Provide an interface for 'Drama Corpora Project' ('DraCor') API: <https://dracor.org/documentation/api>.

Regression-discontinuity (RD) designs are quasi-experimental research designs popular in social, behavioral and natural sciences. The RD design is usually employed to study the (local) causal effect of a treatment, intervention or policy. This package provides tools for data-driven graphical and analytical statistical inference in RD designs: rdrobust() to construct local-polynomial point estimators and robust confidence intervals for average treatment effects at the cutoff in Sharp, Fuzzy and Kink RD settings, rdbwselect() to perform bandwidth selection for the different procedures implemented, and rdplot() to conduct exploratory data analysis (RD plots).

Provides tools for fitting and simulating univariate and multivariate damped random walk processes, also known as Ornstein-Uhlenbeck processes or first-order continuous-time autoregressive models, CAR(1) or CARMA(1, 0). The package supports irregularly spaced observation times, heteroscedastic measurement errors, missing measurements across multivariate time series, and polynomial mean trends in normalized time. The current implementation models up to ten time series jointly. Kalman filtering is used to evaluate the likelihood efficiently for maximum likelihood estimation and Bayesian posterior sampling. Users should preserve sufficient numerical precision when loading astronomical observation times; see the manual for details. Also see Hu and Tak (2020) <doi:10.48550/arXiv.2005.08049>.

Interface to the Dryad "Solr" API, their "OAI-PMH" service, and
    fetch datasets. Dryad (<https://datadryad.org/>) is a curated host of
    data underlying scientific publications.

Provides functionality for carrying out estimation
    with data collected using Respondent-Driven Sampling. This includes
    Heckathorn's RDS-I and RDS-II estimators as well as Gile's Sequential
    Sampling estimator. The package is part of the "RDS Analyst" suite of
    packages for the analysis of respondent-driven sampling data.
    See Gile and Handcock (2010) <doi:10.1111/j.1467-9531.2010.01223.x>, 
    Gile and Handcock (2015) <doi:10.1111/rssa.12091> and
    Gile, Beaudry, Handcock and Ott (2018) <doi:10.1146/annurev-statistics-031017-100704>.