Implements a range of facilities for post-hoc analysis and
             summarizing linear models, generalized linear models and 
             generalized linear mixed models, including grouping and clustering 
             via pairwise comparisons using graph representations and efficient
             algorithms for finding maximal cliques of a graph. 
             Includes also non-parametric toos for post-hoc analysis.
             It has S3 methods for printing summarizing, and producing plots, 
             line and barplots suitable for post-hoc analyses.

Functions to estimate the incubation period distribution of post-infectious syndrome which is defined as the time between the symptom onset of the antecedent infection and that of the post-infectious syndrome.

This is a wrapper for the Mercury Parser API. The Mercury Parser is 
    a single API endpoint that takes a URL and gives you back the content reliably 
    and easily. 
    With just one API request, Mercury takes any web article and returns 
    only the relevant content — headline, author, body text, relevant images and 
    more — free from any clutter. It’s reliable, easy-to-use and free.
    See the webpage here: <https://mercury.postlight.com/>.

Provides a suite of statistical tools for post-linkage data analysis (PLDA), designed to account for record linkage errors in downstream modeling. The package implements a familiar, formula-based regression interface that adjusts for linkage uncertainty, accommodating workflows where direct access to unlinked primary files is restricted. It consolidates diverse adjustment methodologies, all of which support generalized linear models (linear, logistic, Poisson, and Gamma). These methodologies include weighting approaches (Chambers (2009) <https://hdl.handle.net/10779/uow.27788247>; Chambers et al. (2023) <doi:10.1002/wics.1596>), mixture modeling (Slawski et al. (2025) <doi:10.1093/jrsssa/qnae083>), and Bayesian mixture modeling (Gutman et al. (2016) <doi:10.1002/sim.6586>). For time-to-event data, both the weighting (Vo et al. (2024) <doi:10.1002/sim.9960>) and mixture modeling approaches accommodate Cox proportional hazards models, while the Bayesian approaches extend to parametric survival analysis. Additionally, the package leverages mixture modeling for contingency table analyses and Bayesian methods to enable the multiple imputation of latent match status.