* Probability Distributions as S3 Objects* (

**distributions3**)

Tools to create and manipulate probability

distributions using S3. Generics random(), pdf(), cdf() and

quantile() provide replacements for base R’s r/d/p/q style functions.

Functions and arguments have been named carefully to minimize

confusion for students in intro stats courses. The documentation for

each distribution contains detailed mathematical notes.

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**Threshold Matching for Thick Description for Optimal Matching in**

Observational StudiesObservational Studies

**thickmatch**)

Conducts several closest matched pairs to strengthen a matched quantitative comparison of many pairs by thick description in observational studies.

Rosenbaum, P. R. (2017). <doi:10.1080/10618600.2016.1152971>.

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**A friendly MCMC framework****fmcmc**)

Provides a friendly (flexible) Markov Chain Monte Carlo (MCMC) framework for implementing Metropolis-Hastings algorithm in a modular way allowing users to specify automatic convergence checker, personalized transition kernels, and out-of-the-box multiple MCMC chains using parallel computing. Most of the methods implemented in this package can be found in Brooks et al. (2011, ISBN 9781420079425).

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**Testing One Hypothesis Multiple Times****TOHM**)

Approximations of global p-values when testing hypothesis in presence of non-identifiable nuisance parameters. The method relies on the Euler characteristic heuristic and the expected Euler characteristic is efficiently computed by in Algeri and van Dyk (2018) <arXiv:1803.03858>.

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**Nonparametric Regression****npreg**)

Multiple and generalized nonparametric regression using smoothing spline ANOVA models and generalized additive models. Includes support for Gaussian and non-Gaussian responses, smoothers for multiple types of predictors, interactions between smoothers of mixed types, and eight different methods for smoothing parameter selection.