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  "Title": "Bayesian Regression Models using 'Stan'",
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  "Date": "2025-09-12",
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  "Description": "Fit Bayesian generalized (non-)linear multivariate\nmultilevel models using 'Stan' for full Bayesian inference. A\nwide range of distributions and link functions are supported,\nallowing users to fit -- among others -- linear, robust linear,\ncount data, survival, response times, ordinal, zero-inflated,\nhurdle, and even self-defined mixture models all in a\nmultilevel context. Further modeling options include both\ntheory-driven and data-driven non-linear terms,\nauto-correlation structures, censoring and truncation,\nmeta-analytic standard errors, and quite a few more. In\naddition, all parameters of the response distribution can be\npredicted in order to perform distributional regression. Prior\nspecifications are flexible and explicitly encourage users to\napply prior distributions that actually reflect their prior\nknowledge. Models can easily be evaluated and compared using\nseveral methods assessing posterior or prior predictions.\nReferences: Bürkner (2017) <doi:10.18637/jss.v080.i01>; Bürkner\n(2018) <doi:10.32614/RJ-2018-017>; Bürkner (2021)\n<doi:10.18637/jss.v100.i05>; Carpenter et al. (2017)\n<doi:10.18637/jss.v076.i01>.",
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  "_exports": [
    "acat",
    "acformula",
    "add_criterion",
    "add_ic",
    "add_ic<-",
    "add_loo",
    "add_rstan_model",
    "add_waic",
    "ar",
    "arma",
    "as_draws",
    "as_draws_array",
    "as_draws_df",
    "as_draws_list",
    "as_draws_matrix",
    "as_draws_rvars",
    "as.brmsprior",
    "as.mcmc",
    "asym_laplace",
    "autocor",
    "bayes_factor",
    "bayes_R2",
    "bernoulli",
    "Beta",
    "beta_binomial",
    "bf",
    "bridge_sampler",
    "brm",
    "brm_multiple",
    "brmsfamily",
    "brmsfit_needs_refit",
    "brmsformula",
    "brmsterms",
    "car",
    "categorical",
    "combine_models",
    "compare_ic",
    "conditional_effects",
    "conditional_smooths",
    "constant",
    "control_params",
    "cor_ar",
    "cor_arma",
    "cor_arr",
    "cor_bsts",
    "cor_car",
    "cor_cosy",
    "cor_errorsar",
    "cor_fixed",
    "cor_icar",
    "cor_lagsar",
    "cor_ma",
    "cor_sar",
    "cosy",
    "cox",
    "cratio",
    "cs",
    "cse",
    "cumulative",
    "custom_family",
    "dasym_laplace",
    "data_predictor",
    "data_response",
    "dbeta_binomial",
    "ddirichlet",
    "default_prior",
    "density_ratio",
    "dexgaussian",
    "dfrechet",
    "dgen_extreme_value",
    "dhurdle_gamma",
    "dhurdle_lognormal",
    "dhurdle_negbinomial",
    "dhurdle_poisson",
    "dinv_gaussian",
    "dirichlet",
    "dirichlet_multinomial",
    "dlogistic_normal",
    "dmulti_normal",
    "dmulti_student_t",
    "do_call",
    "dshifted_lnorm",
    "dskew_normal",
    "dstudent_t",
    "dvon_mises",
    "dwiener",
    "dzero_inflated_beta",
    "dzero_inflated_beta_binomial",
    "dzero_inflated_binomial",
    "dzero_inflated_negbinomial",
    "dzero_inflated_poisson",
    "dzero_one_inflated_beta",
    "empty_prior",
    "exgaussian",
    "exponential",
    "expose_functions",
    "expp1",
    "extract_draws",
    "fcor",
    "fixef",
    "frechet",
    "gen_extreme_value",
    "geometric",
    "get_dpar",
    "get_prior",
    "get_y",
    "gp",
    "gr",
    "horseshoe",
    "hurdle_cumulative",
    "hurdle_gamma",
    "hurdle_lognormal",
    "hurdle_negbinomial",
    "hurdle_poisson",
    "hypothesis",
    "inits",
    "inv_logit_scaled",
    "is.brmsfit",
    "is.brmsfit_multiple",
    "is.brmsformula",
    "is.brmsprior",
    "is.brmsterms",
    "is.cor_arma",
    "is.cor_brms",
    "is.cor_car",
    "is.cor_cosy",
    "is.cor_fixed",
    "is.cor_sar",
    "is.mvbrmsformula",
    "is.mvbrmsterms",
    "kfold",
    "kfold_predict",
    "lasso",
    "lf",
    "log_lik",
    "log_posterior",
    "logistic_normal",
    "logit_scaled",
    "logm1",
    "lognormal",
    "loo",
    "LOO",
    "loo_compare",
    "loo_epred",
    "loo_linpred",
    "loo_model_weights",
    "loo_moment_match",
    "loo_predict",
    "loo_predictive_interval",
    "loo_R2",
    "loo_subsample",
    "ma",
    "make_conditions",
    "make_stancode",
    "make_standata",
    "marginal_effects",
