Provides access to the complete Pali Canon, or
    Tipitaka, the canonical scripture for Theravadin Buddhists
    worldwide. Based on the Chattha Sangayana Tipitaka version
    4 (Vipassana Research Institute, 1990). Includes word
    frequency data and tools for Pali string sorting. For a
    lemmatized critical edition with sutta-level granularity,
    see the companion package 'tipitaka.critical'.

A lemmatized critical edition of the complete Pali Canon
    (Tipitaka), the canonical scripture of Theravadin Buddhism. Based
    on a five-witness collation of the Pali Text Society (PTS) edition
    (via 'GRETIL'), 'SuttaCentral', the Vipassana Research Institute
    (VRI) Chattha Sangayana edition, the Buddha Jayanti Tipitaka (BJT),
    and the Thai Royal Edition. All text is lemmatized using the
    'Digital Pali Dictionary', grouping inflected forms by dictionary
    headword. Covers all three pitakas (Sutta, Vinaya, Abhidhamma)
    with 5,777 individual text units. The companion package 'tipitaka'
    provides the original VRI edition data and Pali text tools.
    For background on the collation method, see Zigmond (2026)
    <https://github.com/dangerzig/tipitaka.critical>.

Tipping point analysis for clinical trials that employ
    Bayesian dynamic borrowing via robust meta-analytic predictive (MAP)
    priors. Further functions facilitate expert elicitation of a primary
    weight of the informative component of the robust MAP prior and
    computation of operating characteristics. Intended use is the
    planning, analysis and interpretation of extrapolation studies in
    pediatric drug development, but applicability is generally wider.

'Htmlwidget' of 'Tippyjs' to add tooltips to 'Shiny' apps and 'R markdown' documents.

The strength of evidence provided by epidemiological and
    observational studies is inherently limited by the potential for
    unmeasured confounding.  We focus on three key quantities: the
    observed bound of the confidence interval closest to the null, the
    relationship between an unmeasured confounder and the outcome, for
    example a plausible residual effect size for an unmeasured continuous
    or binary confounder, and the relationship between an unmeasured
    confounder and the exposure, for example a realistic mean difference
    or prevalence difference for this hypothetical confounder between
    exposure groups. Building on the methods put forth by Cornfield et al.
    (1959), Bross (1966), Schlesselman (1978), Rosenbaum & Rubin (1983),
    Lin et al. (1998), Lash et al. (2009), Rosenbaum (1986), Cinelli &
    Hazlett (2020), VanderWeele & Ding (2017), and Ding & VanderWeele
    (2016), we can use these quantities to assess how an unmeasured
    confounder may tip our result to insignificance.