R interface to DSDP semidefinite programming library. The DSDP software is a free open source implementation of an interior-point method for semidefinite programming. It provides primal and dual solutions, exploits low-rank structure and sparsity in the data, and has relatively low memory requirements for an interior-point method.

Helper functions to accompany the Blair, Coppock, and Humphreys (2022) "Research Design in the Social Sciences: Declaration, Diagnosis, and Redesign" <https://book.declaredesign.org>. 'rdss' includes datasets, helper functions, and plotting components to enable use and replication of the book.

Provides functionality for carrying out sample size estimation and power calculation in Respondent-Driven Sampling.

Implements a unified framework combining staggered
    difference-in-differences with regression discontinuity designs
    and network interference. Extends Callaway and Sant'Anna (2021)
    <doi:10.1016/j.jeconom.2020.12.001> to settings where treatment
    assignment is determined by a running variable crossing a cutoff,
    adoption timing is heterogeneous across units, and spillover effects
    operate through a known network structure. Provides group-time average
    treatment effects (direct and spillover), aggregation schemes,
    bandwidth selection, and pre-treatment falsification tests.

A tree bootstrap method for estimating uncertainty in respondent-driven samples (RDS). Quantiles are estimated by multilevel resampling in such a way that preserves the dependencies of and accounts for the high variability of the RDS process.

We provide a toolbox to fit univariate and multivariate linear mixed models via data transforming augmentation. Users can also fit these models via typical data augmentation for a comparison. It returns either maximum likelihood estimates of unknown model parameters (hyper-parameters) via an EM algorithm or posterior samples of those parameters via MCMC. Also see Tak et al. (2019) <doi:10.1080/10618600.2019.1704295>.