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Commit 948d79b

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update docs
1 parent 5ee4ade commit 948d79b

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‎R/RcppExports.R‎

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@@ -101,10 +101,6 @@ R_milstein <- function(x0, L, t, theta, drift_pntr, diffusion_pntr, ddiffusion_p
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.Call('_bssm_R_milstein', PACKAGE = 'bssm', x0, L, t, theta, drift_pntr, diffusion_pntr, ddiffusion_pntr, positive, seed)
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}
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R_milstein_joint <- function(x0, L_c, L_f, t, theta, drift_pntr, diffusion_pntr, ddiffusion_pntr, positive, seed) {
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.Call('_bssm_R_milstein_joint', PACKAGE = 'bssm', x0, L_c, L_f, t, theta, drift_pntr, diffusion_pntr, ddiffusion_pntr, positive, seed)
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}
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suggest_n_nongaussian <- function(model_, theta, candidates, replications, seed, model_type) {
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.Call('_bssm_suggest_n_nongaussian', PACKAGE = 'bssm', model_, theta, candidates, replications, seed, model_type)
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}

‎man/logLik.nongaussian.Rd‎

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‎man/priors.Rd‎

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‎src/RcppExports.cpp‎

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@@ -633,26 +633,6 @@ BEGIN_RCPP
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return rcpp_result_gen;
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END_RCPP
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}
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// R_milstein_joint
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double R_milstein_joint(const double x0, const unsigned int L_c, const unsigned int L_f, const double t, const arma::vec& theta, SEXP drift_pntr, SEXP diffusion_pntr, SEXP ddiffusion_pntr, bool positive, const unsigned int seed);
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RcppExport SEXP _bssm_R_milstein_joint(SEXP x0SEXP, SEXP L_cSEXP, SEXP L_fSEXP, SEXP tSEXP, SEXP thetaSEXP, SEXP drift_pntrSEXP, SEXP diffusion_pntrSEXP, SEXP ddiffusion_pntrSEXP, SEXP positiveSEXP, SEXP seedSEXP) {
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BEGIN_RCPP
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Rcpp::RObject rcpp_result_gen;
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Rcpp::RNGScope rcpp_rngScope_gen;
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Rcpp::traits::input_parameter< const double >::type x0(x0SEXP);
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Rcpp::traits::input_parameter< const unsigned int >::type L_c(L_cSEXP);
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Rcpp::traits::input_parameter< const unsigned int >::type L_f(L_fSEXP);
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Rcpp::traits::input_parameter< const double >::type t(tSEXP);
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Rcpp::traits::input_parameter< const arma::vec& >::type theta(thetaSEXP);
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Rcpp::traits::input_parameter< SEXP >::type drift_pntr(drift_pntrSEXP);
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Rcpp::traits::input_parameter< SEXP >::type diffusion_pntr(diffusion_pntrSEXP);
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Rcpp::traits::input_parameter< SEXP >::type ddiffusion_pntr(ddiffusion_pntrSEXP);
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Rcpp::traits::input_parameter< bool >::type positive(positiveSEXP);
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Rcpp::traits::input_parameter< const unsigned int >::type seed(seedSEXP);
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rcpp_result_gen = Rcpp::wrap(R_milstein_joint(x0, L_c, L_f, t, theta, drift_pntr, diffusion_pntr, ddiffusion_pntr, positive, seed));
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return rcpp_result_gen;
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END_RCPP
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}
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// suggest_n_nongaussian
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arma::vec suggest_n_nongaussian(const Rcpp::List model_, const arma::vec theta, const arma::vec candidates, const unsigned int replications, const unsigned int seed, const int model_type);
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RcppExport SEXP _bssm_suggest_n_nongaussian(SEXP model_SEXP, SEXP thetaSEXP, SEXP candidatesSEXP, SEXP replicationsSEXP, SEXP seedSEXP, SEXP model_typeSEXP) {
@@ -1303,7 +1283,6 @@ static const R_CallMethodDef CallEntries[] = {
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{"_bssm_nonlinear_ekf_mcmc", (DL_FUNC) &_bssm_nonlinear_ekf_mcmc, 27},
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{"_bssm_nonlinear_is_mcmc", (DL_FUNC) &_bssm_nonlinear_is_mcmc, 33},
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{"_bssm_R_milstein", (DL_FUNC) &_bssm_R_milstein, 9},
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{"_bssm_R_milstein_joint", (DL_FUNC) &_bssm_R_milstein_joint, 10},
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{"_bssm_suggest_n_nongaussian", (DL_FUNC) &_bssm_suggest_n_nongaussian, 6},
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{"_bssm_suggest_n_nonlinear", (DL_FUNC) &_bssm_suggest_n_nonlinear, 20},
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{"_bssm_postcorrection_nongaussian", (DL_FUNC) &_bssm_postcorrection_nongaussian, 10},

‎vignettes/sde_model.Rmd‎

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@@ -43,7 +43,7 @@ where $B_t$ is a Brownian motion and where $\mu$ and $\sigma$ are real valued fu
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## Example
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Discretely observed latent diffusion models can be constructed using the `ssm_sde` function, which takes pointers to `C++` functions defining the drift, diffusion, the derivative of the diffusion function, and the log-densities of the observations and the prior. As an example, let us consider an Ornstein–Uhlenbeck process
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Discretely observed latent diffusion models can be constructed using the `ssm_sde` function, which takes pointers to `C++` functions defining the drift, diffusion, the derivative of the diffusion function, the log-densities of the observations, and the log-prior. As an example, let us consider an Ornstein–Uhlenbeck process
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$$
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\textrm{d} \alpha_t = \rho (\nu - \alpha_t) \textrm{d} t + \sigma \textrm{d} B_t,
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$$

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