Converts legacy microscopy video formats (H.264/H.265, AVI/MJPEG, TIFF stacks) to the modern AV1 codec with minimal quality loss. Typical use cases include compressing large TIFF stacks from confocal microscopy and time-lapse experiments from hundreds of gigabytes to manageable sizes, re-encoding MP4 files exported from CellProfiler', ImageJ'/'Fiji', and microscope software with approximately 2x better compression at the same visual quality, and converting legacy AVI (MJPEG) and H.265 recordings to a single patent-free format suited for long-term archival. Automatically selects the best available backend: GPU hardware acceleration via Vulkan VK_KHR_VIDEO_ENCODE_AV1 or VAAPI (tested on AMD RDNA4; bundled headers, builds with any Vulkan SDK >= 1.3.275), with automatic fallback to CPU encoding through FFmpeg and SVT-AV1'. User controls quality via a single CRF parameter; each backend adapts automatically (CPU and Vulkan use CRF directly, VAAPI targets 55 percent of input bitrate). TIFF stacks use near-lossless CRF 5 by default, with optional proportional scaling via tiff_scale (multiplier or bounding box, aspect ratio always preserved). Small frames are automatically scaled up to meet hardware encoder minimums. Audio tracks are preserved automatically. Provides a simple R API for batch conversion of entire experiment folders.

The goal of amp.sim is to transform NONMEM models into R syntax so they can be used for simulations using the deSolve', nlmixr2 or mrgsolve package. Additionally, functionality is included to aid simulations performed directly in NONMEM and to automatically create shiny apps for simulation models.

All animal behaviour occurs sequentially. The package has a number of functions to format sequence data from different sources, to analyse sequential behaviour and communication in animals. It also has functions to plot the data and to calculate the entropy of sequences.

This package implements the Bayesian Additive Voronoi Tessellation model for non-parametric regression and machine learning as introduced in Stone and Gosling (2025) <doi:10.1080/10618600.2024.2414104>. This package provides a flexible alternative to BART (Bayesian Additive Regression Trees) using Voronoi tessellations instead of trees. Users can fit Bayesian regression models, estimate posterior distributions, and visualise the resulting tessellations. It is particularly useful for spatial data analysis, machine learning regression, complex function approximation and Bayesian modeling where the underlying structure is unknown. The method is well-suited to capturing spatial patterns and non-linear relationships.

This package provides simple and intuitive functions for basic statistical analyses. Methods include the t-test (Student 1908 <doi:10.1093/biomet/6.1.1>), the Mann-Whitney U test (Mann and Whitney 1947 <doi:10.1214/aoms/1177730491>), Pearson's correlation (Pearson 1895 <doi:10.1098/rspl.1895.0041>), and analysis of variance (Fisher 1925, <doi:10.1007/978-1-4612-4380-9_5>). Functions are compatible with ggplot2 and dplyr'.

Adaptive wavelet lifting transforms for signal denoising using optimal local neighbourhood regression, from Nunes et al. (2006) <doi:10.1007/s11222-006-6560-y>.

This package provides a unified framework for Autoregressive Distributed Lag (ARDL) modeling and cointegration analysis. Implements Panel ARDL with Pooled Mean Group (PMG), Mean Group (MG), and Dynamic Fixed Effects (DFE) estimators following Pesaran, Shin & Smith (1999) <doi:10.1002/jae.616>. Provides bootstrap-based bounds testing per Pesaran, Shin & Smith (2001) <doi:10.1002/jae.616>. Includes Quantile Nonlinear ARDL (QNARDL) combining distributional and asymmetric effects based on Shin, Yu & Greenwood-Nimmo (2014) <doi:10.1007/978-1-4899-8008-3_9>, and Fourier ARDL for modeling smooth structural breaks following Enders & Lee (2012) <doi:10.1016/j.econlet.2012.05.019>. Features include Augmented ARDL (AARDL) with deferred t and F tests, Multiple-Threshold NARDL for complex asymmetries, Rolling/Recursive ARDL for time-varying relationships, and Panel NARDL for nonlinear panel cointegration. All methods include comprehensive diagnostics, publication-ready outputs, and visualization tools.

