Fit survival data and perform dynamic prediction under joint frailty-copula models for tumour progression and death. Likelihood-based methods are employed for estimating model parameters, where the baseline hazard functions are modeled by the cubic M-spline or the Weibull model. The methods are applicable for meta-analytic data containing individual-patient information from several studies. Survival outcomes need information on both terminal event time (e.g., time-to-death) and non-terminal event time (e.g., time-to-tumour progression). Methodologies were published in Emura et al. (2017) <doi:10.1177/0962280215604510>, Emura et al. (2018) <doi:10.1177/0962280216688032>, Emura et al. (2020) <doi:10.1177/0962280219892295>, Shinohara et al. (2020) <doi:10.1080/03610918.2020.1855449>, Wu et al. (2020) <doi:10.1007/s00180-020-00977-1>, and Emura et al. (2021) <doi:10.1177/09622802211046390>. See also the book of Emura et al. (2019) <doi:10.1007/978-981-13-3516-7>. Survival data from ovarian cancer patients are also available.

Shared parameter models for the joint modeling of longitudinal and time-to-event data using MCMC; Dimitris Rizopoulos (2016) <doi:10.18637/jss.v072.i07>.

The Jalaali calendar, also known as the Persian or Solar Hijri calendar, is the official calendar of Iran and Afghanistan. It starts on Nowruz, the spring equinox, and follows an astronomical system for determining leap years. Each year consists of 365 or 366 days, divided into 12 months. This package provides functions for converting dates between the Jalaali and Gregorian calendars. The conversion calculations are based on the work of Kazimierz M. Borkowski (1996) (<doi:10.1007/BF00055188>), who used an analytical model of Earth's motion to compute equinoxes from AD 550 to 3800 and determine leap years based on Tehran time.

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 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.

This package provides a Joint PENalty Estimation of Covariance and Inverse Covariance Matrices.

This package provides a long-term forecast model called "Jubilee-Tectonic model" is implemented to forecast future returns of the U.S. stock market, Treasury yield, and gold price. The five-factor model forecasts the 10-year and 20-year future equity returns with high R-squared above 80 percent. It is based on linear growth and mean reversion characteristics in the U.S. stock market. This model also enhances the CAPE model by introducing the hypothesis that there are fault lines in the historical CAPE, which can be calibrated and corrected through statistical learning. In addition, it contains a module for business cycles, optimal interest rate, and recession forecasts.

This package provides functions for grid square codes in Japan (<https://www.stat.go.jp/english/data/mesh/index.html>). Generates the grid square codes from longitude/latitude, geometries, and the grid square codes of different scales, and vice versa.

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.

Shared parameter models for the joint modeling of longitudinal and time-to-event data.

Calculate statistical significance of Jaccard/Tanimoto similarity coefficients.

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>.

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

Estimates Jensen-Shannon divergence (JSD) for quantifying distributional differences between two groups on a given variable. Supports both continuous and discrete variables, with tools for point estimation, bootstrap confidence intervals, and visualization of raw group-specific distributions.

In the observational study design stage, matching/weighting methods are conducted. However, when many background variables are present, the decision as to which variables to prioritize for matching/weighting is not trivial. Thus, the joint treatment-outcome variable importance plots are created to guide variable selection. The joint variable importance plots enhance variable comparisons via unadjusted bias curves derived under the omitted variable bias framework. The plots translate variable importance into recommended values for tuning parameters in existing methods. Post-matching and/or weighting plots can also be used to visualize and assess the quality of the observational study design. The method motivation and derivation is presented in "Prioritizing Variables for Observational Study Design using the Joint Variable Importance Plot" by Liao et al. (2024) <doi:10.1080/00031305.2024.2303419>. See the package paper by Liao and Pimentel (2024) <doi:10.21105/joss.06093> for a beginner friendly user introduction.

Interact with the Entrez API hosted by the National Center for Biotechnology Information (NCBI), <https://www.ncbi.nlm.nih.gov/books/NBK25499/>. This package is focused on working with sequence metadata and links. It handles pagination and compensates for some API limitations to simplify these tasks. API calls are printed to the console to highlight how high-level queries are translated into individual HTTP requests.

This package provides a calculation tool to obtain the 5-year or 10-year risk of cardiovascular disease from various risk models.

Fits univariate and joint N-mixture models for data on two unmarked site-associated species. Includes functions to estimate latent abundances through empirical Bayes methods.

Download and post process the infectious disease case data from Japan Institute for Health Security. Also the package included ready-to-analyse datasets. See the data source website for further details <https://id-info.jihs.go.jp/>.

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).

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.

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 functions to justify alpha levels for statistical hypothesis tests by avoiding Lindley's paradox, or by minimizing or balancing error rates. For more information about the package please read the following: Maier & Lakens (2021) <doi:10.31234/osf.io/ts4r6>).

Implementation of a parametric joint model for modelling recurrent and competing event processes using generalised survival models as described in Entrop et al., (2025) <doi:10.1002/bimj.70038>. The joint model can subsequently be used to predict the mean number of events in the presence of competing risks at different time points. Comparisons of the mean number of event functions, e.g. the differences in mean number of events between two exposure groups, are also available.