Bayesian data analysis usually incurs long runtimes and cumbersome custom code. A pipeline toolkit tailored to Bayesian statisticians, the jagstargets R package is leverages targets and R2jags to ease this burden. jagstargets makes it super easy to set up scalable JAGS pipelines that automatically parallelize the computation and skip expensive steps when the results are already up to date. Minimal custom code is required, and there is no need to manually configure branching, so usage is much easier than targets alone. For the underlying methodology, please refer to the documentation of targets <doi:10.21105/joss.02959> and JAGS (Plummer 2003) <https://www.r-project.org/conferences/DSC-2003/Proceedings/Plummer.pdf>.

Automatic disaggregation of small-area population estimates by demographic groups (e.g., age, sex, race, marital status, educational level, etc) along with the estimates of uncertainty, using advanced Bayesian statistical modelling approaches based on integrated nested Laplace approximation (INLA) Rue et al. (2009) <doi:10.1111/j.1467-9868.2008.00700.x> and stochastic partial differential equation (SPDE) methods Lindgren et al. (2011) <doi:10.1111/j.1467-9868.2011.00777.x>. The package implements hierarchical Bayesian modeling frameworks for small area estimation as described in Leasure et al. (2020) <doi:10.1073/pnas.1913050117> and Nnanatu et al. (2025) <doi:10.1038/s41467-025-59862-4>.

Minimal and memory efficient implementation of the junction tree algorithm using the Lauritzen-Spiegelhalter scheme; S. L. Lauritzen and D. J. Spiegelhalter (1988) <https://www.jstor.org/stable/2345762?seq=1>. The jti package is part of the paper <doi:10.18637/jss.v111.i02>.

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).

This package provides a set of helper functions to conduct joint-significance tests for mediation analysis, as recommended by Yzerbyt, Muller, Batailler, & Judd. (2018) <doi:10.1037/pspa0000132>.

Fitting and analyzing a Joint Trait Distribution Model. The Joint Trait Distribution Model is implemented in the Bayesian framework using conjugate priors and posteriors, thus guaranteeing fast inference. In particular the package computes joint probabilities and multivariate confidence intervals, and enables the investigation of how they depend on the environment through partial response curves. The method implemented by the package is described in Poggiato et al. (2023) <doi:10.1111/geb.13706>.

This package performs a permutation test on the difference between two location parameters, a permutation correlation test, a permutation F-test, the Siegel-Tukey test, a ratio mean deviance test. Also performs some graphing techniques, such as for confidence intervals, vector addition, and Fourier analysis; and includes functions related to the Laplace (double exponential) and triangular distributions. Performs power calculations for the binomial test.

Since the reference management software (such as Zotero', Mendeley') exports Bib file journal abbreviation is not detailed enough, the journalabbr package only abbreviates the journal field of Bib file, and then outputs a new Bib file for generating reference format with journal abbreviation on other software (such as texstudio'). The abbreviation table is from JabRef'. At the same time, Shiny application is provided to generate thebibliography', a reference format that can be directly used for latex paper writing based on Rmd files.

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.

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>.

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/>).

Joint analysis and imputation of incomplete data in the Bayesian framework, using (generalized) linear (mixed) models and extensions there of, survival models, or joint models for longitudinal and survival data, as described in Erler, Rizopoulos and Lesaffre (2021) <doi:10.18637/jss.v100.i20>. Incomplete covariates, if present, are automatically imputed. The package performs some preprocessing of the data and creates a JAGS model, which will then automatically be passed to JAGS <https://mcmc-jags.sourceforge.io/> with the help of the package rjags'.

In a typical experiment for the intuitive judgment of frequencies (JoF) different stimuli with different frequencies are presented. The participants consider these stimuli with a constant duration and give a judgment of frequency. These judgments can be simulated by formal models: PASS 1 and PASS 2 based on Sedlmeier (2002, ISBN:978-0198508632), MINERVA 2 baesd on Hintzman (1984) <doi:10.3758/BF03202365> and TODAM 2 based on Murdock, Smith & Bai (2001) <doi:10.1006/jmps.2000.1339>. The package provides an assessment of the frequency by determining the core aspects of these four models (attention, decay, and presented frequency) that can be compared to empirical results.

Option is a one of the financial derivatives and its pricing is an important problem in practice. The process of stock prices are represented as Geometric Brownian motion [Black (1973) <doi:10.1086/260062>] or jump diffusion processes [Kou (2002) <doi:10.1287/mnsc.48.8.1086.166>]. In this package, algorithms and visualizations are implemented by Monte Carlo method in order to calculate European option price for three equations by Geometric Brownian motion and jump diffusion processes and furthermore a model that presents jumps among companies affect each other.

Metaprogramming utilities for converting R regression model formulae to equivalents in Julia <doi:10.1137/141000671>, via modifications to the abstract syntax tree. Supports translations in zero correlation random effects syntax, protection of expressions to be evaluated as-is, interaction terms, and more. Accepts strings or R formula objects and returns modified R formula objects where possible (or a modified string, if not a valid formula in R).

This package provides tools are provided to streamline Bayesian analyses in JAGS using the jagsUI package. Included are functions for extracting output in simpler format, functions for streamlining assessment of convergence, and functions for producing summary plots of output. Also included is a function that provides a simple template for running JAGS from R'. Referenced materials can be found at <DOI:10.1214/ss/1177011136>.

Evaluation of the Jacobi theta functions and related functions: Weierstrass elliptic function, Weierstrass sigma function, Weierstrass zeta function, Klein j-function, Dedekind eta function, lambda modular function, Jacobi elliptic functions, Neville theta functions, Eisenstein series, lemniscate elliptic functions, elliptic alpha function, Rogers-Ramanujan continued fractions, and Dixon elliptic functions. Complex values of the variable are supported.

Proposes a coarse-to-fine optimization of a recommending system based on deep-neural networks using tensorflow'.

This package implements under/oversampling for probability estimation. To be used with machine learning methods such as AdaBoost, random forests, etc.

Install packages without attaching them. If a package it is already installed, it will be skipped.

This package provides a highly configurable jQuery plugin offering a simple interface to create complex queries/filters in Shiny'. The outputted rules can easily be parsed into a set of R and/or SQL queries and used to filter data. Custom parsing of the rules is also supported. For more information about jQuery QueryBuilder see <https://querybuilder.js.org/>.

Fit latent space network cluster models using an expectation-maximization algorithm. Enables flexible modeling of unweighted or weighted network data (with or without noise edges), supporting both directed and undirected networks (with or without degree and strength heterogeneity). Designed to handle large networks efficiently, it allows users to explore network structure through latent space representations, identify clusters (i.e., community detection) within network data, and simulate networks with varying clustering, connectivity patterns, and noise edges. Methodology for the implementation is described in Arakkal and Sewell (2025) <doi:10.1016/j.csda.2025.108228>.

Maximum likelihood estimation for the semiparametric joint modeling of survival and longitudinal data. Refer to the Journal of Statistical Software article: <doi:10.18637/jss.v093.i02>.

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.