Estimation of extended joint models with shared random effects. Longitudinal data are handled in latent process models for continuous (Gaussian or curvilinear) and ordinal outcomes while proportional hazard models are used for the survival part. We propose a frequentist approach using maximum likelihood estimation. See Saulnier et al, 2022 <doi:10.1016/j.ymeth.2022.03.003>.

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.

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

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.

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.

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.