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.

Allows to import functions and whole packages from Julia in R. Imported Julia functions can directly be called as R functions. Data structures can be translated between Julia and R. More details can also be found in the corresponding article <doi:10.18637/jss.v101.i06>.

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 tools for competing risks trials that allow simultaneous inference on recovery and mortality endpoints. Provides data preparation helpers, standard cumulative incidence estimators (restricted mean time gained/lost), and severity weighted extensions that integrate longitudinal ordinal outcomes to summarise treatment benefit. Methods follow Wen, Hu, and Wang (2023) Biometrics 79(3):1635-1645 <doi:10.1111/biom.13752>.

This package contains functions for fitting a joinpoint proportional hazards model to relative survival or cause-specific survival data, including estimates of joinpoint years at which survival trends have changed and trend measures in the hazard and cumulative survival scale. See Yu et al.(2009) <doi:10.1111/j.1467-985X.2009.00580.x>.

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).

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.

Read Japanese city codes (<https://www.e-stat.go.jp/municipalities/cities>) to get city and prefecture names, or convert to city codes at different points in time. In addition, it merges or splits wards of designated cities and gets all city codes at a specific point in time.

This package provides a suite of common statistical methods such as descriptives, t-tests, ANOVAs, regression, correlation matrices, proportion tests, contingency tables, and factor analysis. This package is also useable from the jamovi statistical spreadsheet (see <https://www.jamovi.org> for more information).

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

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.

Aids in the calculation and visualization of regions of non-significance using the Johnson-Neyman technique and its extensions as described by Bauer and Curran (2005) <doi:10.1207/s15327906mbr4003_5> to assess the influence of categorical and continuous moderators. Allows correcting for phylogenetic relatedness.

This package provides analysis tools for big data where the sample size is very large. It offers a suite of functions for fitting and predicting joint models, which allow for the simultaneous analysis of longitudinal and time-to-event data. This statistical methodology is particularly useful in medical research where there is often interest in understanding the relationship between a longitudinal biomarker and a clinical outcome, such as survival or disease progression. This can be particularly useful in a clinical setting where it is important to be able to predict how a patient's health status may change over time. Overall, this package provides a comprehensive set of tools for joint modeling of BIG data obtained as survival and longitudinal outcomes with both Bayesian and non-Bayesian approaches. Its versatility and flexibility make it a valuable resource for researchers in many different fields, particularly in the medical and health sciences.

Java GUI for R - cross-platform, universal and unified Graphical User Interface for R. For full functionality on Windows and Mac OS X JGR requires a start application which depends on your OS.

This is a collection of tools for more efficiently understanding and sharing the results of (primarily) regression analyses. There are also a number of miscellaneous functions for statistical and programming purposes. Support for models produced by the survey and lme4 packages are points of emphasis.

Leverages the yum package to implement a YAML ('YAML Ain't Markup Language', a human friendly standard for data serialization; see <https://yaml.org>) standard for documenting justifications, such as for decisions taken during the planning, execution and analysis of a study or during the development of a behavior change intervention as illustrated by Marques & Peters (2019) <doi:10.17605/osf.io/ndxha>. These justifications are both human- and machine-readable, facilitating efficient extraction and organisation.

Fit joint mean-covariance models for longitudinal data. The models and their components are represented using S4 classes and methods. The core computational algorithms are implemented using the Armadillo C++ library for numerical linear algebra and RcppArmadillo glue.

This package provides method used to check whether data have outlier in efficiency measurement of big samples with data envelopment analysis (DEA). In this jackstrap method, the package provides two criteria to define outliers: heaviside and k-s test. The technique was developed by Sousa and Stosic (2005) "Technical Efficiency of the Brazilian Municipalities: Correcting Nonparametric Frontier Measurements for Outliers." <doi:10.1007/s11123-005-4702-4>.

This package provides a gridded classification of weather types by applying the Jenkinson and Collison classification. For a given region (it can be either local region or the whole map),it computes at each grid the 11 weather types during the period considered for the analysis. See Otero et al., (2017) <doi:10.1007/s00382-017-3705-y> for more information.

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>.

Runs resampling-based tests jointly, e.g., sign-flip score tests from Hemerik et al., (2020) <doi:10.1111/rssb.12369>, to allow for multivariate testing, i.e., weak and strong control of the Familywise Error Rate or True Discovery Proportion.

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/>.

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.

This package implements penalised multivariate regression (i.e., for multiple outcomes and many features) by stacked generalisation (<doi:10.1093/bioinformatics/btab576>). For positively correlated outcomes, a single multivariate regression is typically more predictive than multiple univariate regressions. Includes functions for model fitting, extracting coefficients, outcome prediction, and performance measurement. For optional comparisons, install remMap from GitHub (<https://github.com/cran/remMap>).