This package provides a new class of Bayesian meta-analysis models that incorporates a model for internal and external validity bias. In this way, it is possible to combine studies of diverse quality and different types. For example, we can combine the results of randomized control trials (RCTs) with the results of observational studies (OS).

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

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'.

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>.

This package implements an S4 distribution system and estimation methods for parameters of common distribution families. The common d, p, q, r function family for each distribution is enriched with the ll, e, and v counterparts, computing the log-likelihood, performing estimation, and calculating the asymptotic variance - covariance matrix, respectively. Parameter estimation is performed analytically whenever possible.

This package provides functions to access data from public RESTful APIs including Nager.Date', World Bank API', and REST Countries API', retrieving real-time or historical data related to Japan, such as holidays, economic indicators, and international demographic and geopolitical indicators. Additionally, the package includes one of the largest curated collections of open datasets focused on Japan, covering topics such as natural disasters, economic production, vehicle industry, air quality, demographics, and administrative divisions. The package supports reproducible research and teaching by integrating reliable international APIs and structured datasets from public, academic, and government sources. For more information on the APIs, see: Nager.Date <https://date.nager.at/Api>, World Bank API <https://datahelpdesk.worldbank.org/knowledgebase/articles/889392>, and REST Countries API <https://restcountries.com/>.

Jade Lizard and Reverse Jade Lizard Option Strategies are presented here through their Graphs. The graphic indicators, strategies, calculations, functions and all the discussions are for academic, research, and educational purposes only and should not be construed as investment advice and come with absolutely no Liability. Russell A. Stultz (â The option strategy desk reference: an essential reference for option traders (First edition.)â , 2019, ISBN: 9781949443912).

Implementation of joint sparse optimization (JSparO) to infer the gene regulatory network for cell fate conversion. The proximal gradient method is implemented to solve different low-order regularization models for JSparO.

Jointly estimates two-group means and covariances for matrix-variate data and calculates test statistics. This package implements the algorithms defined in Hornstein, Fan, Shedden, and Zhou (2018) <doi:10.1080/01621459.2018.1429275>.

The age is estimated by calculating the Dirichlet Normal Energy (DNE) on the whole auricular surface and the apex of the auricular surface. It involves three estimation methods: principal component discriminant analysis (PCQDA), and principal component logistic regression analysis (PCLR) methods, principal component regression analysis with Southeast Asian (A_PCR), and principal component regression analysis with multipopulation (M_PCR). The package is created with the data from the Louis Lopes Collection in Lisbon, the 21st Century Identified Human Remains Collection in Coimbra, and the CAL Milano Cemetery Skeletal Collection in Milan, and the skeletal collection at Khon Kaen University (KKU) Human Skeletal Research Centre (HSRC), housed in the Department of Anatomy in the Faculty of Medicine at KKU in Khon Kaen.

Template engine powered by the inja C++ library. Users write a template document, using syntax inspired by the Jinja Python package, and then render the final document by passing data from R. The template syntax supports features such as variables, loops, conditions and inheritance.

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.

Calculate statistical significance of Jaccard/Tanimoto similarity coefficients.

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.

Create regression tables from generalized linear model(GLM), generalized estimating equation(GEE), generalized linear mixed-effects model(GLMM), Cox proportional hazards model, survey-weighted generalized linear model(svyglm) and survey-weighted Cox model results for publication.

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.

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>.

Estimates networks of conditional dependencies (Gaussian graphical models) from multiple classes of data (similar but not exactly, i.e. measurements on different equipment, in different locations or for various sub-types). Package also allows to generate simulation data and evaluate the performance. Implementation of the method described in Angelini, De Canditiis and Plaksienko (2022) <doi:10.3390/math10213983>.

An implementation of fast cluster-based permutation analysis (CPA) for densely-sampled time data developed in Maris & Oostenveld, 2007 <doi:10.1016/j.jneumeth.2007.03.024>. Supports (generalized, mixed-effects) regression models for the calculation of timewise statistics. Provides both a wholesale and a piecemeal interface to the CPA procedure with an emphasis on interpretability and diagnostics. Integrates Julia libraries MixedModels.jl and GLM.jl for performance improvements, with additional functionalities for interfacing with Julia from R powered by the JuliaConnectoR package.

Miscellaneous tools and functions, including: generate descriptive statistics tables, format output, visualize relations among variables or check distributions, and generic functions for residual and model diagnostics.

There are occasions where you need a piece of HTML with integrated styles. A prime example of this is HTML email. This transformation involves moving the CSS and associated formatting instructions from the style block in the head of your document into the body of the HTML. Many prominent email clients require integrated styles in HTML email; otherwise a received HTML email will be displayed without any styling. This package will quickly and precisely perform these CSS transformations when given HTML text and it does so by using the JavaScript juice library.

We provide tools to estimate the individualized interval-valued dose rule (I2DR) that maximizes the expected beneficial clinical outcome for each individual and returns an optimal interval-valued dose, by using the jump Q-learning (JQL) method. The jump Q-learning method directly models the conditional mean of the response given the dose level and the baseline covariates via jump penalized least squares regression under the framework of Q learning. We develop a searching algorithm by dynamic programming in order to find the optimal I2DR with the time complexity O(n2) and spatial complexity O(n). To alleviate the effects of misspecification of the Q-function, a residual jump Q-learning is further proposed to estimate the optimal I2DR. The outcome of interest includes the best partition of the entire dosage of interest, the regression coefficients of each partition, and the value function under the estimated I2DR as well as the Wald-type confidence interval of value function constructed through the Bootstrap.

RStudio addins and Shiny modules for descriptive statistics, regression and survival analysis.

This package provides diagnostic tools for understanding and debugging data frame joins. Analyzes key columns before joining to detect duplicates, mismatches, encoding issues, and other common problems. Explains unexpected row count changes and provides safe join wrappers with cardinality enforcement. Concepts and diagnostics build on tidy data principles as described in Wickham (2014) <doi:10.18637/jss.v059.i10>.