Allows interaction with Interactive Brokers Trader Workstation <https://interactivebrokers.github.io/tws-api/>. Handles the connection over the network and the exchange of messages. Data is encoded and decoded between user and wire formats. Data structures and functionality closely mirror the official implementations.

Network-based regularization has achieved success in variable selection for high-dimensional biological data due to its ability to incorporate correlations among genomic features. This package provides procedures of network-based variable selection for generalized linear models (Ren et al. (2017) <doi:10.1186/s12863-017-0495-5> and Ren et al.(2019) <doi:10.1002/gepi.22194>). Continuous, binary, and survival response are supported. Robust network-based methods are available for continuous and survival responses.

Encrypt R objects to a raw vector or file using modern cryptographic techniques. Password-based key derivation is with Argon2 (<https://en.wikipedia.org/wiki/Argon2>). Objects are serialized and then encrypted using XChaCha20-Poly1305 (<https://en.wikipedia.org/wiki/ChaCha20-Poly1305>) which follows RFC 8439 for authenticated encryption (<https://en.wikipedia.org/wiki/Authenticated_encryption>). Cryptographic functions are provided by the included monocypher C library (<https://monocypher.org>).

An implementation of Bayesian online changepoint detection (Adams and MacKay (2007) <doi:10.48550/arXiv.0710.3742>) with an option for probability based outlier detection and removal (Wendelberger et. al. (2021) <doi:10.48550/arXiv.2112.12899>). Building on the independent multivariate constant mean model implemented in the R package ocp', this package models multivariate data as multivariate normal about a linear trend, defined by user input covariates, with an unstructured error covariance. Changepoints are identified based on a probability threshold for windows of points.

Rcpp bindings to the native C++ implementation of MS Numpress, that provides two compression schemes for numeric data from mass spectrometers. The library provides implementations of 3 different algorithms, 1 designed to compress first order smooth data like retention time or M/Z arrays, and 2 for compressing non smooth data with lower requirements on precision like ion count arrays. Refer to the publication (Teleman et al., (2014) <doi:10.1074/mcp.O114.037879>) for more details.

This package provides methods to compute chemical similarity between two or more reactions and molecules. Allows masking of chemical substructures for weighted similarity computations. Uses packages rCDK and fingerprint for cheminformatics functionality. Methods for reaction similarity and sub-structure masking are as described in: Giri et al. (2015) <doi:10.1093/bioinformatics/btv416>.

This package provides a collection of palettes designed to integrate with ggplot', reflecting the color schemes associated with ConesaLab'.

Relative, generalized, and Erreygers corrected concentration index; plot Lorenz curves; and decompose health inequalities into contributing factors. The package currently works with (generalized) linear models, survival models, complex survey models, and marginal effects probit models. originally forked by Brecht Devleesschauwer from the decomp package (no longer on CRAN), rineq is now maintained by Kaspar Walter Meili. Compared to the earlier rineq version on github by Brecht Devleesschauwer (<https://github.com/brechtdv/rineq>), the regression tree functionality has been removed. Improvements compared to earlier versions include improved plotting of decomposition and concentration, added functionality to calculate the concentration index with different methods, calculation of robust standard errors, and support for the decomposition analysis using marginal effects probit regression models. The development version is available at <https://github.com/kdevkdev/rineq>.

Adds subtotal rows / sections (a la the SAS Proc Tabulate All option) to a Group By output by running a series of Group By functions with partial sets of the same variables and combining the results with the original. Can be used to add comprehensive information to a data report or to quickly aggregate Group By outputs used to gain a greater understanding of data.

The TRUST4 or MiXCR is used to identify the clonotypes. The goal of rTCRBCRr is to process the results from these clonotyping tools, and analyze the clonotype repertoire metrics based on chain names and IGH isotypes. The manuscript is still under preparation for publication for now. The references describing the methods in this package will be added later.

Carry out principal component analysis (PCA) of very large pedigrees such as found in breeding populations! This package exploits sparse matrices and randomised linear algebra to deliver a gazillion-times speed-up compared to naive singular value decoposition (SVD) (and eigen decomposition).

After defining an R6 class, R62S3 is used to automatically generate optional S3/S4 generics and methods for dispatch. Also allows piping for R6 objects.

