R: Dynamic Shrinkage Process and Change Point Detection

# Dynamic Shrinkage Process and Change Point Detection

## Documentation for package ‘dsp’ version 1.3.0

- DESCRIPTION file
- Package NEWS
- Package README

## 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)