Package index • rjd3sts

All functions

add(): Adds a state block or a measurement equation to a given state space model
add_equation(): Add a building block to the considered equation
aggregation(): Title
ar() ar2(): Autoregressive model
arima(): Autoregressive Integrated Moving Average (ARIMA) Model
arma(): Autoregressive Moving Average (ARMA) Model
block_d0(): Title
block_p0(): Title
block_t(): Title
block_v(): Title
bsm_model(): Title
bsm_to_ucm(): Title
components_pos(): Position of the components
cumul(): Title
cycle(): Title
.airline(): Creates the state space form of an airline model;
.akf_likelihood(): Computes the diffuse likelihood by means of the augmented Kalman filter.
.arima(): Creates an ARIMA state block (representation I)
.arima2(): Creates an ARIMA state block (representation II)
.bsm2spec(): Title
.bsm2ucm(): Title
.circular_loading(): Title
.ckms_likelihood(): Title
.composite(): Title
.cyclical_loading(): Title
.dk_likelihood(): Computes the diffuse likelihood by means of the diffuse Kalman filter (Durbin-Koopman).
.loading(): Title
.loading_composite(): Creates a composite loading
.local_level(): Creates a local level state block
.local_linear_trend(): Creates a local linear trend state block.
.mssf(): Title
.mssf_measurements(): Title
.noise(): Creates a white noise.
.proc_diffuselikelihood(): Title
.r2jd_bsm(): Title
.sarima(): Creates an ARMA state block
.sarma(): Creates an ARMA state block (representation I)
.sarma2(): Creates an ARMA state block (representation II)
.seasonal(): Creates a seasonal component, corresponding to a multivariate random walk, with an aggregation constraint to 0 and various covariances for the innovations of the transition equation.
.ssf(): Title
.ssf_B(): Title
.ssf_P0(): Title
.ssf_S(): Title
.ssf_T(): Gets the transition matrix.
.ssf_V(): Gets the covariance of the innovations in the transition equation.
.ssf_Z(): Gets the loading vector. It should have the same length as the corresponding state block.
.ssf_as_time_invariant(): Transforms a time invariant state space form based on functions into a state space models represented by matrices.
.ssf_smooth(): Computes smoothed states by means of the augmented Kalman filter in the case of diffuse initialization
.state_diffuse_dim(): Title
.state_dim(): Retrieves the dimension of a state block
.state_of(): Gets the state of the state space form
equation(): Create equation
estimate(): Estimate a SSF Model
filtered_states(): Title
filtered_states_stdev(): Title
filtering_states(): Title
filtering_states_stdev(): Title
loading(): Title
loading_cyclical(): Title
loading_periodic(): Title
loading_sum(): Title
loadings(): Give all the loadings for a given variable
locallevel(): Local Level
locallineartrend(): Local linear trend state block
ltd_airline(): Title
model(): Create Composite Model
msae() msae2() msae3(): Modeling errors in surveys with overlapping panels
msignal(): Title
noise(): Noise state block
parameters(): Get Parameters of SSF Model
periodic(): Title
print(<JD3STS>): Title
reg(): Time Varying Regressors
reg_td(): Title
sae(): Title
sarima(): Title
seasonal(): Seasonal state block
seasonalbreaks(): Title
signal(): Title
smoothed_components(): Retrieves the components of the model (univariate case) or the components corresponding to a given equation (multivariate case)
smoothed_components_stdev(): Retrieves the standard deviations of the components of the model (univariate case) or of the components corresponding to a given equation (multivariate case)
smoothed_states(): Title
smoothed_states_stdev(): Standard deviations of the smoothed states
splines_daily(): Title
splines_generic(): Title
splines_regular(): Title
ssf(): Title
sts(): Title
sts_forecast(): Forecast with STS model
sts_outliers(): Title
sts_raw(): Title
tdairline_estimation(): Title
var_ar(): Title
var_loading(): Title
var_locallevel(): Title
var_locallineartrend(): Title
var_noise(): Title
var_reg(): Time Varying Regressor
var_seasonal(): Title