Communications simulation package supporting forward error correction.

Implementation of some functions to create quizzes in the GIFT format. This format is used by several Virtual Learning Environments such as Moodle.

This package provides tools for the analysis of reverse-phase protein arrays (RPPAs), which are also known as tissue lysate arrays or simply lysate arrays'. The package's primary purpose is to input a set of quantification files representing dilution series of samples and control points taken from scanned RPPA slides and determine a relative log concentration value for each valid dilution series present in each slide and provide graphical visualization of the input and output data and their relationships. Other optional features include generation of quality control scores for judging the quality of the input data, spatial adjustment of sample points based on controls added to the slides, and various types of normalization of calculated values across a set of slides. The package was derived from a previous package named SuperCurve. For a detailed description of data inputs and outputs, usage information, and a list of related papers describing methods used in the package please review the vignette Guide_to_RPPASPACE'. RPPA SPACE: an R package for normalization and quantitation of Reverse-Phase Protein Array data'. Bioinformatics Nov 15;38(22):5131-5133. <doi: 10.1093/bioinformatics/btac665>.

Allows developers to work with many R folders inside a package. It offers functionalities to transfer R scripts (saved outside the R folder) into the R folder while making additional checks.

Enhances the R Optimization Infrastructure ('ROI') package with the NLopt solver for solving nonlinear optimization problems.

This package provides methods for calculation and visualization of the Repertoire Dissimilarity Index. Citation: Bolen and Rubelt, et al (2017) <doi:10.1186/s12859-017-1556-5>.

Facilitates making a connection to the Revenera API and executing various queries. You can use it to get event data and metadata. The Revenera documentation is available at <https://rui-api.redoc.ly/>. This package is not supported by Flexera (owner of the software).

This package provides a framework with tools to compare two random variables via stochastic dominance. See the README.md at <https://github.com/EtorArza/RVCompare> for a quick start guide. It can compute the Cp and Cd of two probability distributions and the Cumulative Difference Plot as explained in E. Arza (2022) <doi:10.1080/10618600.2022.2084405>. Uses bootstrap or DKW-bounds to compute the confidence bands of the cumulative distributions. These two methods are described in B. Efron. (1979) <doi:10.1214/aos/1176344552> and P. Massart (1990) <doi:10.1214/aop/1176990746>.

Convert one biological ID to another of rice (Oryza sativa). Rice(Oryza sativa) has more than one form gene ID for the genome. The two main gene ID for rice genome are the RAP (The Rice Annotation Project, <https://rapdb.dna.affrc.go.jp/>, and the MSU(The Rice Genome Annotation Project, <http://rice.plantbiology.msu.edu/>. All RAP rice gene IDs are of the form Os##g####### as explained on the website <https://rapdb.dna.affrc.go.jp/>. All MSU rice gene IDs are of the form LOC_Os##g##### as explained on the website <http://rice.plantbiology.msu.edu/analyses_nomenclature.shtml>. All SYMBOL rice gene IDs are the unique name on the NCBI(National Center for Biotechnology Information, <https://www.ncbi.nlm.nih.gov/>. The TRANSCRIPTID, is the transcript id of rice, are of the form Os##t#######. The researchers usually need to converter between various IDs. Such as converter RAP to SYMBOLS for function searching on NCBI. There are a lot of websites with the function for converting RAP to MSU or MSU to RA, such as ID Converter <https://rapdb.dna.affrc.go.jp/tools/converter>. But it is difficult to convert super multiple IDs on these websites. The package can convert all IDs between the three IDs (RAP, MSU and SYMBOL) regardless of the number.

Analyze download logs from the CRAN RStudio mirror (<http://cran.rstudio.com/>). This CRAN mirror is the default one used in RStudio. The available data is the result of parsed and anonymised raw log data from that CRAN mirror.

Reads in text from unstructured modern Microsoft Office files (XML based files) such as Word and PowerPoint. This does not read in structured data (from Excel or Access) as there are many other great packages to that do so already.

This package provides functionality to read files containing observations which consist of arbitrary key/value pairs.

Slow Feature Analysis (SFA), ported to R based on matlab implementations of SFA: SFA toolkit 1.0 by Pietro Berkes and SFA toolkit 2.8 by Wolfgang Konen.

This package provides a data mining approach for longitudinal and clustered data, which combines the structure of mixed effects model with tree-based estimation methods. See Sela, R.J. and Simonoff, J.S. (2012) RE-EM trees: a data mining approach for longitudinal and clustered data <doi:10.1007/s10994-011-5258-3>.

Routines to interact with the Numerai Machine Learning Tournament API <https://numer.ai>. The functionality includes the ability to automatically download the current tournament data, submit predictions, and to get information for your user.

