Bayesian Spectral Analysis Models using Gaussian Process Priors
Bayesian Quantile Regression
Bayesian Linear Regression
Bayesian Semiparametric Density Estimation
Bayesian Shape-Restricted Spectral Analysis Quantile Regression
Bayesian Shape-Restricted Spectral Analysis Quantile Regression with D...
Bayesian Shape-Restricted Spectral Analysis Regression
Bayesian Spectral Analysis Regression for Big data
Bayesian Shape-Restricted Spectral Analysis Regression with Dirichlet ...
Electricity demand data
Compute fitted values for a blm object
Compute fitted values for a bsad object
Compute fitted values for a bsam object
Compute fitted values for a bsamdpm object
Specify a Fourier Basis Fit in a BSAM Formula
Generalized Bayesian Linear Models
Bayesian Shape-Restricted Spectral Analysis for Generalized Partial Li...
Numerical integration using a simple Trapezoidal rule
Numerical integration using Simpson's rule
Plot a blm object
Plot a bsad object
Plot a bsam object
Plot a bsamdpm object
Plot a fitted.bsad object
Plot a fitted.bsam object
Plot a fitted.bsamdpm object
Predict method for a blm object
Predict method for a bsam object
Predict method for a bsamdpm object
The asymmetric Laplace distribution
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).