{
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  "Type": "Package",
  "Title": "Bayesian Inference of Non-Linear and Non-Gaussian State Space\nModels",
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  "Authors@R": "c(person(given = \"Jouni\",\nfamily = \"Helske\",\nrole = c(\"aut\", \"cre\"),\nemail = \"jouni.helske@iki.fi\",\ncomment = c(ORCID = \"0000-0001-7130-793X\")),\nperson(given = \"Matti\",\nfamily = \"Vihola\",\nrole = \"aut\",\ncomment = c(ORCID = \"0000-0002-8041-7222\")))",
  "Description": "Efficient methods for Bayesian inference of state space\nmodels via Markov chain Monte Carlo (MCMC) based on parallel\nimportance sampling type weighted estimators (Vihola, Helske,\nand Franks, 2020, <doi:10.1111/sjos.12492>), particle MCMC, and\nits delayed acceptance version. Gaussian, Poisson, binomial,\nnegative binomial, and Gamma observation densities and basic\nstochastic volatility models with linear-Gaussian state\ndynamics, as well as general non-linear Gaussian models and\ndiscretised diffusion models are supported. See Helske and\nVihola (2021, <doi:10.32614/RJ-2021-103>) for details.",
  "License": "GPL (>= 2)",
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  "BugReports": "https://github.com/helske/bssm/issues",
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  "Repository": "https://helske.r-universe.dev",
  "Date/Publication": "2025-09-24 11:48:27 UTC",
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  "Author": "Jouni Helske [aut, cre] (ORCID:\n<https://orcid.org/0000-0001-7130-793X>),\nMatti Vihola [aut] (ORCID: <https://orcid.org/0000-0002-8041-7222>)",
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  "_topics": [
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    "cpp",
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    "description": "Bayesian statistics, time series, causal inference, state space models, hidden Markov models, visualization."
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  "_exports": [
    "ar1_lg",
    "ar1_ng",
    "as_bssm",
    "as_draws",
    "as_draws_df",
    "asymptotic_var",
    "bootstrap_filter",
    "bsm_lg",
    "bsm_ng",
    "check_diagnostics",
    "cpp_example_model",
    "ekf",
    "ekf_fast_smoother",
    "ekf_smoother",
    "ekpf_filter",
    "estimate_ess",
    "expand_sample",
    "fast_smoother",
    "gamma",
    "gamma_prior",
    "gaussian_approx",
    "halfnormal",
    "halfnormal_prior",
    "iact",
    "importance_sample",
    "kfilter",
    "normal",
    "normal_prior",
    "particle_smoother",
    "post_correct",
    "run_mcmc",
    "sim_smoother",
    "smoother",
    "ssm_mlg",
    "ssm_mng",
    "ssm_nlg",
    "ssm_sde",
    "ssm_ulg",
    "ssm_ung",
    "suggest_N",
    "svm",
    "tnormal",
    "tnormal_prior",
    "ukf",
    "uniform",
    "uniform_prior"
  ],
  "_datasets": [
    {
      "name": "drownings",
      "title": "Deaths by drowning in Finland in 1969-2019",
      "object": "drownings",
      "file": "drownings.rda",
      "class": [
        "mts",
        "ts",
        "matrix"
      ],
      "fields": [
        "deaths",
        "population",
        "summer_temp",
        "summer_temp_unadjusted"
      ],
      "rows": 51,
      "table": true,
      "tojson": true
    },
    {
      "name": "exchange",
      "title": "Pound/Dollar daily exchange rates",
      "object": "exchange",
      "file": "exchange.rda",
      "class": [
        "numeric"
      ],
      "fields": [],
      "table": false,
      "tojson": true
    },
    {
      "name": "negbin_model",
      "title": "Estimated Negative Binomial Model of Helske and Vihola (2021)",
      "object": "negbin_model",
      "file": "negbin_model.rda",
      "class": [
        "mcmc_output"
      ],
      "fields": [],
      "table": false,
      "tojson": false
    },
    {
      "name": "negbin_series",
      "title": "Simulated Negative Binomial Time Series Data",
      "object": "negbin_series",
      "file": "negbin_series.rda",
      "class": [
        "mts",
        "ts",
        "matrix"
      ],
      "fields": [
        "y",
        "x"
      ],
      "rows": 200,
      "table": true,
      "tojson": true
    },
    {
      "name": "poisson_series",
      "title": "Simulated Poisson Time Series Data",
      "object": "poisson_series",
      "file": "poisson_series.rda",
      "class": [
        "integer"
      ],
      "fields": [],
      "table": false,
      "tojson": true
    }
  ],
  "_help": [
    {
      "page": "ar1_lg",
      "title": "Univariate Gaussian model with AR(1) latent process",
      "topics": [
        "ar1_lg"
      ]
    },
    {
      "page": "ar1_ng",
      "title": "Non-Gaussian model with AR(1) latent process",
      "topics": [
        "ar1_ng"
      ]
    },
    {
      "page": "as_bssm",
      "title": "Convert KFAS Model to bssm Model",
      "topics": [
        "as_bssm"
      ]
    },
    {
      "page": "as_draws-mcmc_output",
      "title": "Convert 'run_mcmc' Output to 'draws_df' Format",
      "topics": [
        "as_draws",
        "as_draws.mcmc_output",
        "as_draws_df",
        "as_draws_df.mcmc_output"
      ]
    },
    {
      "page": "as.data.frame.mcmc_output",
      "title": "Convert MCMC Output to data.frame",
