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
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)
