequency tables are generated with variable and value label attributes where applicable with optional html output to quickly examine datasets.

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.

Read and write PNG images with arrays, rasters, native rasters, numeric arrays, integer arrays, raw vectors and indexed values. This PNG encoder exposes configurable internal options enabling the user to select a speed-size tradeoff. For example, disabling compression can speed up writing PNG by a factor of 50. Multiple image formats are supported including raster, native rasters, and integer and numeric arrays at color depths of 1, 2, 3 or 4. 16-bit images are also supported. This implementation uses the libspng C library which is available from <https://github.com/randy408/libspng/>.

Allows the user to execute interactively radial data envelopment analysis models. The user has the ability to upload a data frame, select the input/output variables, choose the technology assumption to adopt and decide whether to run an input or an output oriented model. When the model is executed a set of results are displayed which include efficiency scores, peers determination, scale efficiencies evaluation and slacks calculation. Fore more information about the theoretical background of the package, please refer to Bogetoft & Otto (2011) <doi:10.1007/978-1-4419-7961-2>.

This package provides tools for detecting and summarize influential cases that can affect exploratory and confirmatory factor analysis models as well as structural equation models more generally (Chalmers, 2015, <doi:10.1177/0146621615597894>; Flora, D. B., LaBrish, C. & Chalmers, R. P., 2012, <doi:10.3389/fpsyg.2012.00055>).

This package provides functionality to produce graphs of probability density functions and cumulative distribution functions with few keystrokes, allows shading under the curve of the probability density function to illustrate concepts such as p-values and critical values, and fits a simple linear regression line on a scatter plot with the equation as the main title.

This package creates dynamic grid layouts of images that can be included in Shiny applications and R markdown documents.

This package provides generic data structures and algorithms for use with forest mensuration data in a consistent framework. The functions and objects included are a collection of broadly applicable tools. More specialized applications should be implemented in separate packages that build on this foundation. Documentation about ForestElementsR is provided by three vignettes included in this package. For an introduction to the field of forest mensuration, refer to the textbooks by Kershaw et al. (2017) <doi:10.1002/9781118902028>, and van Laar and Akca (2007) <doi:10.1007/978-1-4020-5991-9>.

This package implements the bayesRecon probabilistic reconciliation methods within the fable framework for hierarchical time series forecasting. Bayesian reconciliation (bayesRecon) methods are accessed via the reconcile verb, following fable conventions. For methodological background, see Corani et al. (2021) <doi:10.1007/978-3-030-67664-3_13>, Zambon et al. (2024a) <doi:10.1007/s11222-023-10343-y>, Zambon et al. (2024b) <https://proceedings.mlr.press/v244/zambon24a.html>, and Carrara et al. (2026) <doi:10.1016/j.ijforecast.2026.07.003>.

This package provides functions for analysing and modelling extreme events in financial time Series. The topics include: (i) data pre-processing, (ii) explorative data analysis, (iii) peak over threshold modelling, (iv) block maxima modelling, (v) estimation of VaR and CVaR, and (vi) the computation of the extreme index.

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>.

For binomial outcome data Alternate Binomial Distributions and Binomial Mixture Distributions are fitted when overdispersion is available.

Fair machine learning regression models which take sensitive attributes into account in model estimation. Currently implementing Komiyama et al. (2018) <http://proceedings.mlr.press/v80/komiyama18a/komiyama18a.pdf>, Zafar et al. (2019) <https://www.jmlr.org/papers/volume20/18-262/18-262.pdf> and my own approach from Scutari, Panero and Proissl (2022) <doi:10.1007/s11222-022-10143-w> that uses ridge regression to enforce fairness.

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.

Converts R objects such as data frames, lists and vectors into JSON strings, and sf spatial objects into GeoJSON'. 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.

Integrated Functional Depth for Partially Observed Functional Data and applications to visualization, outlier detection and classification. It implements the methods proposed in: Elà as, A., Jiménez, R., Paganoni, A. M. and Sangalli, L. M., (2023), "Integrated Depth for Partially Observed Functional Data", Journal of Computational and Graphical Statistics, <doi:10.1080/10618600.2022.2070171>. Elà as, A., Jiménez, R., & Shang, H. L. (2023), "Depth-based reconstruction method for incomplete functional data", Computational Statistics, <doi:10.1007/s00180-022-01282-9>. Elà as, A., Nagy, S. (2024), "Statistical properties of partially observed integrated functional depths", TEST, <doi:10.1007/s11749-024-00954-6>.

Simulation and analysis of Fully-Latent Principal Stratification (FLPS) with measurement models. Lee, Adam, Kang, & Whittaker (2023). <doi:10.1007/978-3-031-27781-8_25>. This package is supported by the Institute of Education Sciences, U.S. Department of Education, through Grant R305D210036.

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.

Compute maximum likelihood estimators of parameters in a Gaussian factor model using the the matrix-free methodology described in Dai et al. (2020) <doi:10.1080/10618600.2019.1704296>. In contrast to the factanal() function from stats package, fad() can handle high-dimensional datasets where number of variables exceed the sample size and is also substantially faster than the EM algorithms.

Estimate the of fractal dimension of a black area in 2D and 3D (slices) images using the box-counting method. See Klinkenberg B. (1994) <doi:10.1007/BF02065874>.

This package provides a simplified interface to the Central Data Repository REST API service made available by the United States Federal Financial Institutions Examination Council ('FFIEC'). Contains functions to retrieve reports of Condition and Income (Call Reports) and Uniform Bank Performance Reports ('UBPR') in list or tidy data frame format for most FDIC insured institutions. See <https://cdr.ffiec.gov/public/Files/SIS611_-_Retrieve_Public_Data_via_Web_Service.pdf> for the official REST API documentation published by the FFIEC'.