    "marginal_smooths",
    "mcmc_plot",
    "me",
    "mi",
    "mixture",
    "mm",
    "mmc",
    "mo",
    "model_weights",
    "multinomial",
    "mvbf",
    "mvbind",
    "mvbrmsformula",
    "nchains",
    "ndraws",
    "neff_ratio",
    "negbinomial",
    "ngrps",
    "niterations",
    "nlf",
    "nsamples",
    "nuts_params",
    "nvariables",
    "opencl",
    "parnames",
    "parse_bf",
    "pasym_laplace",
    "pbeta_binomial",
    "pexgaussian",
    "pfrechet",
    "pgen_extreme_value",
    "phurdle_gamma",
    "phurdle_lognormal",
    "phurdle_negbinomial",
    "phurdle_poisson",
    "pinv_gaussian",
    "post_prob",
    "posterior_average",
    "posterior_epred",
    "posterior_interval",
    "posterior_linpred",
    "posterior_predict",
    "posterior_samples",
    "posterior_smooths",
    "posterior_summary",
    "posterior_table",
    "pp_average",
    "pp_check",
    "pp_expect",
    "pp_mixture",
    "predictive_error",
    "predictive_interval",
    "prepare_predictions",
    "prior",
    "prior_",
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    "prior_samples",
    "prior_string",
    "prior_summary",
    "pshifted_lnorm",
    "psis",
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    "pvon_mises",
    "pzero_inflated_beta",
    "pzero_inflated_beta_binomial",
    "pzero_inflated_binomial",
    "pzero_inflated_negbinomial",
    "pzero_inflated_poisson",
    "pzero_one_inflated_beta",
    "qasym_laplace",
    "qbeta_binomial",
    "qexgaussian",
    "qfrechet",
    "qgen_extreme_value",
    "qhurdle_gamma",
    "qhurdle_lognormal",
    "qhurdle_negbinomial",
    "qhurdle_poisson",
    "qinv_gaussian",
    "qshifted_lnorm",
    "qskew_normal",
    "qstudent_t",
    "qvon_mises",
    "qzero_inflated_beta",
    "qzero_inflated_beta_binomial",
    "qzero_inflated_binomial",
    "qzero_inflated_negbinomial",
    "qzero_inflated_poisson",
    "qzero_one_inflated_beta",
    "R2D2",
    "ranef",
    "rasym_laplace",
    "rbeta_binomial",
    "rdirichlet",
    "read_csv_as_stanfit",
    "recompile_model",
    "reloo",
    "rename_pars",
    "resp_bhaz",
    "resp_cat",
    "resp_cens",
    "resp_dec",
    "resp_index",
    "resp_mi",
    "resp_rate",
    "resp_se",
    "resp_subset",
    "resp_thres",
    "resp_trials",
    "resp_trunc",
    "resp_vint",
    "resp_vreal",
    "resp_weights",
    "restructure",
    "rexgaussian",
    "rfrechet",
    "rgen_extreme_value",
    "rhat",
    "rhurdle_gamma",
    "rhurdle_lognormal",
    "rhurdle_negbinomial",
    "rhurdle_poisson",
    "rinv_gaussian",
    "rlogistic_normal",
    "rmulti_normal",
    "rmulti_student_t",
    "rows2labels",
    "rshifted_lnorm",
    "rskew_normal",
    "rstudent_t",
    "rvon_mises",
    "rwiener",
    "s",
    "sar",
    "save_pars",
    "set_mecor",
    "set_nl",
    "set_prior",
    "set_rescor",
    "shifted_lognormal",
    "skew_normal",
    "sratio",
    "stancode",
    "standata",
    "stanplot",
    "stanvar",
    "student",
    "t2",
    "theme_black",
    "theme_default",
    "threading",
    "unstr",
    "update_adterms",
    "validate_newdata",
    "validate_prior",
    "VarCorr",
    "variables",
    "von_mises",
    "waic",
    "WAIC",
    "weibull",
    "wiener",
    "xbeta",
    "zero_inflated_beta",
    "zero_inflated_beta_binomial",
    "zero_inflated_binomial",
    "zero_inflated_negbinomial",
    "zero_inflated_poisson",
    "zero_one_inflated_beta"
  ],
  "_datasets": [
    {
      "name": "epilepsy",
      "title": "Epileptic seizure counts",
      "object": "epilepsy",
      "class": [
        "data.frame"
      ],
      "fields": [
        "Age",
        "Base",
        "Trt",
        "patient",
        "visit",
        "count",
        "obs",
        "zAge",
        "zBase"
      ],
      "rows": 236,
      "table": true,
      "tojson": true
    },
    {
      "name": "inhaler",
      "title": "Clarity of inhaler instructions",
      "object": "inhaler",
      "class": [
        "data.frame"
      ],
      "fields": [
        "subject",
        "rating",
        "treat",
        "period",
        "carry"
      ],
      "rows": 572,
      "table": true,
      "tojson": true
    },
    {
      "name": "kidney",
      "title": "Infections in kidney patients",
      "object": "kidney",
      "class": [
        "data.frame"
      ],
      "fields": [
        "time",
        "censored",
        "patient",
        "recur",
        "age",