Access data stored in REDCap databases using the Application Programming Interface (API). REDCap (Research Electronic Data CAPture; <https://projectredcap.org>, Harris, et al. (2009) <doi:10.1016/j.jbi.2008.08.010>, Harris, et al. (2019) <doi:10.1016/j.jbi.2019.103208>) is a web application for building and managing online surveys and databases developed at Vanderbilt University. The API allows users to access data and project meta data (such as the data dictionary) from the web programmatically. The redcapAPI package facilitates the process of accessing data with options to prepare an analysis-ready data set consistent with the definitions in a database's data dictionary.

Animal movement models including Moving-Resting Process with Embedded Brownian Motion (Yan et al., 2014, <doi:10.1007/s10144-013-0428-8>; Pozdnyakov et al., 2017, <doi:10.1007/s11009-017-9547-6>), Brownian Motion with Measurement Error (Pozdnyakov et al., 2014, <doi:10.1890/13-0532.1>), Moving-Resting-Handling Process with Embedded Brownian Motion (Pozdnyakov et al., 2020, <doi:10.1007/s11009-020-09774-1>), Moving-Resting Process with Measurement Error (Hu et al., 2021, <doi:10.1111/2041-210X.13694>), Moving-Moving Process with two Embedded Brownian Motions.

Spike and slab for prediction and variable selection in linear regression models. Uses a generalized elastic net for variable selection.

This package provides methods for computing spatial, temporal, and spatiotemporal statistics as described in Gouhier and Guichard (2014) <doi:10.1111/2041-210X.12188>. These methods include empirical univariate, bivariate and multivariate variograms; fitting variogram models; phase locking and synchrony analysis; generating autocorrelated and cross-correlated matrices.

Transforms or simulates data with a target empirical covariance matrix supplied by the user. The method to obtain the data with the target empirical covariance matrix is described in Section 5.1 of Christidis, Van Aelst and Zamar (2019) <arXiv:1812.05678>.

Facilitates basic and equation-based analyses of some important soil properties related to soil chemical environment and nutrient availability to plants. Freundlich H (1907). <doi:10.1515/zpch-1907-5723>. Datta SP, Bhadoria PBS (1999). <doi:10.1002%2F%28SICI%291522-2624%28199903%29162%3A2%3C183%3A%3AAID-JPLN183%3E3.0.CO%3B2-A>."Boron adsorption and desorption in some acid soils of West Bengal, India". Langmuir I (1918). <doi:10.1021/ja02242a004> "The adsorption of gases on plane surfaces of glass, mica, and platinum". Khasawneh FE (1971). <doi:10.2136/sssaj1971.03615995003500030029x> "Solution ion activity and plant growth".

We provide a suite of tools for estimating the sample complexity of a chosen model through theoretical bounds and simulation. The package incorporates methods for estimating the Vapnik-Chervonenkis dimension (VCD) of a chosen algorithm, which can be used to estimate its sample complexity. Alternatively, we provide simulation methods to estimate sample complexity directly. For more details, see Carter, P & Choi, D (2024). "Learning from Noise: Applying Sample Complexity for Political Science Research" <doi:10.31219/osf.io/evrcj>.

Approximate Bayesian regularization using Gaussian approximations. The input is a vector of estimates and a Gaussian error covariance matrix of the key parameters. Bayesian shrinkage is then applied to obtain parsimonious solutions. The method is described on Karimova, van Erp, Leenders, and Mulder (2024) <DOI:10.31234/osf.io/2g8qm>. Gibbs samplers are used for model fitting. The shrinkage priors that are supported are Gaussian (ridge) priors, Laplace (lasso) priors (Park and Casella, 2008 <DOI:10.1198/016214508000000337>), and horseshoe priors (Carvalho, et al., 2010; <DOI:10.1093/biomet/asq017>). These priors include an option for grouped regularization of different subsets of parameters (Meier et al., 2008; <DOI:10.1111/j.1467-9868.2007.00627.x>). F priors are used for the penalty parameters lambda^2 (Mulder and Pericchi, 2018 <DOI:10.1214/17-BA1092>). This correspond to half-Cauchy priors on lambda (Carvalho, Polson, Scott, 2010 <DOI:10.1093/biomet/asq017>).

Add-on for the scan package that creates plots from single-case data frames ('scdf'). It includes functions for styling single-case plots, adding phase-based lines to indicate various statistical parameters, and predefined themes for presentations and publications. More information and in depth examples can be found in the online book "Analyzing Single-Case Data with R and scan" Jürgen Wilbert (2025) <https://jazznbass.github.io/scan-Book/>.