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Commit3f90d6f

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Rebuild readme
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‎README.Rmd‎

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[![CRAN total downloads](https://cranlogs.r-pkg.org/badges/grand-total/bayesSSM)](https://cran.r-project.org/package=bayesSSM)
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<!-- badges: end-->
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bayesSSM is an R package offering a set of tools for performing Bayesian
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`bayesSSM` is an R package offering a set of tools for performing Bayesian
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inference in state-space models (SSMs). It implements the
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Particle Marginal Metropolis-Hastings (PMMH) in the main function`pmmh`
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for Bayesian inference in SSMs.

‎README.md‎

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downloads](https://cranlogs.r-pkg.org/badges/grand-total/bayesSSM)](https://cran.r-project.org/package=bayesSSM)
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<!-- badges: end-->
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bayesSSM is an R package offering a set of tools for performing Bayesian
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inference in state-space models (SSMs). It implements the Particle
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Marginal Metropolis-Hastings (PMMH) in the main function`pmmh` for
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Bayesian inference in SSMs.
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`bayesSSM` is an R package offering a set of tools for performing
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Bayesianinference in state-space models (SSMs). It implements the
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ParticleMarginal Metropolis-Hastings (PMMH) in the main function`pmmh`
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forBayesian inference in SSMs.
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##Why bayesSSM?
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#> Running pilot chain for tuning...
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#> Using 50 particles for PMMH:
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#> Running Particle MCMC chain with tuned settings...
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#> PMMH Results Summary:
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#> Parameter Mean SD Median 2.5% 97.5% ESS Rhat
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#> phi 0.76 0.12 0.75 0.55 0.97 8 1.478
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#> sigma_x 0.78 0.56 0.74 0.01 1.85 15 1.093
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#> sigma_y 0.89 0.36 0.94 0.22 1.45 36 1.051
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#> Warning in pmmh(pf_wrapper = bootstrap_filter, y = y, m = 500, init_fn =
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#> init_fn, : Some ESS values are below 400, indicating poor mixing. Consider
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#> running the chains for more iterations.

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