Health research using data from electronic health records (EHR) has gained
    popularity, but misclassification of EHR-derived disease status and lack of
    representativeness of the study sample can result in substantial bias in
    effect estimates and can impact power and type I error for association
    tests. Here, the assumed target of inference is the relationship between
    binary disease status and predictors modeled using a logistic regression
    model. 'SAMBA' implements several methods for obtaining bias-corrected
    point estimates along with valid standard errors as proposed in Beesley and
    Mukherjee (2020) <doi:10.1111/biom.13400>, Biometrics.

Implements functions for working with absorbing Markov chains. The
    implementation is based on the framework described in "Toward a unified
    framework for connectivity that disentangles movement and mortality in space
    and time" by Fletcher et al. (2019) <doi:10.1111/ele.13333>, which applies
    them to spatial ecology. This framework incorporates both resistance and 
    absorption with spatial absorbing Markov chains (SAMC) to provide several
    short-term and long-term predictions for metrics related to connectivity in 
    landscapes. Despite the ecological context of the framework, this package
    can be used in any application of absorbing Markov chains.

Design a Bayesian seamless multi-arm biomarker-enriched phase II/III 
             design with the survival endpoint with allowing sample size re-estimation.
             James M S Wason, Jean E Abraham, Richard D Baird, Ioannis Gournaris, Anne-Laure Vallier, James D Brenton, Helena M Earl, Adrian P Mander (2015) <doi:10.1038/bjc.2015.278>.
             Guosheng Yin, Nan Chen, J. Jack Lee (2018) <doi:10.1007/s12561-017-9199-7>.
             Ying Yuan, Beibei Guo, Mark Munsell, Karen Lu, Amir Jazaeri (2016) <doi:10.1002/sim.6971>.

Renders plots to a temporary image using the ragg graphics device and returns knitr::include_graphics() output. Optionally saves the image to a specified path. This helps ensure consistent appearance across interactive sessions, saved files, and knitted documents. For more details see Pedersen and Shemanarev (2025) <doi: 10.32614/CRAN.package.ragg>.