Dynamic Shrinkage Process and Change Point Detection

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Documentation for package ‘dsp’ version 1.2.0

• DESCRIPTION file.
• Package NEWS.

Help Pages

abco	Adaptive Bayesian Changepoint with Outliers
btf	MCMC Sampler for Bayesian Trend Filtering
btf0	MCMC Sampler for Bayesian Trend Filtering: D = 0
btf_bspline	MCMC Sampler for B-spline Bayesian Trend Filtering
btf_bspline0	MCMC Sampler for B-spline Bayesian Trend Filtering: D = 0
btf_reg	MCMC Sampler for Bayesian Trend Filtering: Regression
btf_sparse	Run the MCMC for sparse Bayesian trend filtering
build_Q	Compute the quadratic term in Bayesian trend filtering
build_XtX	Compute X'X
computeDIC_ASV	Function for calculating DIC and Pb (Bayesian measures of model complexity and fit by Spiegelhalter et al. 2002)
credBands	Compute Simultaneous Credible Bands
dsp_fit	MCMC Sampler for Models with Dynamic Shrinkage Processes
dsp_spec	Model Specification
ergMean	Compute the ergodic (running) mean.
fit_ASV	MCMC Sampler for Adaptive Stchoastic Volatility (ASV) model
fit_paramsASV	Helper function for Sampling parameters for ASV model
fit_paramsASV_n	Helper function for Sampling parameters for ASV model with a nugget Effect
generate_ly2hat	Posterior predictive sampler on the transformed y (log(y^2))
getARpXmat	Compute the design matrix X for AR(p) model
getEffSize	Summarize of effective sample size
getNonZeros	Compute Non-Zeros (Signals)
initCholReg_spam	Compute initial Cholesky decomposition for TVP Regression
initChol_spam	Compute initial Cholesky decomposition for Bayesian Trend Filtering
initDHS	Initialize the evolution error variance parameters
initEvol0	Initialize the parameters for the initial state variance
initEvolParams	Initialize the evolution error variance parameters
initSV	Initialize the stochastic volatility parameters
init_paramsASV	Helper function for initializing parameters for ASV model
init_paramsASV_n	Helper function for initializing parameters for ASV model with a nugget effect
invlogit	Compute the inverse log-odds
logit	Compute the log-odds
ncind	Sample components from a discrete mixture of normals
plot.dsp	Plot the Bayesian trend filtering fitted values
predict.dsp	Predict changepoints from the output of ABCO
print.dsp	MCMC Sampler for Models with Dynamic Shrinkage Processes
print.dsp_spec	Model Specification
sampleAR1	Sample the AR(1) coefficient(s)
sampleBTF	Sampler for first or second order random walk (RW) Gaussian dynamic linear model (DLM)
sampleBTF_bspline	Sampler for first or second order random walk (RW) Gaussian dynamic linear model (DLM)
sampleBTF_reg	Sampler for first or second order random walk (RW) Gaussian dynamic linear model (DLM)
sampleBTF_reg_backfit	(Backfitting) Sampler for first or second order random walk (RW) Gaussian dynamic linear model (DLM)
sampleBTF_sparse	Sampler for first or second order random walk (RW) Gaussian dynamic linear model (DLM) with additional shrinkage to zero
sampleDSP	Sample the dynamic shrinkage process parameters
sampleEvol0	Sample the parameters for the initial state variance
sampleEvolParams	Sampler evolution error variance parameters
sampleFastGaussian	Sample a Gaussian vector using the fast sampler of BHATTACHARYA et al.
sampleLogVolMu	Sample the AR(1) unconditional means
sampleLogVolMu0	Sample the mean of AR(1) unconditional means
sampleLogVols	Sample the latent log-volatilities
sampleSVparams	Sampler for the stochastic volatility parameters
sampleSVparams0	Sampler for the stochastic volatility parameters using same functions as DHS prior
sample_j_wrap	Sampling from 10-component Gaussian Mixture component described in Omori et al. 2007
sample_mat_c	Wrapper function for C++ call for sample mat, check pre-conditions to prevent crash
simBaS	Compute Simultaneous Band Scores (SimBaS)
simRegression	Simulate noisy observations from a dynamic regression model
simRegression0	Simulate noisy observations from a dynamic regression model
simUnivariate	Generate univariate signals of different type
spec_dsp	Compute the spectrum of an AR(p) model
summary.dsp	Summarize DSP MCMC chains
t_create_loc	Initializer for location indices for filling in band-sparse matrix
t_initEvolParams_no	Initialize the evolution error variance parameters
t_initEvolZeta_ps	Initialize the anomaly component parameters
t_initSV	Initialize the stochastic volatility parameters
t_sampleAR1	Sample the TAR(1) coefficients
t_sampleBTF	Sampler for first or second order random walk (RW) Gaussian dynamic linear model (DLM)
t_sampleEvolParams	Sample the thresholded dynamic shrinkage process parameters
t_sampleEvolZeta_ps	Sampler for the anomaly component parameters
t_sampleLogVolMu	Sample the TAR(1) unconditional means
t_sampleLogVols	Sample the latent log-volatilities
t_sampleR_mh	Sample the threshold parameter
t_sampleSVparams	Sampler for the stochastic volatility parameters
uni.slice	Univariate Slice Sampler from Neal (2008)