        "sex",
        "disease"
      ],
      "rows": 76,
      "table": true,
      "tojson": true
    },
    {
      "name": "loss",
      "title": "Cumulative Insurance Loss Payments",
      "object": "loss",
      "class": [
        "data.frame"
      ],
      "fields": [
        "AY",
        "dev",
        "cum",
        "premium"
      ],
      "rows": 55,
      "table": true,
      "tojson": true
    }
  ],
  "_help": [
    {
      "page": "brms-package",
      "title": "Bayesian Regression Models using 'Stan'",
      "topics": [
        "brms-package",
        "brms"
      ]
    },
    {
      "page": "add_criterion",
      "title": "Add model fit criteria to model objects",
      "topics": [
        "add_criterion",
        "add_criterion.brmsfit"
      ]
    },
    {
      "page": "add_ic",
      "title": "Add model fit criteria to model objects",
      "topics": [
        "add_ic",
        "add_ic.brmsfit",
        "add_ic<-",
        "add_loo",
        "add_waic"
      ]
    },
    {
      "page": "add_rstan_model",
      "title": "Add compiled 'rstan' models to 'brmsfit' objects",
      "topics": [
        "add_rstan_model"
      ]
    },
    {
      "page": "addition-terms",
      "title": "Additional Response Information",
      "topics": [
        "addition-terms",
        "cat",
        "cens",
        "dec",
        "index",
        "rate",
        "resp_bhaz",
        "resp_cat",
        "resp_cens",
        "resp_dec",
        "resp_index",
        "resp_mi",
        "resp_rate",
        "resp_se",
        "resp_subset",
        "resp_thres",
        "resp_trials",
        "resp_trunc",
        "resp_vint",
        "resp_vreal",
        "resp_weights",
        "se",
        "subset",
        "thres",
        "trials",
        "trunc",
        "vint",
        "vreal",
        "weights"
      ]
    },
    {
      "page": "ar",
      "title": "Set up AR(p) correlation structures",
      "topics": [
        "ar"
      ]
    },
    {
      "page": "arma",
      "title": "Set up ARMA(p,q) correlation structures",
      "topics": [
        "arma"
      ]
    },
    {
      "page": "as.brmsprior",
      "title": "Transform into a brmsprior object",
      "topics": [
        "as.brmsprior"
      ]
    },
    {
      "page": "as.data.frame.brmsfit",
      "title": "Extract Posterior Draws",
      "topics": [
        "as.array.brmsfit",
        "as.data.frame.brmsfit",
        "as.matrix.brmsfit"
      ]
    },
    {
      "page": "as.mcmc.brmsfit",
      "title": "(Deprecated) Extract posterior samples for use with the 'coda' package",
      "topics": [
        "as.mcmc",
        "as.mcmc.brmsfit"
      ]
    },
    {
      "page": "AsymLaplace",
      "title": "The Asymmetric Laplace Distribution",
      "topics": [
        "AsymLaplace",
        "dasym_laplace",
        "pasym_laplace",
        "qasym_laplace",
        "rasym_laplace"
      ]
    },
    {
      "page": "autocor-terms",
      "title": "Autocorrelation structures",
      "topics": [
        "autocor-terms"
      ]
    },
    {
      "page": "autocor.brmsfit",
      "title": "(Deprecated) Extract Autocorrelation Objects",
      "topics": [
        "autocor",
        "autocor.brmsfit"
      ]
    },
    {
      "page": "bayes_factor.brmsfit",
      "title": "Bayes Factors from Marginal Likelihoods",
      "topics": [
        "bayes_factor",
        "bayes_factor.brmsfit"
      ]
    },
    {
      "page": "bayes_R2.brmsfit",
      "title": "Compute a Bayesian version of R-squared for regression models",
      "topics": [
        "bayes_R2",
        "bayes_R2.brmsfit"
      ]
    },
    {
      "page": "BetaBinomial",
      "title": "The Beta-binomial Distribution",
      "topics": [
        "BetaBinomial",
        "dbeta_binomial",
        "pbeta_binomial",
        "qbeta_binomial",
        "rbeta_binomial"
      ]
    },
    {
      "page": "bridge_sampler.brmsfit",
      "title": "Log Marginal Likelihood via Bridge Sampling",
      "topics": [
        "bridge_sampler",
        "bridge_sampler.brmsfit"
      ]
    },
    {
      "page": "brm",
      "title": "Fit Bayesian Generalized (Non-)Linear Multivariate Multilevel Models",
      "topics": [
        "brm"
      ]
    },
    {
      "page": "brm_multiple",
      "title": "Run the same 'brms' model on multiple datasets",
      "topics": [
        "brm_multiple"
      ]
    },
    {
      "page": "brmsfamily",
