Kjell Konis <kjell.konis@me.com>, Victor M. Panaretos
<victor.panaretos@epfl.ch>.
Nonparametric multivariate kernel density \ estimation
using a back-projected kernel.

Kjell Konis <kjell.konis@me.com>,
Victor M. Panaretos <victor.panaretos@epfl.ch>.
Nonparametric multivariate kernel density \
estimation using a back-projected kernel.

Back Projected Kernel Density Estimation
Kjell Konis <kjell.konis@me.com>, Victor Panaretos
<victor.panaretos@epfl.ch>.
alpha version but now a little bit better

Bayesian Projected Normal Regression Models for Circular Data
person("Jolien", "Cremers", email = "joliencremers@gmail.com",
role = c("aut", "cre"))
Fitting Bayesian multiple and mixed-effect regression models for
circular data based on the projected normal distribution. Both continuous
and categorical predictors can be included. Sampling from the posterior is
performed via an MCMC algorithm. Posterior descriptives of all parameters,
model fit statistics and Bayes factors for hypothesis tests for inequality
constrained hypotheses are provided. See Cremers, Mulder & Klugkist (2018)
<doi:10.1111/bmsp.12108> and Nuñez-Antonio & Guttiérez-Peña (2014)
<doi:10.1016/j.csda.2012.07.025>.

Computations Around Bayesian Predictive Power
Kaspar Rufibach, Paul Jordan, Markus Abt
Implements functions to update Bayesian Predictive Power Computations after not stopping a clinical trial at an interim analysis. Such an interim analysis can either be blinded or unblinded. Code is provided for Normally distributed endpoints with known variance, with a prominent example being the hazard ratio.

Posterior sampling and inference for Bayesian Poisson regression models. The model specification makes use of Gaussian (or conditionally Gaussian) prior distributions on the regression coefficients. Details on the algorithm are found in D'Angelo and Canale (2021) <arXiv:2109.09520>.
Rcpp (>= 1.0.7), RcppArmadillo, coda, MASS

Posterior sampling and inference for Bayesian Poisson regression models. The model specification makes use of Gaussian (or conditionally Gaussian) prior distributions on the regression coefficients. Details on the algorithm are found in D'Angelo and Canale (2022) <doi:10.1080/10618600.2022.2123337>.