This package provides baseline functions for actigraphy and activity data. This package is intended to be extended by downstream overlays such as actiread', actimetrics', and stepcount'.

Query the four endpoints of the Air and Water Database (AWDB) REST API maintained by the National Water and Climate Center (NWCC) at the United States Department of Agriculture (USDA). Endpoints include data, forecast, reference-data, and metadata. The package is extremely light weight, with Rust via extendr doing most of the heavy lifting to deserialize and flatten deeply nested JSON responses. The AWDB can be found at <https://wcc.sc.egov.usda.gov/awdbRestApi/swagger-ui/index.html>.

Created to host raw accelerometry data sets and their derivatives which are used in the corresponding adept package.

Reads *.agd files exported from ActiGraph devices; implements the Troiano (2008) <doi:10.1249/mss.0b013e31815a51b3> and Choi (2011) <doi:10.1249/MSS.0b013e3181ed61a3> algorithms for detecting periods on non-wear; implements the Sadeh (1994) <doi:10.1093/sleep/17.3.201> and Cole-Kripke (1992) <doi:10.1093/sleep/15.5.461> algorithms for detecting asleep/awake state and the Tudor-Locke (2014) <doi:10.1139/apnm-2013-0173> algorithm to detect sleep periods from asleep/awake states.

Extract and process bird sightings records from eBird (<http://ebird.org>), an online tool for recording bird observations. Public access to the full eBird database is via the eBird Basic Dataset (EBD; see <http://ebird.org/ebird/data/download> for access), a downloadable text file. This package is an interface to AWK for extracting data from the EBD based on taxonomic, spatial, or temporal filters, to produce a manageable file size that can be imported into R.

This package provides a varied array of mathematical derivations from various titrimetric and colorimetric methods for analyzing water quality parameters were condensed and integrated for the better physicochemical analysis. It is indispensable for managing any aquatic ecosystem, including aquaculture facilities. By substituting titrant and spectrophotometric absorbance readings, accurate determination of the concentrations of critical parameters such as Dissolved Oxygen, Free Carbon Dioxide, Total Alkalinity, Water Hardness, Hydrogen Sulfide, Total Ammonia Nitrogen, Nitrite, Nitrate, Chlorinity, Salinity, Inorganic Phosphate, and Transparency can be facilitated APHA(2017,ISBN:9780875532875).

The Aligned Corpus Toolkit (act) is designed for linguists that work with time aligned transcription data. It offers functions to import and export various annotation file formats ('ELAN .eaf, EXMARaLDA .exb and Praat .TextGrid files), create print transcripts in the style of conversation analysis, search transcripts (span searches across multiple annotations, search in normalized annotations, make concordances etc.), export and re-import search results (.csv and Excel .xlsx format), create cuts for the search results (print transcripts, audio/video cuts using FFmpeg and video sub titles in Subrib title .srt format), modify the data in a corpus (search/replace, delete, filter etc.), interact with Praat using Praat'-scripts, and exchange data with the rPraat package. The package is itself written in R and may be expanded by other users.

This package provides methods for analyzing DNA copy-number data. Specifically, this package implements the multi-source copy-number normalization (MSCN) method for normalizing copy-number data obtained on various platforms and technologies. It also implements the TumorBoost method for normalizing paired tumor-normal SNP data.

This package provides a simulations-first sample size determination package that aims at making sample size formulae obsolete for most easily computable statistical experiments ; the main envisioned use case is clinical trials. The proposed clinical trial must be written by the user in the form of a function that takes as argument a sample size and returns a boolean (for whether or not the trial is a success). The adsasi functions will then use it to find the correct sample size empirically. The unavoidable mis-specification is obviated by trying sample size values close to the right value, the latter being understood as the value that gives the probability of success the user wants (usually 80 or 90% in biostatistics, corresponding to 20 or 10% type II error).