      "title": "Special Family Functions for 'brms' Models",
      "topics": [
        "acat",
        "asym_laplace",
        "bernoulli",
        "Beta",
        "beta_binomial",
        "brmsfamily",
        "categorical",
        "cox",
        "cratio",
        "cumulative",
        "dirichlet",
        "dirichlet_multinomial",
        "exgaussian",
        "exponential",
        "frechet",
        "gen_extreme_value",
        "geometric",
        "hurdle_cumulative",
        "hurdle_gamma",
        "hurdle_lognormal",
        "hurdle_negbinomial",
        "hurdle_poisson",
        "logistic_normal",
        "lognormal",
        "multinomial",
        "negbinomial",
        "shifted_lognormal",
        "skew_normal",
        "sratio",
        "student",
        "von_mises",
        "weibull",
        "wiener",
        "xbeta",
        "zero_inflated_beta",
        "zero_inflated_beta_binomial",
        "zero_inflated_binomial",
        "zero_inflated_negbinomial",
        "zero_inflated_poisson",
        "zero_one_inflated_beta"
      ]
    },
    {
      "page": "brmsfit-class",
      "title": "Class 'brmsfit' of models fitted with the 'brms' package",
      "topics": [
        "brmsfit",
        "brmsfit-class"
      ]
    },
    {
      "page": "brmsformula",
      "title": "Set up a model formula for use in 'brms'",
      "topics": [
        "bf",
        "brmsformula"
      ]
    },
    {
      "page": "brmsformula-helpers",
      "title": "Linear and Non-linear formulas in 'brms'",
      "topics": [
        "acformula",
        "bf-helpers",
        "brmsformula-helpers",
        "lf",
        "nlf",
        "set_mecor",
        "set_nl",
        "set_rescor"
      ]
    },
    {
      "page": "brmshypothesis",
      "title": "Descriptions of 'brmshypothesis' Objects",
      "topics": [
        "brmshypothesis",
        "plot.brmshypothesis",
        "print.brmshypothesis"
      ]
    },
    {
      "page": "brmsterms",
      "title": "Parse Formulas of 'brms' Models",
      "topics": [
        "brmsterms",
        "brmsterms.brmsformula",
        "brmsterms.default",
        "brmsterms.mvbrmsformula",
        "parse_bf"
      ]
    },
    {
      "page": "car",
      "title": "Spatial conditional autoregressive (CAR) structures",
      "topics": [
        "car"
      ]
    },
    {
      "page": "coef.brmsfit",
      "title": "Extract Model Coefficients",
      "topics": [
        "coef.brmsfit"
      ]
    },
    {
      "page": "combine_models",
      "title": "Combine Models fitted with 'brms'",
      "topics": [
        "combine_models"
      ]
    },
    {
      "page": "compare_ic",
      "title": "Compare Information Criteria of Different Models",
      "topics": [
        "compare_ic"
      ]
    },
    {
      "page": "conditional_effects.brmsfit",
      "title": "Display Conditional Effects of Predictors",
      "topics": [
        "conditional_effects",
        "conditional_effects.brmsfit",
        "marginal_effects",
        "marginal_effects.brmsfit",
        "plot.brms_conditional_effects"
      ]
    },
    {
      "page": "conditional_smooths.brmsfit",
      "title": "Display Smooth Terms",
      "topics": [
        "conditional_smooths",
        "conditional_smooths.brmsfit",
        "marginal_smooths",
        "marginal_smooths.brmsfit"
      ]
    },
    {
      "page": "constant",
      "title": "Constant priors in 'brms'",
      "topics": [
        "constant"
      ]
    },
    {
      "page": "control_params",
      "title": "Extract Control Parameters of the NUTS Sampler",
      "topics": [
        "control_params",
        "control_params.brmsfit"
      ]
    },
    {
      "page": "cor_ar",
      "title": "(Deprecated) AR(p) correlation structure",
      "topics": [
        "cor_ar"
      ]
    },
    {
      "page": "cor_arma",
      "title": "(Deprecated) ARMA(p,q) correlation structure",
      "topics": [
        "cor_arma",
        "cor_arma-class"
      ]
    },
    {
      "page": "cor_brms",
      "title": "(Deprecated) Correlation structure classes for the 'brms' package",
      "topics": [
        "cor_brms",
        "cor_brms-class"
      ]
    },
    {
      "page": "cor_car",
      "title": "(Deprecated) Spatial conditional autoregressive (CAR) structures",
      "topics": [
        "cor_car",
        "cor_icar"
      ]
    },
    {
      "page": "cor_cosy",
      "title": "(Deprecated) Compound Symmetry (COSY) Correlation Structure",
