This package provides a set of functions to build simple GUI controls for R functions. These are built on the tcltk package. Uses could include changing a parameter on a graph by animating it with a slider or a "doublebutton", up to more sophisticated control panels. Some functions for specific graphical tasks, referred to as cartoons', are provided.

This package contains a variety of functions, based around regime shift analysis of paleoecological data. Citations: Rodionov() from Rodionov (2004) <doi:10.1029/2004GL019448> Lanzante() from Lanzante (1996) <doi:10.1002/(SICI)1097-0088(199611)16:11%3C1197::AID-JOC89%3E3.0.CO;2-L> Hellinger_trans from Numerical Ecology, Legendre & Legendre (ISBN 9780444538680) rolling_autoc from Liu, Gao & Wang (2018) <doi:10.1016/j.scitotenv.2018.06.276> Sample data sets lake_data & lake_RSI processed from Bush, Silman & Urrego (2004) <doi:10.1126/science.1090795> Sample data set January_PDO from NOAA: <https://www.ncei.noaa.gov/access/monitoring/pdo/>.

This package provides functions to conduct hypothesis tests and derive confidence intervals for quantiles, linear combinations of quantiles, ratios of dependent linear combinations and differences and ratios of all of the above for comparisons between independent samples. Additionally, quantile-based measures of inequality are also considered.

This package provides functions to convert Rd to roxygen documentation. It can parse an Rd file to a list, create the roxygen documentation and update the original R script (e.g. the one containing the definition of the function) accordingly. This package also provides utilities that can help developers build packages using roxygen more easily. The formatR package can be used to reformat the R code in the examples sections so that the code will be more readable.

Extracts tagged text from markdown manuscripts for inclusion in dynamically generated revision letters. Provides an R markdown template based on papaja::revision_letter_pdf() with comment cross-referencing, a system for managing multiple sections of extracted text, and a way to automatically determine the page number of quoted sections from PDF manuscripts.

Assesses the robustness of the community structure of a network found by one or more community detection algorithm to give indications about their reliability. It detects if the community structure found by a set of algorithms is statistically significant and compares the different selected detection algorithms on the same network. robin helps to choose among different community detection algorithms the one that better fits the network of interest. Reference in Policastro V., Righelli D., Carissimo A., Cutillo L., De Feis I. (2021) <https://journal.r-project.org/archive/2021/RJ-2021-040/index.html>.

Defines storage standard for Read, process, and analyze intracranial electroencephalography and deep-brain stimulation in RAVE', a reproducible framework for analysis and visualization of iEEG by Magnotti, Wang, and Beauchamp, (2020, <doi:10.1016/j.neuroimage.2020.117341>). Supports brain imaging data structure (BIDS) <https://bids.neuroimaging.io> and native file structure to ingest signals from Matlab data files, hierarchical data format 5 (HDF5), European data format (EDF), BrainVision core data format (BVCDF), or BlackRock Microsystem (NEV/NSx); process images in Neuroimaging informatics technology initiative (NIfTI) and FreeSurfer formats, providing brain imaging normalization to template brain, facilitating threeBrain package for comprehensive electrode localization via YAEL (your advanced electrode localizer) by Wang, Magnotti, Zhang, and Beauchamp (2023, <doi:10.1523/ENEURO.0328-23.2023>).

This package implements a computational framework for a pattern-based, zoneless analysis, and visualization of (ethno)racial topography (Dmowska, Stepinski, and Nowosad (2020) <doi:10.1016/j.apgeog.2020.102239>). It is a reimagined approach for analyzing residential segregation and racial diversity based on the concept of landscapeâ used in the domain of landscape ecology.

Sets a significance level for Random Forest MDI (Mean Decrease in Impurity, Gini or sum of squares) variable importance scores, using an empirical Bayes approach. See Dunne et al. (2022) <doi:10.1101/2022.04.06.487300>.

Uses a combination of raytracing and multiple hill shading methods to produce 2D and 3D data visualizations and maps. Includes water detection and layering functions, programmable color palette generation, several built-in textures for hill shading, 2D and 3D plotting options, a built-in path tracer, Wavefront OBJ file export, and the ability to save 3D visualizations to a 3D printable format.

This package provides a parallel function for multivariate outlier detection named modified Stahel-Donoho estimators is contained in this package. The function RMSDp() is for elliptically distributed datasets and recognizes outliers based on Mahalanobis distance. This function is for higher dimensional datasets that cannot be handled by a single core function RMSD() included in RMSD package. See Wada and Tsubaki (2013) <doi:10.1109/CLOUDCOM-ASIA.2013.86> for the detail of the algorithm.

This package provides tools for randomization-based inference. Current focus is on the d^2 omnibus test of differences of means following Hansen and Bowers (2008) <doi:10.1214/08-STS254> . This test is useful for assessing balance in matched observational studies or for analysis of outcomes in block-randomized experiments.

Generic functions to analyze the distribution of two continuous variables: conf2d to calculate a smooth empirical confidence region, and freq2d to calculate a frequency distribution.

