Fits the joint model proposed by Henderson and colleagues (2000) 
    <doi:10.1093/biostatistics/1.4.465>, but extended to the case of multiple 
    continuous longitudinal measures. The time-to-event data is modelled using a 
    Cox proportional hazards regression model with time-varying covariates. The 
    multiple longitudinal outcomes are modelled using a multivariate version of the 
    Laird and Ware linear mixed model. The association is captured by a multivariate
    latent Gaussian process. The model is estimated using a Monte Carlo Expectation 
    Maximization algorithm. This project was funded by the Medical Research Council 
    (Grant number MR/M013227/1).

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>).

Provides tools to explore and summarize relationship patterns between
    variables across one or multiple datasets. The package relies on efficient
    sampling strategies to estimate pairwise associations and supports quick
    exploratory data analysis for large or heterogeneous data sources.

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>.

Joint analysis and imputation of incomplete data in the Bayesian
    framework, using (generalized) linear (mixed) models and extensions there of,
    survival models, or joint models for longitudinal and survival data, as
    described in Erler, Rizopoulos and Lesaffre (2021) <doi:10.18637/jss.v100.i20>.
    Incomplete covariates, if present, are automatically imputed.
    The package performs some preprocessing of the data and creates a 'JAGS'
    model, which will then automatically be passed to 'JAGS' 
    <https://mcmc-jags.sourceforge.io/> with the help of 
    the package 'rjags'.