      "topics": [
        "as.data.frame.mcmc_output"
      ]
    },
    {
      "page": "asymptotic_var",
      "title": "Asymptotic Variance of IS-type Estimators",
      "topics": [
        "asymptotic_var"
      ]
    },
    {
      "page": "bootstrap_filter",
      "title": "Bootstrap Filtering",
      "topics": [
        "bootstrap_filter",
        "bootstrap_filter.lineargaussian",
        "bootstrap_filter.nongaussian",
        "bootstrap_filter.ssm_nlg",
        "bootstrap_filter.ssm_sde"
      ]
    },
    {
      "page": "bsm_lg",
      "title": "Basic Structural (Time Series) Model",
      "topics": [
        "bsm_lg"
      ]
    },
    {
      "page": "bsm_ng",
      "title": "Non-Gaussian Basic Structural (Time Series) Model",
      "topics": [
        "bsm_ng"
      ]
    },
    {
      "page": "bssm",
      "title": "Bayesian Inference of State Space Models",
      "topics": [
        "bssm-package",
        "bssm"
      ]
    },
    {
      "page": "check_diagnostics",
      "title": "Quick Diagnostics Checks for 'run_mcmc' Output",
      "topics": [
        "check_diagnostics"
      ]
    },
    {
      "page": "cpp_example_model",
      "title": "Example C++ Codes for Non-Linear and SDE Models",
      "topics": [
        "cpp_example_model"
      ]
    },
    {
      "page": "drownings",
      "title": "Deaths by drowning in Finland in 1969-2019",
      "topics": [
        "drownings"
      ]
    },
    {
      "page": "ekf",
      "title": "(Iterated) Extended Kalman Filtering",
      "topics": [
        "ekf"
      ]
    },
    {
      "page": "ekf_smoother",
      "title": "Extended Kalman Smoothing",
      "topics": [
        "ekf_fast_smoother",
        "ekf_smoother"
      ]
    },
    {
      "page": "ekpf_filter",
      "title": "Extended Kalman Particle Filtering",
      "topics": [
        "ekpf_filter",
        "ekpf_filter.ssm_nlg"
      ]
    },
    {
      "page": "estimate_ess",
      "title": "Effective Sample Size for IS-type Estimators",
      "topics": [
        "estimate_ess"
      ]
    },
    {
      "page": "exchange",
      "title": "Pound/Dollar daily exchange rates",
      "topics": [
        "exchange"
      ]
    },
    {
      "page": "expand_sample",
      "title": "Expand the Jump Chain representation",
      "topics": [
        "expand_sample"
      ]
    },
    {
      "page": "smoother",
      "title": "Kalman Smoothing",
      "topics": [
        "fast_smoother",
        "fast_smoother.lineargaussian",
        "smoother",
        "smoother.lineargaussian"
      ]
    },
    {
      "page": "fitted.mcmc_output",
      "title": "Fitted for State Space Model",
      "topics": [
        "fitted.mcmc_output"
      ]
    },
    {
      "page": "gaussian_approx",
      "title": "Gaussian Approximation of Non-Gaussian/Non-linear State Space Model",
      "topics": [
        "gaussian_approx",
        "gaussian_approx.nongaussian",
        "gaussian_approx.ssm_nlg"
      ]
    },
    {
      "page": "iact",
      "title": "Integrated Autocorrelation Time",
      "topics": [
        "iact"
      ]
    },
    {
      "page": "importance_sample",
      "title": "Importance Sampling from non-Gaussian State Space Model",
      "topics": [
        "importance_sample",
        "importance_sample.nongaussian"
      ]
    },
    {
      "page": "kfilter",
      "title": "Kalman Filtering",
      "topics": [
        "kfilter",
        "kfilter.lineargaussian",
        "kfilter.nongaussian"
      ]
    },
    {
      "page": "logLik_bssm",
      "title": "Extract Log-likelihood of a State Space Model of class 'bssm_model'",
      "topics": [
        "logLik.lineargaussian",
        "logLik.nongaussian",
        "logLik.ssm_nlg",
        "logLik.ssm_sde"
      ]
    },
    {
      "page": "negbin_model",
      "title": "Estimated Negative Binomial Model of Helske and Vihola (2021)",
      "topics": [
        "negbin_model"
      ]
    },
    {
      "page": "negbin_series",
      "title": "Simulated Negative Binomial Time Series Data",
      "topics": [
        "negbin_series"
      ]
    },
    {
      "page": "particle_smoother",
      "title": "Particle Smoothing",
      "topics": [
        "particle_smoother",
        "particle_smoother.lineargaussian",
        "particle_smoother.nongaussian",
        "particle_smoother.ssm_nlg",
        "particle_smoother.ssm_sde"
      ]
    },
    {
      "page": "plot.mcmc_output",
      "title": "Trace and Density Plots for 'mcmc_output'",
      "topics": [
        "plot.mcmc_output"
      ]
    },
    {
      "page": "poisson_series",
      "title": "Simulated Poisson Time Series Data",
      "topics": [
        "poisson_series"
      ]
    },
    {
      "page": "post_correct",
      "title": "Run Post-correction for Approximate MCMC using psi-APF",
      "topics": [
        "post_correct"
      ]
    },
    {
      "page": "predict.mcmc_output",
      "title": "Predictions for State Space Models",
      "topics": [
        "predict",
        "predict.mcmc_output"
      ]
    },
    {
      "page": "print.mcmc_output",
      "title": "Print Results from MCMC Run",
      "topics": [
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