Inverse Stable Prior for Widely-Used Exponential Models
c(person("Dexter", "Cahoy", role = c("aut", "cre"), email = "dexter.cahoy@gmail.com"), person("Joseph", "Sedransk", role = "aut", email = "jxs123@case.edu")  )
Contains functions that allow Bayesian inference on a parameter of some widely-used exponential models. The functions can generate independent samples from the closed-form posterior distribution using the inverse stable prior. Inverse stable is a non-conjugate prior for a parameter of an exponential subclass of discrete and continuous data distributions (e.g. Poisson, exponential, inverse gamma, double exponential (Laplace), half-normal/half-Gaussian, etc.). The prior class provides flexibility in capturing a wide array of prior beliefs (right-skewed and left-skewed) as modulated by a parameter that is bounded in (0,1). The generated samples can be used to simulate the prior and posterior predictive distributions. More details can be found in Cahoy and Sedransk (2019)  <doi:10.1007/s42519-018-0027-2>. The package can also be used as a teaching demo for introductory Bayesian courses.
Dexter Cahoy [aut, cre],
Joseph Sedransk [aut]

Confidence intervals for causal prediction in a regression setting. An experimental version is also available for classification.

Confidence intervals for causal effects, using data collected in different experimental or environmental conditions. Hidden variables can be included in the model with a more experimental version.

Nicolai Meinshausen [aut, cre], Dominik Rothenhaeusler [aut]
glmnet, mboost, linprog, parallel, RColorBrewer

The correction is achieved under the assumption that non-migrating cells of the essay approximately form a quadratic flow profile due to frictional effects, compare law of Hagen-Poiseuille for flow in a tube. The script fits a conical plane to give xyz-coordinates of the cells. It outputs the number of migrated cells and the new corrected coordinates.

Determination of the optimal policy in inventory problems from a game-theoretic perspective.