Wrapper for 'Datamuse' API to find rhyming and other associated words.
This includes words of similar meaning, spelling, or other related words. Learn
more about the 'Datamuse' API here <https://www.datamuse.com/api/>.

person("Hugh", "Warden", , "hugh.warden@outlook.com", role = c("aut", "cre"),
comment = c(ORCID = "0000-0002-4308-7316"))
Create and manipulate hypergraph objects. This early version of
rhype allows for the output of matrices associated with the hypergraphs
themselves. It also uses these matrices to calculate hypergraph spectra and
perform spectral comparison. Functionality coming soon includes calculation
of hyperpaths and hypergraph centrality measures.

ri: R package for performing randomization-based inference for
experiments
Peter M. Aronow <peter.aronow@yale.edu> and Cyrus Samii
<cds2083@nyu.edu>
This package provides a set of tools for conducting exact
or approximate randomization-based inference for experiments of
arbitrary design. The primary functionality of the package is
in the generation, manipulation and use of permutation matrices
implied by given experimental designs. Among other features,
the package facilitates estimation of average treatment
effects, constant effects variance estimation, randomization
inference for significance testing against sharp null
hypotheses and visualization of data and results.

Randomization Inference for Randomized Experiments
person("Alexander", "Coppock", email = "acoppock@gmail.com", role = c("aut", "cre"))
Randomization inference procedures for simple and complex randomized designs, including multi-armed trials, as described in Gerber and Green (2012, ISBN: 978-0393979954). Users formally describe their randomization procedure and test statistic. The randomization distribution of the test statistic under some null hypothesis is efficiently simulated.

A Flexible Approach for Causal Inference with Multiple
Treatments and Clustered Survival Outcomes
c(
person("Liangyuan", "Hu", role = "aut", email = "lh707@sph.rutgers.edu"),
person("Jiayi", "Ji", role = c("aut", "cre"), email = "jj869@sph.rutgers.edu"))
Random-intercept accelerated failure time (AFT) model utilizing Bayesian additive regression trees (BART) for drawing causal inferences about multiple treatments while accounting for the multilevel survival data structure. It also includes an interpretable sensitivity analysis approach to evaluate how the drawn causal conclusions might be altered in response to the potential magnitude of departure from the no unmeasured confounding assumption.
MASS, MCMCpack, msm, dbarts, magrittr, foreach, doParallel,
dplyr, BART, stringr, tidyr, survival, cowplot, ggplot2, twang,
nnet