Datasets for the book entitled "Modelling Survival Data in Medical Research" by Collett (2023) <doi:10.1201/9781003282525>. The datasets provide extensive examples of time-to-event data.

Built upon popular R packages such as 'ggstatsplot' and 'ARTool', this collection offers a wide array of tools for simplifying reproducible analyses, generating high-quality visualizations, and producing 'APA'-compliant outputs. The primary goal of this package is to significantly reduce repetitive coding efforts, allowing you to focus on interpreting results. Whether you're dealing with ANOVA assumptions, reporting effect sizes, or creating publication-ready visualizations, this package makes these tasks easier.

Check for namespace collisions between a string input (your function or package name) 
    and half a million packages and functions on CRAN.

Tool to assessing whether the results of a study could be influenced by
    collinearity. Simulations under a given hypothesized truth regarding effects of an
    exposure on the outcome are used and the resulting curves of lagged effects are
    visualized. A user's manual is provided, which includes detailed examples (e.g. a
    cohort study looking for windows of vulnerability to air pollution, a time series
    study examining the linear association of air pollution with hospital admissions,
    and a time series study examining the non-linear association between temperature and
    mortality). The methods are described in Basagana and Barrera-Gomez (2021) <doi:10.1093/ije/dyab179>.

Provides a comprehensive and automated workflow for managing multicollinearity in data frames with numeric and/or categorical variables. The package integrates five robust methods into a single function: (1) target encoding of categorical variables based on response values (Micci-Barreca, 2001 (Micci-Barreca, D. 2001 <doi:10.1145/507533.507538>); (2) automated feature prioritization to preserve key predictors during filtering; (3 and 4) pairwise correlation and VIF filtering across all variable types (numeric–numeric, numeric–categorical, and categorical–categorical); (5) adaptive correlation and VIF thresholds. Together, these methods enable a reliable multicollinearity management in most use cases while maintaining model integrity. The package also supports parallel processing and progress tracking via the packages 'future' and 'progressr', and provides seamless integration with the 'tidymodels' ecosystem through a dedicated recipe step.