Different algorithms to perform approximate joint diagonalization of a finite set of square matrices. Depending on the algorithm, orthogonal or non-orthogonal diagonalizer is found. These algorithms are particularly useful in the context of blind source separation. Original publications of the algorithms can be found in Ziehe et al. (2004), Pham and Cardoso (2001) <doi:10.1109/78.942614>, Souloumiac (2009) <doi:10.1109/TSP.2009.2016997>, Vollgraff and Obermayer <doi:10.1109/TSP.2006.877673>. An example of application in the context of Brain-Computer Interfaces EEG denoising can be found in Gouy-Pailler et al (2010) <doi:10.1109/TBME.2009.2032162>.

This package provides a suite of common statistical methods such as descriptives, t-tests, ANOVAs, regression, correlation matrices, proportion tests, contingency tables, and factor analysis. This package is also useable from the jamovi statistical spreadsheet (see <https://www.jamovi.org> for more information).

Generates interactive Jellyfish plots to visualize spatiotemporal tumor evolution by integrating sample and phylogenetic trees into a unified plot. This approach provides an intuitive way to analyze tumor heterogeneity and evolution over time and across anatomical locations. The Jellyfish plot visualization design was first introduced by Lahtinen, Lavikka, et al. (2023, <doi:10.1016/j.ccell.2023.04.017>). This package also supports visualizing ClonEvol results, a tool developed by Dang, et al. (2017, <doi:10.1093/annonc/mdx517>), for analyzing clonal evolution from multi-sample sequencing data. The clonevol package is not available on CRAN but can be installed from its GitHub repository (<https://github.com/hdng/clonevol>).

Fits the joint model proposed by Henderson and colleagues (2000) <doi:10.1093/biostatistics/1.4.465>, but extended to the case of multiple continuous longitudinal measures. The time-to-event data is modelled using a Cox proportional hazards regression model with time-varying covariates. The multiple longitudinal outcomes are modelled using a multivariate version of the Laird and Ware linear mixed model. The association is captured by a multivariate latent Gaussian process. The model is estimated using a Monte Carlo Expectation Maximization algorithm. This project was funded by the Medical Research Council (Grant number MR/M013227/1).

The goal of jetty is to execute R functions and code snippets in an isolated R subprocess within a Docker container and return the evaluated results to the local R session. jetty can install necessary packages at runtime and seamlessly propagates errors and outputs from the Docker subprocess back to the main session. jetty is primarily designed for sandboxed testing and quick execution of example code.

Helpful functions for using mesh code (80km to 100m) data in Japan. Visualize mesh code using ggplot2 and leaflet', etc.

Allows to import functions and whole packages from Julia in R. Imported Julia functions can directly be called as R functions. Data structures can be translated between Julia and R. More details can also be found in the corresponding article <doi:10.18637/jss.v101.i06>.

Some handy function in R.

Reproducible work requires a record of where every statistic originated. When writing reports, some data is too big to load in the same environment and some statistics take a while to compute. This package offers a way to keep notes on statistics, simple functions, and small objects. Notepads can be locked to avoid accidental updates. Notepads keep track of who added the notes and when the notes were added. A simple text representation is used to allow for clear version histories.

This is a set of simple utility functions to perform mutual conversion between the current Japanese calendar system that Wareki, the old Japanese calendar system that the Kyureki calendar and the Julian and Gregorian calendar. To calculate each calendar method, it converts to the Julian Day Number.

This package provides functions and data to reproduce all plots in the book "Practical Smoothing. The Joys of P-splines" by Paul H.C. Eilers and Brian D. Marx (2021, ISBN:978-1108482950).

This package provides a set of utilities for working with JavaScript syntax in R. Includes tools to parse, tokenize, compile, validate, reformat, optimize and analyze JavaScript code.

An httpuv based bridge between R and JavaScript'. Provides an easy way to exchange commands and data between a web page and a currently running R session.

This package provides methods to access data sets from the jamovi statistical spreadsheet (see <https://www.jamovi.org> for more information) from R.

Bayesian methods for estimating developmental age from ordinal dental data. For an explanation of the model used, see Konigsberg (2015) <doi:10.3109/03014460.2015.1045430>. For details on the conditional correlation correction, see Sgheiza (2022) <doi:10.1016/j.forsciint.2021.111135>. Dental scoring is based on Moorrees, Fanning, and Hunt (1963) <doi:10.1177/00220345630420062701>.