      "topics": [
        "cor_cosy",
        "cor_cosy-class"
      ]
    },
    {
      "page": "cor_fixed",
      "title": "(Deprecated) Fixed user-defined covariance matrices",
      "topics": [
        "cor_fixed",
        "cov_fixed"
      ]
    },
    {
      "page": "cor_ma",
      "title": "(Deprecated) MA(q) correlation structure",
      "topics": [
        "cor_ma"
      ]
    },
    {
      "page": "cor_sar",
      "title": "(Deprecated) Spatial simultaneous autoregressive (SAR) structures",
      "topics": [
        "cor_errorsar",
        "cor_lagsar",
        "cor_sar"
      ]
    },
    {
      "page": "cosy",
      "title": "Set up COSY correlation structures",
      "topics": [
        "cosy"
      ]
    },
    {
      "page": "create_priorsense_data.brmsfit",
      "title": "Prior sensitivity: Create priorsense data",
      "topics": [
        "create_priorsense_data.brmsfit"
      ]
    },
    {
      "page": "cs",
      "title": "Category Specific Predictors in 'brms' Models",
      "topics": [
        "cs",
        "cse"
      ]
    },
    {
      "page": "custom_family",
      "title": "Custom Families in 'brms' Models",
      "topics": [
        "customfamily",
        "custom_family"
      ]
    },
    {
      "page": "default_prior",
      "title": "Default priors for Bayesian models",
      "topics": [
        "default_prior",
        "get_prior"
      ]
    },
    {
      "page": "default_prior.default",
      "title": "Default Priors for 'brms' Models",
      "topics": [
        "default_prior.default"
      ]
    },
    {
      "page": "density_ratio",
      "title": "Compute Density Ratios",
      "topics": [
        "density_ratio"
      ]
    },
    {
      "page": "diagnostic-quantities",
      "title": "Extract Diagnostic Quantities of 'brms' Models",
      "topics": [
        "diagnostic-quantities",
        "log_posterior",
        "log_posterior.brmsfit",
        "neff_ratio",
        "neff_ratio.brmsfit",
        "nuts_params",
        "nuts_params.brmsfit",
        "rhat",
        "rhat.brmsfit"
      ]
    },
    {
      "page": "Dirichlet",
      "title": "The Dirichlet Distribution",
      "topics": [
        "ddirichlet",
        "Dirichlet",
        "rdirichlet"
      ]
    },
    {
      "page": "draws-brms",
      "title": "Transform 'brmsfit' to 'draws' objects",
      "topics": [
        "as_draws",
        "as_draws.brmsfit",
        "as_draws_array",
        "as_draws_array.brmsfit",
        "as_draws_df",
        "as_draws_df.brmsfit",
        "as_draws_list",
        "as_draws_list.brmsfit",
        "as_draws_matrix",
        "as_draws_matrix.brmsfit",
        "as_draws_rvars",
        "as_draws_rvars.brmsfit",
        "draws-brms"
      ]
    },
    {
      "page": "draws-index-brms",
      "title": "Index 'brmsfit' objects",
      "topics": [
        "draws-index-brms",
        "nchains",
        "nchains.brmsfit",
        "ndraws",
        "ndraws.brmsfit",
        "niterations",
        "niterations.brmsfit",
        "nvariables",
        "nvariables.brmsfit",
        "variables",
        "variables.brmsfit"
      ]
    },
    {
      "page": "emmeans-brms-helpers",
      "title": "Support Functions for 'emmeans'",
      "topics": [
        "emmeans-brms-helpers",
        "emm_basis.brmsfit",
        "recover_data.brmsfit"
      ]
    },
    {
      "page": "epilepsy",
      "title": "Epileptic seizure counts",
      "topics": [
        "epilepsy"
      ]
    },
    {
      "page": "ExGaussian",
      "title": "The Exponentially Modified Gaussian Distribution",
      "topics": [
        "dexgaussian",
        "ExGaussian",
        "pexgaussian",
        "qexgaussian",
        "rexgaussian"
      ]
    },
    {
      "page": "expose_functions.brmsfit",
      "title": "Expose user-defined 'Stan' functions",
      "topics": [
        "expose_functions",
        "expose_functions.brmsfit"
      ]
    },
    {
      "page": "expp1",
      "title": "Exponential function plus one.",
      "topics": [
        "expp1"
      ]
    },
    {
      "page": "family.brmsfit",
      "title": "Extract Model Family Objects",
      "topics": [
        "family.brmsfit"
      ]
    },
    {
      "page": "fcor",
      "title": "Fixed residual correlation (FCOR) structures",
      "topics": [
        "fcor"
      ]
    },
    {
      "page": "fitted.brmsfit",
      "title": "Expected Values of the Posterior Predictive Distribution",
