In 'Shiny' apps, it is sometimes useful to store information
    on a particular item in a tooltip. 'Prompter' allows you to easily 
    create such tooltips, using 'Hint.css'.

Format and submit few-shot prompts to OpenAI's Large Language Models (LLMs). Designed to be particularly useful for text classification problems in the social sciences. Methods are described in Ornstein, Blasingame, and Truscott (2024) <https://joeornstein.github.io/publications/ornstein-blasingame-truscott.pdf>.

A native 'R' client library for querying the 'Prometheus' 
    time-series database, using the 'PromQL' query language.

Carrying out inferences about any linear combination of proportions and the ratio of two proportions.

Propagation of uncertainty using higher-order Taylor expansion and Monte Carlo simulation. Calculations of propagated uncertainties are based on matrix calculus including covariance structure according to Arras 1998 <doi:10.3929/ethz-a-010113668> (first order), Wang & Iyer 2005 <doi:10.1088/0026-1394/42/5/011> (second order) and BIPM Supplement 1 (Monte Carlo) <doi:10.59161/JCGM101-2008>.

Computes two-sample confidence intervals for single, paired and independent proportions.

Implementation of propensity clustering and
        decomposition as described in Ranola et al. (2013) <doi:10.1186/1752-0509-7-21>. 
        Propensity decomposition can be viewed on the
        one hand as a generalization of the eigenvector-based
        approximation of correlation networks, and on the other hand as
        a generalization of random multigraph models and
        conformity-based decompositions.

Calculates propensity score weights for multiple causal
    'estimands' across binary, continuous, and categorical exposures.
    Provides methods for handling extreme propensity scores through
    trimming, truncation, and calibration. Includes inverse probability
    weighted estimators that correctly account for propensity score
    estimation uncertainty.

The Prognostic Regression Offsets with Propagation of
    ERrors (for Treatment Effect Estimation) package facilitates
    direct adjustment for experiments and observational studies that
    is compatible with a range of study designs and covariance
    adjustment strategies. It uses explicit specification of clusters,
    blocks and treatment allocations to furnish probability of
    assignment-based weights targeting any of several average
    treatment effect parameters, and for standard error calculations
    reflecting these design parameters. For covariance adjustment of
    its Hajek and (one-way) fixed effects estimates, it enables
    offsetting the outcome against predictions from a dedicated
    covariance model, with standard error calculations propagating
    error as appropriate from the covariance model.