This package provides a data manager meant to avoid manual storage/retrieval of data to/from the file system. It builds one (or more) centralized repository where R objects are stored with rich annotations, including corresponding code chunks, and easily searched and retrieved. See Napolitano (2017) <doi:10.1037/a0028240> for further information.

Download and plot Open Street Map <https://www.openstreetmap.org/>, Bing Maps <https://www.bing.com/maps> and other tiled map sources. Use to create basemaps quickly and add hillshade to vector-based maps.

Flux (mass per unit time) and Load (mass) are computed from timeseries estimates of analyte concentration and discharge. Concentration timeseries are computed from regression between surrogate and user-provided analyte. Uncertainty in calculations is estimated using bootstrap resampling. Code for the processing of acoustic backscatter from horizontally profiling acoustic Doppler current profilers is provided. All methods detailed in Livsey et al (2020) <doi:10.1007/s12237-020-00734-z>, Livsey et al (2023) <doi:10.1029/2022WR033982>, and references therein.

Rcpp11 includes a header only C++11 library that facilitates integration between R and modern C++.

The commonly used methods for relative quantification of gene expression levels obtained in real-time PCR (Polymerase Chain Reaction) experiments are the delta Ct methods, encompassing 2^-dCt and 2^-ddCt methods, originally proposed by Kenneth J. Livak and Thomas D. Schmittgen (2001) <doi:10.1006/meth.2001.1262>. The main idea is to normalise gene expression values using endogenous control gene, present gene expression levels in linear form by using the 2^-(value)^ transformation, and calculate differences in gene expression levels between groups of samples (or technical replicates of a single sample). The RQdeltaCT package offers functions that cover both methods for comparison of either independent groups of samples or groups with paired samples, together with importing expression datasets, performing multi-step quality control of data, enabling numerous data visualisations, enrichment of the standard workflow with additional useful analyses (correlation analysis, Receiver Operating Characteristic analysis, logistic regression), and conveniently export obtained results in table and image formats. The package has been designed to be friendly to non-experts in R programming.

It computes the Schmidt decomposition of bipartite quantum systems, discrete or continuous, and their respective entanglement metrics. See Artur Ekert, Peter L. Knight (1995) <doi:10.1119/1.17904> for more details.

Providing just one primary function, readit uses a set of reasonable heuristics to apply the appropriate reader function to the given file path. As long as the data file has an extension, and the data is (or can be coerced to be) rectangular, readit() can probably read it.

This package provides a programmatic interface to the API provided by the iNaturalist website <https://www.inaturalist.org/> to download species occurrence data submitted by citizen scientists.

Enables Retrieval-Augmented Generation (RAG) workflows in R by combining local vector search using DuckDB with optional web search via the Tavily API. Supports OpenAI'- and Ollama'-compatible embedding models, full-text and HNSW (Hierarchical Navigable Small World) indexing, and modular large language model (LLM) invocation. Designed for advanced question-answering, chat-based applications, and production-ready AI pipelines. This package is the R equivalent of the python package RAGFlowChain available at <https://pypi.org/project/RAGFlowChain/>.

Blaze is an open-source, high-performance C++ math library for dense and sparse arithmetic. With its state-of-the-art Smart Expression Template implementation Blaze combines the elegance and ease of use of a domain-specific language with HPC-grade performance, making it one of the most intuitive and fastest C++ math libraries available. The RcppBlaze package includes the header files from the Blaze library with disabling some functionalities related to link to the thread and system libraries which make RcppBlaze be a header-only library. Therefore, users do not need to install Blaze'.

Calculate the probability density functions (PDFs) for two threshold evidence accumulation models (EAMs). These are defined using the following Stochastic Differential Equation (SDE), dx(t) = v(x(t),t)*dt+D(x(t),t)*dW, where x(t) is the accumulated evidence at time t, v(x(t),t) is the drift rate, D(x(t),t) is the noise scale, and W is the standard Wiener process. The boundary conditions of this process are the upper and lower decision thresholds, represented by b_u(t) and b_l(t), respectively. Upper threshold b_u(t) > 0, while lower threshold b_l(t) < 0. The initial condition of this process x(0) = z where b_l(t) < z < b_u(t). We represent this as the relative start point w = z/(b_u(0)-b_l(0)), defined as a ratio of the initial threshold location. This package generates the PDF using the same approach as the python package it is based upon, PyBEAM by Murrow and Holmes (2023) <doi:10.3758/s13428-023-02162-w>. First, it converts the SDE model into the forwards Fokker-Planck equation dp(x,t)/dt = d(v(x,t)*p(x,t))/dt-0.5*d^2(D(x,t)^2*p(x,t))/dx^2, then solves this equation using the Crank-Nicolson method to determine p(x,t). Finally, it calculates the flux at the decision thresholds, f_i(t) = 0.5*d(D(x,t)^2*p(x,t))/dx evaluated at x = b_i(t), where i is the relevant decision threshold, either upper (i = u) or lower (i = l). The flux at each thresholds f_i(t) is the PDF for each threshold, specifically its PDF. We discuss further details of this approach in this package and PyBEAM publications. Additionally, one can calculate the cumulative distribution functions of and sampling from the EAMs.