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.

All datasets and functions used in the german book "Statistik mit R und RStudio" by grosse Schlarmann (2022) <https://www.produnis.de/R/>.

Implements the JICO algorithm [Wang, P., Wang, H., Li, Q., Shen, D., & Liu, Y. (2022). <arXiv:2209.12388>], which solves the multi-group regression problem. The algorithm decomposes the responses from multiple groups into shared and group-specific
        components, which are driven by low-rank approximations of joint and individual structures from the covariates respectively. It provides the implementation of
        the algorithm so solve the iterative continuum regression problem with fixed rank selection, as well as the cross-validation function to perform hyperparameter tuning.

Chinese text segmentation, keyword extraction and speech tagging
    For R.

jiebaR is a package for Chinese text segmentation, keyword extraction
   and speech tagging. This package provides the data files required by jiebaR.

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.

Approximate joint-inclusion probabilities in Unequal Probability Sampling, or compute Monte Carlo approximations of the first and second-order inclusion probabilities of a general sampling design as in Fattorini (2006) <doi:10.1093/biomet/93.2.269>.

Set of common functions used for manipulating colors,
    detecting and interacting with 'RStudio', modeling, formatting, determining
    users' operating system, feature scaling, and more!

Implements the tree-based approach to joint modeling of time-to-event and longitudinal data. This approach looks for a tree-based partitioning such that within each estimated latent class defined by a terminal node, the time-to-event and longitudinal responses display a lack of association. See Zhang and Simonoff (2018) <arXiv:1812.01774>.