Interface to JSON-stat <https://json-stat.org/>, a simple lightweight JSON format for data dissemination.

The main purpose of this package is to make it easy for userR's to interact with jMetrik an open source application for psychometric analysis. For example it allows useR's to write data frames to file in a format that can be used by jMetrik'. It also allows useR's to read *.jmetrik files (e.g. output from an analysis) for follow-up analysis in R. The *.jmetrik format is a flat file that includes a multiline header and the data as comma separated values. The header includes metadata about the file and one row per variable with the following information in each row: variable name, data type, item scoring, special data codes, and variable label.

This package provides a modern, tidy interface to the Jira REST API for both Jira Cloud and Jira Server / Data Center. Authenticate once, query issues with the Jira Query Language (JQL), and retrieve projects, fields, dashboards and more as tibbles. Built on httr2 with automatic pagination, informative errors and support for API tokens and personal access tokens.

This package provides a comprehensive computational framework for joint mapping, developed by Li (2016) <doi:10.11841/j.issn.1007-4333.2016.06.002>, supports quantitative trait locus detection in structured genetic populations. It integrates robust phenotype summarization, computes genotype probabilities, and imputes missing markers for association and linkage mapping. Empirical significance thresholds are estimated via permutation testing coupled with stepwise regression. The framework enables genome-wide scans under both univariate and multivariate trait models, streamlining the discovery of complex genetic architectures.

Simply and efficiently simulates (i) variants from reference genomes and (ii) reads from both Illumina <https://www.illumina.com/> and Pacific Biosciences (PacBio) <https://www.pacb.com/> platforms. It can either read reference genomes from FASTA files or simulate new ones. Genomic variants can be simulated using summary statistics, phylogenies, Variant Call Format (VCF) files, and coalescent simulationsâ the latter of which can include selection, recombination, and demographic fluctuations. jackalope can simulate single, paired-end, or mate-pair Illumina reads, as well as PacBio reads. These simulations include sequencing errors, mapping qualities, multiplexing, and optical/polymerase chain reaction (PCR) duplicates. Simulating Illumina sequencing is based on ART by Huang et al. (2012) <doi:10.1093/bioinformatics/btr708>. PacBio sequencing simulation is based on SimLoRD by Stöcker et al. (2016) <doi:10.1093/bioinformatics/btw286>. All outputs can be written to standard file formats.

JSON-LD <https://www.w3.org/TR/json-ld/> is a light-weight syntax for expressing linked data. It is primarily intended for web-based programming environments, interoperable web services and for storing linked data in JSON-based databases. This package provides bindings to the JavaScript library for converting, expanding and compacting JSON-LD documents.

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).

Takes an R expression and returns a job object with a $stop() method which can be called to terminate the background job. Also provides timeouts and other mechanisms for automatically terminating a background job. The result of the expression is available synchronously via $result or asynchronously with callbacks or through the promises package framework.

Set of common functions used for manipulating colors, detecting and interacting with RStudio', modeling, formatting, determining users operating system, feature scaling, and more!

This package implements interpretable multi-biomarker fusion in joint longitudinal-survival models via semi-parametric association surfaces. Provides a two-stage estimation framework where Stage 1 fits mixed-effects longitudinal models and extracts Best Linear Unbiased Predictors ('BLUP's), and Stage 2 fits transition-specific penalized Cox models with tensor-product spline surfaces linking latent biomarker summaries to transition hazards. Supports multi-state disease processes with transition-specific surfaces, Restricted Maximum Likelihood ('REML') smoothing parameter selection, effective degrees of freedom ('EDF') diagnostics, dynamic prediction of transition probabilities, and three interpretability visualizations (surface plots, contour heatmaps, marginal effect slices). Methods are described in Bhattacharjee (2025, under review).

Offer procedures to download financial-economic time series data and enhanced procedures for computing the investment performance indices of Bacon (2004) <DOI:10.1002/9781119206309>.

This package provides functions to extract joint planes from 3D triangular mesh derived from point cloud and makes data available for structural analysis.

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 performs power calculations for joint modeling of longitudinal and survival data with k-th order trajectories when the variance-covariance matrix, Sigma_theta, is unknown.

All datasets and functions used in the german book "Statistik mit R und RStudio" by grosse Schlarmann (2010-2024) <https://www.produnis.de/R/>.

Lints LaTeX'/'BibTeX manuscripts against the Journal of Statistical Software (JSS) style guide. Wraps the same Rust rule engine used by the standalone jsslint binary, the browser/'WASM build, and the Python binding, exposed to R via extendr'.

Create and customize interactive trees using the jQuery jsTree <https://www.jstree.com/> plugin library and the htmlwidgets package. These trees can be used directly from the R console, from RStudio', in Shiny apps and R Markdown documents.

This package provides a mainly instrumental package meant to allow other packages whose core is written in C++ to read, write and manipulate matrices in a binary format so that the memory used for them is no more than strictly needed. Its functionality is already inside parallelpam and scellpam', so if you have installed any of these, you do not need to install jmatrix'. Using just the needed memory is not always true with R matrices or vectors, since by default they are of double type. Trials like the float package have been done, but to use them you have to coerce a matrix already loaded in R memory to a float matrix, and then you can delete it. The problem comes when your computer has not memory enough to hold the matrix in the first place, so you are forced to load it by chunks. This is the problem this package tries to address (with partial success, but this is a difficult problem since R is not a strictly typed language, which is anyway quite hard to get in an interpreted language). This package allows the creation and manipulation of full, sparse and symmetric matrices of any standard data type.

This package provides an interface to Jamendo API <https://developer.jamendo.com/v3.0>. Pull audio, features and other information for a given Jamendo user (including yourself!) or enter an artist's -, album's -, or track's name and retrieve the available information in seconds.

This package provides a GUI interface for automating data extraction from multiple images containing scatter and bar plots, semi-automated tools to tinker with extraction attempts, and a fully-loaded point-and-click manual extractor with image zoom, calibrator, and classifier. Also provides detailed and R-independent extraction reports as fully-embedded .html records.

This package provides a framework for creating rich interactive analyses for the jamovi platform (see <https://www.jamovi.org> for more information).

This package provides tools to use the e-Stat API (<https://www.e-stat.go.jp/>), the portal site for Japanese government statistics.

Analysis of repeated measurements and time-to-event data via random effects joint models. Fits the joint models proposed by Henderson and colleagues <doi:10.1093/biostatistics/1.4.465> (single event time) and by Williamson and colleagues (2008) <doi:10.1002/sim.3451> (competing risks events time) to a single continuous repeated measure. The time-to-event data is modelled using a (cause-specific) Cox proportional hazards regression model with time-varying covariates. The longitudinal outcome is modelled using a linear mixed effects model. The association is captured by a latent Gaussian process. The model is estimated using an Expectation Maximization algorithm. Some plotting functions and the variogram are also included. This project is funded by the Medical Research Council (Grant numbers G0400615 and MR/M013227/1).

An estimation method that can use computer simulations to approximate maximum-likelihood estimates even when the likelihood function can not be evaluated directly. It can be applied whenever it is feasible to conduct many simulations, but works best when the data is approximately Poisson distributed. It was originally designed for demographic inference in evolutionary biology (Naduvilezhath et al., 2011 <doi:10.1111/j.1365-294X.2011.05131.x>, Mathew et al., 2013 <doi:10.1002/ece3.722>). It has optional support for conducting coalescent simulation using the coala package.