      "topics": [
        "fitted.brmsfit"
      ]
    },
    {
      "page": "fixef.brmsfit",
      "title": "Extract Population-Level Estimates",
      "topics": [
        "fixef",
        "fixef.brmsfit"
      ]
    },
    {
      "page": "Frechet",
      "title": "The Frechet Distribution",
      "topics": [
        "dfrechet",
        "Frechet",
        "pfrechet",
        "qfrechet",
        "rfrechet"
      ]
    },
    {
      "page": "GenExtremeValue",
      "title": "The Generalized Extreme Value Distribution",
      "topics": [
        "dgen_extreme_value",
        "GenExtremeValue",
        "pgen_extreme_value",
        "qgen_extreme_value",
        "rgen_extreme_value"
      ]
    },
    {
      "page": "get_dpar",
      "title": "Draws of a Distributional Parameter",
      "topics": [
        "get_dpar"
      ]
    },
    {
      "page": "get_refmodel.brmsfit",
      "title": "Projection Predictive Variable Selection: Get Reference Model",
      "topics": [
        "get_refmodel.brmsfit"
      ]
    },
    {
      "page": "gp",
      "title": "Set up Gaussian process terms in 'brms'",
      "topics": [
        "gp"
      ]
    },
    {
      "page": "gr",
      "title": "Set up basic grouping terms in 'brms'",
      "topics": [
        "gr"
      ]
    },
    {
      "page": "horseshoe",
      "title": "Regularized horseshoe priors in 'brms'",
      "topics": [
        "horseshoe"
      ]
    },
    {
      "page": "Hurdle",
      "title": "Hurdle Distributions",
      "topics": [
        "dhurdle_gamma",
        "dhurdle_lognormal",
        "dhurdle_negbinomial",
        "dhurdle_poisson",
        "Hurdle",
        "phurdle_gamma",
        "phurdle_lognormal",
        "phurdle_negbinomial",
        "phurdle_poisson",
        "qhurdle_gamma",
        "qhurdle_lognormal",
        "qhurdle_negbinomial",
        "qhurdle_poisson",
        "rhurdle_gamma",
        "rhurdle_lognormal",
        "rhurdle_negbinomial",
        "rhurdle_poisson"
      ]
    },
    {
      "page": "hypothesis.brmsfit",
      "title": "Non-Linear Hypothesis Testing",
      "topics": [
        "hypothesis",
        "hypothesis.brmsfit",
        "hypothesis.default"
      ]
    },
    {
      "page": "inhaler",
      "title": "Clarity of inhaler instructions",
      "topics": [
        "inhaler"
      ]
    },
    {
      "page": "inits.brmsfit",
      "title": "Extract Initial Values Used for Each Chain",
      "topics": [
        "inits",
        "inits.brmsfit"
      ]
    },
    {
      "page": "inv_logit_scaled",
      "title": "Scaled inverse logit-link",
      "topics": [
        "inv_logit_scaled"
      ]
    },
    {
      "page": "InvGaussian",
      "title": "The Inverse Gaussian Distribution",
      "topics": [
        "dinv_gaussian",
        "InvGaussian",
        "pinv_gaussian",
        "qinv_gaussian",
        "rinv_gaussian"
      ]
    },
    {
      "page": "is.brmsfit",
      "title": "Checks if argument is a 'brmsfit' object",
      "topics": [
        "is.brmsfit"
      ]
    },
    {
      "page": "is.brmsfit_multiple",
      "title": "Checks if argument is a 'brmsfit_multiple' object",
      "topics": [
        "is.brmsfit_multiple"
      ]
    },
    {
      "page": "is.brmsformula",
      "title": "Checks if argument is a 'brmsformula' object",
      "topics": [
        "is.brmsformula"
      ]
    },
    {
      "page": "is.brmsprior",
      "title": "Checks if argument is a 'brmsprior' object",
      "topics": [
        "is.brmsprior"
      ]
    },
    {
      "page": "is.brmsterms",
      "title": "Checks if argument is a 'brmsterms' object",
      "topics": [
        "is.brmsterms"
      ]
    },
    {
      "page": "is.cor_brms",
      "title": "Check if argument is a correlation structure",
      "topics": [
        "is.cor_arma",
        "is.cor_brms",
        "is.cor_car",
        "is.cor_cosy",
        "is.cor_fixed",
        "is.cor_sar"
      ]
    },
    {
      "page": "is.mvbrmsformula",
      "title": "Checks if argument is a 'mvbrmsformula' object",
      "topics": [
        "is.mvbrmsformula"
      ]
    },
    {
      "page": "is.mvbrmsterms",
      "title": "Checks if argument is a 'mvbrmsterms' object",
      "topics": [
        "is.mvbrmsterms"
      ]
    },
    {
      "page": "kfold_predict",
      "title": "Predictions from K-Fold Cross-Validation",
      "topics": [
        "kfold_predict"