Uses least squares optimisation to estimate the parameters of the best-fitting JohnsonSU distribution for a given dataset, with the possibility of the distributions corresponding to the limiting cases of the JohnsonSU distribution. The code for the Golden Section Search used in the optimisation has been adapted from E. Cai. This package has been created as an extension of my Master's thesis. E. Cai (2013, "Scripts and Functions: Using R to Implement the Golden Section Search Method for Numerical Optimization", <https://chemicalstatistician.wordpress.com/2013/04/22/using-r-to-implement-the-golden-bisection-method/>).

The jscore() function in the package calculates the J-Score metric between two clustering assignments. The score is designed to address some problems with existing common metrics such as problem of matching. The details of J-score is described in Ahmadinejad and Liu. (2021) <arXiv:2109.01306>.

Encode/Decode base64', with support for JSON format, using two functions: j_encode() and j_decode(). Base64 is a group of similar binary-to-text encoding schemes that represent binary data in an ASCII string format by translating it into a radix-64 representation, used when there is a need to encode binary data that needs to be stored and transferred over media that are designed to deal with textual data, ensuring that the data will remain intact and without modification during transport. <https://developer.mozilla.org/en-US/docs/Web/API/WindowBase64/Base64_encoding_and_decoding> On the other side, JSON (JavaScript Object Notation) is a lightweight data-interchange format. Easy to read, write, parse and generate. It is based on a subset of the JavaScript Programming Language. JSON is a text format that is completely language independent but uses conventions that are familiar to programmers of the C-family of languages, including C, C++, C#, Java, JavaScript, Perl, Python, and many others. JSON structure is built around name:value pairs and ordered list of values. <https://www.json.org> The first function, j_encode(), let you transform a data.frame or list to a base64 encoded JSON (or JSON string). The j_decode() function takes a base64 string (could be an encoded JSON) and transform it to a data.frame (or list, depending of the JSON structure).

The function jskm() creates publication quality Kaplan-Meier plot with at risk tables below. svyjskm() provides plot for weighted Kaplan-Meier estimator.

This is a collection of tools for more efficiently understanding and sharing the results of (primarily) regression analyses. There are also a number of miscellaneous functions for statistical and programming purposes. Support for models produced by the survey and lme4 packages are points of emphasis.

Joint mean and dispersion effects models fit the mean and dispersion parameters of a response variable by two separate linear models, the mean and dispersion submodels, simultaneously. It also allows the users to choose either the deviance or the Pearson residuals as the response variable of the dispersion submodel. Furthermore, the package provides the possibility to nest the submodels in one another, if one of the parameters has significant explanatory power on the other. Wu & Li (2016) <doi:10.1016/j.csda.2016.04.015>.

This package provides a Jordan algebra is an algebraic object originally designed to study observables in quantum mechanics. Jordan algebras are commutative but non-associative; they satisfy the Jordan identity. The package follows the ideas and notation of K. McCrimmon (2004, ISBN:0-387-95447-3) "A Taste of Jordan Algebras". To cite the package in publications, please use Hankin (2023) <doi:10.48550/arXiv.2303.06062>.

The function get_parameters() is intended to be used within a docker container to read keyword arguments from a .json file automagically. A tool.yaml file contains specifications on these keyword arguments, which are then passed as input to containerized R tools in the [tool-runner framework](<https://github.com/hydrocode-de/tool-runner>). A template for a containerized R tool, which can be used as a basis for developing new tools, is available at the following URL: <https://github.com/VForWaTer/tool_template_r>.

This package provides a function collection to extract metadata, sectioned text and study characteristics from scientific articles in NISO-JATS format. Articles in PDF format can be converted to NISO-JATS with the Content ExtRactor and MINEr ('CERMINE', <https://github.com/CeON/CERMINE>). For convenience, two functions bundle the extraction heuristics: JATSdecoder() converts NISO-JATS'-tagged XML files to a structured list with elements title, author, journal, history, DOI', abstract, sectioned text and reference list. study.character() extracts multiple study characteristics like number of included studies, statistical methods used, alpha error, power, statistical results, correction method for multiple testing, software used. The function get.stats() extracts all statistical results from text and recomputes p-values for many standard test statistics. It performs a consistency check of the reported with the recalculated p-values. An estimation of the involved sample size is performed based on textual reports within the abstract and the reported degrees of freedom within statistical results. In addition, the package contains some useful functions to process text (text2sentences(), text2num(), ngram(), strsplit2(), grep2()). See Böschen, I. (2021) <doi:10.1007/s11192-021-04162-z> Böschen, I. (2021) <doi:10.1038/s41598-021-98782-3>, Böschen, I. (2023) <doi:10.1038/s41598-022-27085-y>, and Böschen, I. (2024) <doi:10.48550/arXiv.2408.07948>.