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.

This package provides features that allow users to download weather data published by the Japan Meteorological Agency (JMA) website (<https://www.jma.go.jp/jma/index.html>). The data includes information dating back to 1976 and aligns with the categories available on the website. Additionally, users can process the best track data of typhoons and easily handle earthquake record files.

This package provides tools to explore and summarize relationship patterns between variables across one or multiple datasets. The package relies on efficient sampling strategies to estimate pairwise associations and supports quick exploratory data analysis for large or heterogeneous data sources.

Fits joint species distribution models ('jSDM') in a hierarchical Bayesian framework (Warton and al. 2015 <doi:10.1016/j.tree.2015.09.007>). The Gibbs sampler is written in C++'. It uses Rcpp', Armadillo and GSL to maximize computation efficiency.

Implementing a computationally scalable false discovery rate control procedure for replicability analysis based on maximum of p-values. Please cite the manuscript corresponding to this package [Lyu, P. et al., (2023), <doi:10.1093/bioinformatics/btad366>].

This package provides zero-copy R views of Julia-owned arrays by implementing ALTREP (Alternative Representations) classes that return pointers directly into Julia's memory. The package integrates with JuliaCall and uses C-level finalizers for safe cross-runtime garbage collection.

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

This package provides an R interface to Julia', which is a high-level, high-performance dynamic programming language for numerical computing, see <https://julialang.org/> for more information. It provides a high-level interface as well as a low-level interface. Using the high level interface, you could call any Julia function just like any R function with automatic type conversion. Using the low level interface, you could deal with C-level SEXP directly while enjoying the convenience of using a high-level programming language like Julia'.

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>.

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

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.

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.

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>.

This package provides a Wrapper for the Node.js Jdenticon <https://jdenticon.com/> Library. Uses esbuild <https://esbuild.github.io/> to reduce user dependencies.

Test for association between the observed data and their estimated latent variables. The jackstraw package provides a resampling strategy and testing scheme to estimate statistical significance of association between the observed data and their latent variables. Depending on the data type and the analysis aim, the latent variables may be estimated by principal component analysis (PCA), factor analysis (FA), K-means clustering, and related unsupervised learning algorithms. The jackstraw methods learn over-fitting characteristics inherent in this circular analysis, where the observed data are used to estimate the latent variables and used again to test against that estimated latent variables. When latent variables are estimated by PCA, the jackstraw enables statistical testing for association between observed variables and latent variables, as estimated by low-dimensional principal components (PCs). This essentially leads to identifying variables that are significantly associated with PCs. Similarly, unsupervised clustering, such as K-means clustering, partition around medoids (PAM), and others, finds coherent groups in high-dimensional data. The jackstraw estimates statistical significance of cluster membership, by testing association between data and cluster centers. Clustering membership can be improved by using the resulting jackstraw p-values and posterior inclusion probabilities (PIPs), with an application to unsupervised evaluation of cell identities in single cell RNA-seq (scRNA-seq).

Just analysis methods ('jam') base functions focused on bioinformatics. Version- and gene-centric alphanumeric sort, unique name and version assignment, colorized console and HTML output, color ramp and palette manipulation, Rmarkdown cache import, styled Excel worksheet import and export, interpolated raster output from smooth scatter and image plots, list to delimited vector, efficient list tools.

The free and open a statistical spreadsheet jamovi (<https://www.jamovi.org>) aims to make statistical analyses easy and intuitive. jamovi produces syntax that can directly be used in R (in connection with the R-package jmv'). Having import / export routines for the data files jamovi produces ('.omv') permits an easy transfer of data and analyses between jamovi and R.

Josa in Korean is often determined by judging the previous word. When writing reports using Rmd, a function that prints the appropriate investigation for each case is helpful. The josaplay package then evaluates the previous word to determine which josa is appropriate.

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.

Manage project dependencies from your DESCRIPTION file. Create a reproducible virtual environment with minimal additional files in your project. Provides tools to add, remove, and update dependencies as well as install existing dependencies with a single function.

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'.

This package implements the Merton (1976) <doi:10.1016/0304-405X(76)90022-2> and Kou (2002) <doi:10.1287/mnsc.48.8.1086.166> jump-diffusion models through a unified S4 object-oriented interface. Provides exact compound-Poisson asset price simulation, maximum likelihood parameter estimation with Hessian-based standard errors, Wald-type confidence intervals, European option pricing via the Merton analytic series expansion, and publication-quality diagnostic plots. All functionality operates entirely offline without market data dependencies.

This package creates interactive trees that can be included in Shiny apps and R markdown documents. A tree allows to represent hierarchical data (e.g. the contents of a directory). Similar to the shinyTree package but offers more features and options, such as the grid extension, restricting the drag-and-drop behavior, and settings for the search functionality. It is possible to attach some data to the nodes of a tree and then to get these data in Shiny when a node is selected. Also provides a Shiny gadget allowing to manipulate one or more folders, and a Shiny module allowing to navigate in the server side file system.

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