      ]
    },
    {
      "page": "kfold.brmsfit",
      "title": "K-Fold Cross-Validation",
      "topics": [
        "kfold",
        "kfold.brmsfit"
      ]
    },
    {
      "page": "kidney",
      "title": "Infections in kidney patients",
      "topics": [
        "kidney"
      ]
    },
    {
      "page": "lasso",
      "title": "(Defunct) Set up a lasso prior in 'brms'",
      "topics": [
        "lasso"
      ]
    },
    {
      "page": "launch_shinystan.brmsfit",
      "title": "Interface to 'shinystan'",
      "topics": [
        "launch_shinystan",
        "launch_shinystan.brmsfit"
      ]
    },
    {
      "page": "log_lik.brmsfit",
      "title": "Compute the Pointwise Log-Likelihood",
      "topics": [
        "logLik.brmsfit",
        "log_lik",
        "log_lik.brmsfit"
      ]
    },
    {
      "page": "LogisticNormal",
      "title": "The (Multivariate) Logistic Normal Distribution",
      "topics": [
        "dlogistic_normal",
        "LogisticNormal",
        "rlogistic_normal"
      ]
    },
    {
      "page": "logit_scaled",
      "title": "Scaled logit-link",
      "topics": [
        "logit_scaled"
      ]
    },
    {
      "page": "logm1",
      "title": "Logarithm with a minus one offset.",
      "topics": [
        "logm1"
      ]
    },
    {
      "page": "loo_compare.brmsfit",
      "title": "Model comparison with the 'loo' package",
      "topics": [
        "loo_compare",
        "loo_compare.brmsfit"
      ]
    },
    {
      "page": "loo_model_weights.brmsfit",
      "title": "Model averaging via stacking or pseudo-BMA weighting.",
      "topics": [
        "loo_model_weights",
        "loo_model_weights.brmsfit"
      ]
    },
    {
      "page": "loo_moment_match.brmsfit",
      "title": "Moment matching for efficient approximate leave-one-out cross-validation",
      "topics": [
        "loo_moment_match",
        "loo_moment_match.brmsfit",
        "loo_moment_match.loo"
      ]
    },
    {
      "page": "loo_predict.brmsfit",
      "title": "Compute Weighted Expectations Using LOO",
      "topics": [
        "loo_epred",
        "loo_epred.brmsfit",
        "loo_linpred",
        "loo_linpred.brmsfit",
        "loo_predict",
        "loo_predict.brmsfit",
        "loo_predictive_interval",
        "loo_predictive_interval.brmsfit"
      ]
    },
    {
      "page": "loo_R2.brmsfit",
      "title": "Compute a LOO-adjusted R-squared for regression models",
      "topics": [
        "loo_R2",
        "loo_R2.brmsfit"
      ]
    },
    {
      "page": "loo_subsample.brmsfit",
      "title": "Efficient approximate leave-one-out cross-validation (LOO) using subsampling",
      "topics": [
        "loo_subsample",
        "loo_subsample.brmsfit"
      ]
    },
    {
      "page": "loo.brmsfit",
      "title": "Efficient approximate leave-one-out cross-validation (LOO)",
      "topics": [
        "LOO",
        "loo",
        "LOO.brmsfit",
        "loo.brmsfit"
      ]
    },
    {
      "page": "loss",
      "title": "Cumulative Insurance Loss Payments",
      "topics": [
        "loss"
      ]
    },
    {
      "page": "ma",
      "title": "Set up MA(q) correlation structures",
      "topics": [
        "ma"
      ]
    },
    {
      "page": "make_conditions",
      "title": "Prepare Fully Crossed Conditions",
      "topics": [
        "make_conditions"
      ]
    },
    {
      "page": "mcmc_plot.brmsfit",
      "title": "MCMC Plots Implemented in 'bayesplot'",
      "topics": [
        "mcmc_plot",
        "mcmc_plot.brmsfit",
        "stanplot",
        "stanplot.brmsfit"
      ]
    },
    {
      "page": "me",
      "title": "Predictors with Measurement Error in 'brms' Models",
      "topics": [
        "me"
      ]
    },
    {
      "page": "mi",
      "title": "Predictors with Missing Values in 'brms' Models",
      "topics": [
        "mi"
      ]
    },
    {
      "page": "mixture",
      "title": "Finite Mixture Families in 'brms'",
      "topics": [
        "mixture"
      ]
    },
    {
      "page": "mm",
      "title": "Set up multi-membership grouping terms in 'brms'",
      "topics": [
        "mm"
      ]
    },
    {
      "page": "mmc",
      "title": "Multi-Membership Covariates",
      "topics": [
        "mmc"
      ]
    },
    {
      "page": "mo",
      "title": "Monotonic Predictors in 'brms' Models",
      "topics": [
        "mo"
      ]
    },
    {
      "page": "model_weights.brmsfit",