Used for generating randomized community matrices under strict range cohesion. The package can handle data where species occurrence are recorded across sites ordered along gradients such as elevation and latitude, as well as species occurrences recorded on spatial grids with known geographic coordinates.

Uses an indirect method based on truncated quantile-quantile plots to estimate reference limits from routine laboratory data: Georg Hoffmann and colleagues (2024) <doi: 10.3390/jcm13154397>. The principle of the method was developed by Robert G Hoffmann (1963) <doi:10.1001/jama.1963.03060110068020> and modified by Georg Hoffmann and colleagues (2015) <doi:10.1515/labmed-2015-0104>, and Frank Klawonn and colleagues (2020) <doi:10.1515/labmed-2020-0005>, (2022) <doi:10.1007/978-3-031-15509-3_31>.

Machine learning and visualization package with an S7 backend featuring comprehensive type checking and validation, paired with an efficient functional user-facing API. train(), cluster(), and decomp() provide one-call access to supervised and unsupervised learning. All configuration steps are performed using setup functions and validated. A single call to train() handles preprocessing, hyperparameter tuning, and testing with nested resampling. Supports data.frame', data.table', and tibble inputs, parallel execution, and interactive visualizations. The package first appeared in E.D. Gennatas (2017) <https://repository.upenn.edu/entities/publication/d81892ea-3087-4b71-a6f5-739c58626d64>.

Reports errors and messages to Rollbar, the error tracking platform <https://rollbar.com>.

Accessible and flexible implementation of three ecoacoustic indices that are less commonly available in existing R frameworks: Background Noise, Soundscape Power and Soundscape Saturation. The functions were design to accommodate a variety of sampling designs. Users can tailor calculations by specifying spectrogram time bin size, amplitude thresholds and normality tests. By simplifying computation and standardizing reproducible methods, the package aims to support ecoacoustics studies. For more details about the indices read Towsey (2017) <https://eprints.qut.edu.au/110634/> and Burivalova (2017) <doi:10.1111/cobi.12968>.

This package provides tools for generation of (life-history) traits and diversity maps on hexagonal or square grids. Valcu et al.(2012) <doi:10.1111/j.1466-8238.2011.00739.x>.

EZR (Easy R) adds a variety of statistical functions, including survival analyses, ROC analyses, metaanalyses, sample size calculation, and so on, to the R commander. EZR enables point-and-click easy access to statistical functions, especially for medical statistics. EZR is platform-independent and runs on Windows, Mac OS X, and UNIX. Its complete manual is available only in Japanese (Chugai Igakusha, ISBN: 978-4-498-10918-6, Nankodo, ISBN: 978-4-524-21861-5, Ohmsha, ISBN: 978-4-274-22632-8), but an report that introduced the investigation of EZR was published in Bone Marrow Transplantation (Nature Publishing Group) as an Open article. This report can be used as a simple manual. It can be freely downloaded from the journal website as shown below. This report has been cited in more than 14,000 scientific articles.

An ODBC database interface.

This package provides a collection of fast statistical and utility functions for data analysis. Functions for regression, maximum likelihood, column-wise statistics and many more have been included. C++ has been utilized to speed up the functions. References: Tsagris M., Papadakis M. (2018). Taking R to its limits: 70+ tips. PeerJ Preprints 6:e26605v1 <doi:10.7287/peerj.preprints.26605v1>.

This package provides a collection of shiny applications for the R package Luminescence'. These mainly, but not exclusively, include applications for plotting chronometric data from e.g. luminescence or radiocarbon dating. It further provides access to bootstraps tooltip and popover functionality and contains the jscolor.js library with a custom shiny output binding.

Automatic open data acquisition from resources of Polish Head Office of Geodesy and Cartography ('GŠówny UrzÄ d Geodezji i Kartografii') (<https://www.gov.pl/web/gugik>). Available datasets include various types of numeric, raster and vector data, such as orthophotomaps, digital elevation models (digital terrain models, digital surface model, point clouds), state register of borders, spatial databases, geometries of cadastral parcels, 3D models of buildings, and more. It is also possible to geocode addresses or objects using the geocodePL_get() function.

Measuring information flow between time series with Shannon and Rényi transfer entropy. See also Dimpfl and Peter (2013) <doi:10.1515/snde-2012-0044> and Dimpfl and Peter (2014) <doi:10.1016/j.intfin.2014.03.004> for theory and applications to financial time series. Additional references can be found in the theory part of the vignette.