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 tools to fit joinpoint regression models with a log-linear 
    specification by levels of a categorical variable. The package acts as a 
    wrapper around the 'segmented' package, facilitating model fitting, selection, 
    and interpretation. It includes functions to estimate the Annual Percent Change 
    (APC) and the Average Annual Percent Change (AAPC), along with their 95% 
    confidence intervals, and to generate formatted summary tables and plots of results.

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>.

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.