Description: Bayesian inference on the (discrete) power spectrum of time series.
Packaged: Wed Apr 23 09:17:41 2008; christian

Packaged: Tue Apr 29 15:54:41 2008; christian

Description: Bayesian inference on the (discrete) power spectrum of
Packaged: Thu Aug 26 15:04:45 2010; christian

Packaged: 2013-02-21 14:37:46 UTC; christian

Packaged: 2015-04-23 09:07:43 UTC; christian

Packaged: 2022-04-20 13:35:01 UTC; christian
Author: Christian Roever [aut, cre] (<https://orcid.org/0000-0002-6911-698X>)

Title: B-Spline Interpolation and Regression
Description: Build and use B-splines for interpolation and regression.
In case of regression, equality constraints as well as monotonicity
knot positions (not only spline coefficients) can be part of
optimized parameters too. User is provided with
functions calculating spline values at arbitrary points. This
functions can be differentiated to obtain B-splines calculating
derivatives at any point. B-splines of this package can
simultaneously operate on a series of curves sharing the same set of
knots. 'bspline' is written with concern about computing
performance that's why the basis calculation is implemented in C++.
The rest is implemented in R but without notable impact on computing speed.
Imports: Rcpp (>= 1.0.7), nlsic, arrApply

and/or positivity of B-spline weights can be imposed. Moreover,
knot positions (not only spline weights) can be part of
optimized parameters too. For this end, 'bspline' is able to calculate
Jacobian of basis vectors as function of knot positions. User is provided with
functions calculating spline values at arbitrary points. These
functions can be differentiated and integrated to obtain B-splines calculating
derivatives/integrals at any point. B-splines of this package can
performance that's why the basis and Jacobian calculation is implemented in C++.
Imports: Rcpp (>= 1.0.7), nlsic (>= 1.0.2), arrApply

Title: Bayesian Nonparametric Spectral Density Estimation Using
Maintainer: Matthew C. Edwards <matt.edwards@auckland.ac.nz>
Description: Implementation of a Metropolis-within-Gibbs MCMC algorithm to flexibly estimate the spectral density of a stationary time series.  The algorithm updates a nonparametric B-spline prior using the Whittle likelihood to produce pseudo-posterior samples and is based on the work presented by Edwards, Meyer, and Christensen (2017) <arXiv:1707.04878>.
Packaged: 2017-07-18 02:40:32 UTC; mattedwards