Bayesian Spectral Analysis Models using Gaussian Process Priors
Seongil Jo [aut, cre],
Taeryon Choi [aut],
Beomjo Park [aut, cre],
Peter J. Lenk [ctb]
Contains functions to perform Bayesian inference
using a spectral analysis of Gaussian process priors.
Gaussian processes are represented with a Fourier series
based on cosine basis functions. Currently the package
includes parametric linear models, partial linear additive
models with/without shape restrictions, generalized linear
additive models with/without shape restrictions, and
density estimation model. To maximize computational
efficiency, the actual Markov chain Monte Carlo sampling
for each model is done using codes written in FORTRAN 90.

Contains functions to perform Bayesian inference
using a spectral analysis of Gaussian process priors.
Gaussian processes are represented with a Fourier series
based on cosine basis functions. Currently the package
includes parametric linear models, partial linear additive
models with/without shape restrictions, generalized linear
additive models with/without shape restrictions, and
density estimation model. To maximize computational
efficiency, the actual Markov chain Monte Carlo sampling
for each model is done using codes written in FORTRAN 90.
This software has been developed using funding supported by
Basic Science Research Program through the National Research
Foundation of Korea (NRF) funded by the Ministry of Education
(no. NRF-2016R1D1A1B03932178 and no. NRF-2017R1D1A3B03035235).

It is used for scaling a quantitative variable as a binary variable, consisting
only 0s and 1s. It is useful in scaling a response variable measured on interval scale for
binary logistic regression model.

Bayesian parametric and semi-parametric analyses for
semi-competing risks data
Kyu Ha Lee, Sebastein Haneuse, Deborah Schrag, and Francesca
Dominici
Algorithms for Bayesian analysis of semi-competing risks
data.

Kyu Ha Lee, Sebastien Haneuse, Deborah Schrag, and Francesca
Dominici

Exposes the binary search functions of the C++ standard library (std::lower_bound, std::upper_bound) plus other convenience functions, allowing faster lookups on sorted vectors.