bayesSSM0.4.7 package

Bayesian Methods for State Space Models

default_tune_control

Create Tuning Control Parameters

dot-back_transform_params

Internal function to back-transform parameters

dot-check_params_match

Helper function to validate input of user-defined functions and priors

dot-compute_log_jacobian

Internal function to compute the Jacobian of the transformation

dot-pilot_run

Pilot Run for Particle Filter Tuning

dot-resample_multinomial

Internal Resampling Functions

dot-run_pilot_chain

Run Pilot Chain for Posterior Estimation

dot-transform_params

Internal function to transform parameters

ess

Estimate effective sample size (ESS) of MCMC chains.

particle_filter

Particle Filter

pmmh

Particle Marginal Metropolis-Hastings (PMMH) for State-Space Models

print.pmmh_output

Print method for PMMH output

rhat

Compute split Rhat statistic

summary.pmmh_output

Print method for PMMH output

Download source packageRead PDF manual

Implements methods for Bayesian analysis of State Space Models. Includes implementations the Particle Marginal Metropolis-Hastings algorithm described in Andrieu et al. (2010) <doi:10.1111/j.1467-9868.2009.00736.x> and automatic tuning inspired by Pitt et al. (2012) <doi:10.1016/j.jeconom.2012.06.004> and J. Dahlin and T. B. Schön (2019) <doi:10.18637/jss.v088.c02>.

  • Maintainer: Bjarke Hautop
  • License: MIT + file LICENSE
  • Last published: 2025-04-23

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