      "title": "Model Weighting Methods",
      "topics": [
        "model_weights",
        "model_weights.brmsfit"
      ]
    },
    {
      "page": "MultiNormal",
      "title": "The Multivariate Normal Distribution",
      "topics": [
        "dmulti_normal",
        "MultiNormal",
        "rmulti_normal"
      ]
    },
    {
      "page": "MultiStudentT",
      "title": "The Multivariate Student-t Distribution",
      "topics": [
        "dmulti_student_t",
        "MultiStudentT",
        "rmulti_student_t"
      ]
    },
    {
      "page": "mvbind",
      "title": "Bind response variables in multivariate models",
      "topics": [
        "mvbind"
      ]
    },
    {
      "page": "mvbrmsformula",
      "title": "Set up a multivariate model formula for use in 'brms'",
      "topics": [
        "mvbf",
        "mvbrmsformula"
      ]
    },
    {
      "page": "ngrps.brmsfit",
      "title": "Number of Grouping Factor Levels",
      "topics": [
        "ngrps",
        "ngrps.brmsfit"
      ]
    },
    {
      "page": "nsamples.brmsfit",
      "title": "(Deprecated) Number of Posterior Samples",
      "topics": [
        "nsamples",
        "nsamples.brmsfit"
      ]
    },
    {
      "page": "opencl",
      "title": "GPU support in Stan via OpenCL",
      "topics": [
        "opencl"
      ]
    },
    {
      "page": "pairs.brmsfit",
      "title": "Create a matrix of output plots from a 'brmsfit' object",
      "topics": [
        "pairs.brmsfit"
      ]
    },
    {
      "page": "parnames",
      "title": "Extract Parameter Names",
      "topics": [
        "parnames",
        "parnames.brmsfit"
      ]
    },
    {
      "page": "plot.brmsfit",
      "title": "Trace and Density Plots for MCMC Draws",
      "topics": [
        "plot.brmsfit"
      ]
    },
    {
      "page": "post_prob.brmsfit",
      "title": "Posterior Model Probabilities from Marginal Likelihoods",
      "topics": [
        "post_prob",
        "post_prob.brmsfit"
      ]
    },
    {
      "page": "posterior_average.brmsfit",
      "title": "Posterior draws of parameters averaged across models",
      "topics": [
        "posterior_average",
        "posterior_average.brmsfit"
      ]
    },
    {
      "page": "posterior_epred.brmsfit",
      "title": "Draws from the Expected Value of the Posterior Predictive Distribution",
      "topics": [
        "posterior_epred",
        "posterior_epred.brmsfit",
        "pp_expect"
      ]
    },
    {
      "page": "posterior_interval.brmsfit",
      "title": "Compute posterior uncertainty intervals",
      "topics": [
        "posterior_interval",
        "posterior_interval.brmsfit"
      ]
    },
    {
      "page": "posterior_linpred.brmsfit",
      "title": "Posterior Draws of the Linear Predictor",
      "topics": [
        "posterior_linpred",
        "posterior_linpred.brmsfit"
      ]
    },
    {
      "page": "posterior_predict.brmsfit",
      "title": "Draws from the Posterior Predictive Distribution",
      "topics": [
        "posterior_predict",
        "posterior_predict.brmsfit"
      ]
    },
    {
      "page": "posterior_samples.brmsfit",
      "title": "(Deprecated) Extract Posterior Samples",
      "topics": [
        "posterior_samples",
        "posterior_samples.brmsfit"
      ]
    },
    {
      "page": "posterior_smooths.brmsfit",
      "title": "Posterior Predictions of Smooth Terms",
      "topics": [
        "posterior_smooths",
        "posterior_smooths.brmsfit"
      ]
    },
    {
      "page": "posterior_summary",
      "title": "Summarize Posterior draws",
      "topics": [
        "posterior_summary",
        "posterior_summary.brmsfit",
        "posterior_summary.default"
      ]
    },
    {
      "page": "posterior_table",
      "title": "Table Creation for Posterior Draws",
      "topics": [
        "posterior_table"
      ]
    },
    {
      "page": "pp_average.brmsfit",
      "title": "Posterior predictive draws averaged across models",
      "topics": [
        "pp_average",
        "pp_average.brmsfit"
      ]
    },
    {
      "page": "pp_check.brmsfit",
      "title": "Posterior Predictive Checks for 'brmsfit' Objects",
      "topics": [
        "pp_check",
        "pp_check.brmsfit"
      ]
    },
    {
      "page": "pp_mixture.brmsfit",
      "title": "Posterior Probabilities of Mixture Component Memberships",
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