Interface package for sala', the spatial network analysis library from the depthmapX software application. The R parts of the code are based on the rdepthmap package. Allows for the analysis of urban and building-scale networks and provides metrics and methods usually found within the Space Syntax domain. Methods in this package are described by K. Al-Sayed, A. Turner, B. Hillier, S. Iida and A. Penn (2014) "Space Syntax methodology", and also by A. Turner (2004) <https://discovery.ucl.ac.uk/id/eprint/2651> "Depthmap 4: a researcher's handbook".

This package provides R bindings to the Automerge Conflict-free Replicated Data Type ('CRDT') library. Automerge enables automatic merging of concurrent changes without conflicts, making it ideal for distributed systems, collaborative applications, and offline-first architectures. The approach of local-first software was proposed in Kleppmann, M., Wiggins, A., van Hardenberg, P., McGranaghan, M. (2019) <doi:10.1145/3359591.3359737>. This package supports all Automerge data types (maps, lists, text, counters) and provides both low-level and high-level synchronization protocols for seamless interoperability with JavaScript and other Automerge implementations.

This is an implementation of the Generalized Discrimination Score (also known as Two Alternatives Forced Choice Score, 2AFC) for various representations of forecasts and verifying observations. The Generalized Discrimination Score is a generic forecast verification framework which can be applied to any of the following verification contexts: dichotomous, polychotomous (ordinal and nominal), continuous, probabilistic, and ensemble. A comprehensive description of the Generalized Discrimination Score, including all equations used in this package, is provided by Mason and Weigel (2009) <doi:10.1175/MWR-D-10-05069.1>.

Nonparametric estimation of additive isotonic covariate effects for proportional hazards model.

Automated generation, running, and interpretation of moderated nonlinear factor analysis models for obtaining scores from observed variables, using the method described by Gottfredson and colleagues (2019) <doi:10.1016/j.addbeh.2018.10.031>. This package creates M-plus input files which may be run iteratively to test two different types of covariate effects on items: (1) latent variable impact (both mean and variance); and (2) differential item functioning. After sequentially testing for all effects, it also creates a final model by including all significant effects after adjusting for multiple comparisons. Finally, the package creates a scoring model which uses the final values of parameter estimates to generate latent variable scores. \n\n This package generates TEMPLATES for M-plus inputs, which can and should be inspected, altered, and run by the user. In addition to being presented without warranty of any kind, the package is provided under the assumption that everyone who uses it is reading, interpreting, understanding, and altering every M-plus input and output file. There is no one right way to implement moderated nonlinear factor analysis, and this package exists solely to save users time as they generate M-plus syntax according to their own judgment.

An efficient Rcpp implementation of the Adaptive Rejection Metropolis Sampling (ARMS) algorithm proposed by Gilks, W. R., Best, N. G. and Tan, K. K. C. (1995) <doi:10.2307/2986138>. This allows for sampling from a univariate target probability distribution specified by its (potentially unnormalised) log density.

This package implements adaptive tau leaping to approximate the trajectory of a continuous-time stochastic process as described by Cao et al. (2007) The Journal of Chemical Physics <doi:10.1063/1.2745299> (aka. the Gillespie stochastic simulation algorithm). This package is based upon work supported by NSF DBI-0906041 and NIH K99-GM104158 to Philip Johnson and NIH R01-AI049334 to Rustom Antia.

Many complex plots are actually composite plots, such as oncoplot', funkyheatmap', upsetplot', etc. We can produce subplots using ggplot2 and combine them to create composite plots using aplot'. In this way, it is easy to customize these complex plots, by adding, deleting or modifying subplots in the final plot. This package provides a set of utilities to help users to create subplots and complex plots.