Package {fitdistrBayes}

Objective Bayesian Distribution Fitting

Description

Fits a univariate parametric distribution under a registered objective Bayesian prior. For built-in models, data-dependent posterior-propriety checks are executed before any numerical integration or simulation. Posterior means, variances, and their Monte Carlo errors are reported only when the corresponding moments have been certified to exist.

Usage

fitdistrBayes(x, distr, prior, start = NULL, fixed = NULL,
  iter = 4000L, warmup = floor(iter / 2), thin = 1L,
  chains = 4L, seed = NULL, na.action = c("fail", "omit"),
  control = list(), ...)

Arguments

Details

Recognized distributions are beta, Cauchy, chi-squared, exponential, Exponential-Logarithmic, Frechet, Gamma, geometric, Gumbel, lognormal, logistic, Lomax, Nakagami-m, negative binomial, Normal, Poisson, Rician, Student t, and Weibull. Available priors depend on the model and parameter of interest; unsupported or known-improper model-prior combinations stop with an informative error.

Use fitdistrBayes_routes() to obtain the machine-readable catalogue of all enabled combinations, their estimated and required fixed parameters, computational engines, and concise propriety conditions. The fitting function performs the authoritative sample-dependent check.

For the Exponential-Logarithmic model, "reference" and "reference-theta" select the ordering in which theta is the parameter of interest; "reference-rate" selects the rate parameter. The Lomax reference posterior and Nakagami-m MDI posterior are disabled because they are improper. Rician currently has only the proper joint-Jeffreys route.

For numerical routes, the automatic center and any user overrides are recorded in fit$initialization. Each MCMC chain is dispersed from this center on the unconstrained scale using control$init_jitter. For conditionally exact Gamma, Frechet, Nakagami-m, and Weibull algorithms, only the marginal shape parameter requires initialization; rate or scale is drawn from its exact conditional distribution.

The negative-binomial routes estimate mu and require a known positive fixed$size. For the Frechet model, the implemented distribution function is F(x)=\exp\{-\mathit{scale}\,x^{-\mathit{shape}}\}. Thus, the argument named scale is the coefficient in the exponent; the conventional quantile scale is \mathit{scale}^{1/\mathit{shape}}.

Sampler controls are target_accept, adapt_interval, proposal_scale, init_jitter, slice_width, slice_steps, and max_init_tries. Diagnostic controls are rhat_threshold, ess_threshold, and warn_convergence; both bulk and tail ESS must meet the ESS threshold. The overall acceptance column uses post-warmup iterations, with warmup and all-iteration rates stored separately. Entropy evaluation uses entropy_tol and the positive integer entropy_exact_limit. Set store_callables = FALSE to omit the closures needed by predict() and log_lik().

For a custom model, lower and upper define componentwise bounds; the package supplies the corresponding transforms and Jacobian. Log mode is inferred only from an explicit log formal. It can be forced with density_is_log and prior_is_log; an initial ordinary/log consistency check is made when possible. prior_style is one of "auto", "scalar", or "vector". A predictive function can be supplied as rng, and rng_validator may check its returned support. Posterior propriety and moment existence remain the user's responsibility for custom targets.

Value

An object of class "fitdistrBayes". It is a list with the following principal components:

call

The matched fitting call.

model

The canonical model name, estimated parameter names, fixed parameters, sample size, and support information.

prior

The normalized prior identifier, its displayed label and kernel, and the posterior-propriety condition verified before fitting.

initialization

The automatic or user-supplied initialization rule, its classical estimation method, and the center used to initialize non-exact algorithms.

engine

The computational algorithm and the requested chain, iteration, warmup, thinning, saved-draw, and seed settings.

estimates

A named numeric vector of posterior medians, used as the default point estimates.

summary

A data frame containing posterior means and standard deviations when certified to exist, medians, equal-tail 95 percent credible limits, Monte Carlo standard errors, rank-normalized split/folded \widehat R, bulk and tail effective sample sizes, and moment-existence indicators.

moment_status

A data frame recording whether the posterior mean and variance of each parameter are known to exist and explaining the corresponding analytical condition.

diagnostics

A list containing the overall convergence decision, thresholds, extrema of \widehat R and effective sample sizes, acceptance information when applicable, and explanatory messages.

capabilities

Logical indicators describing whether prediction and pointwise log-likelihood evaluation are available.

chains

A list of posterior-draw matrices, one matrix per chain.

draws

A data frame combining all posterior draws. Columns .chain, .iteration, and .draw identify each draw and the remaining columns contain parameter values.

data, omitted, control

The analyzed data, the number of omitted missing observations, and the validated computational controls.

Density and random-generation closures are retained internally by default to support log_lik() and predict(); they are omitted when control = list(store_callables = FALSE).

Built-in models, parameters, and priors

The table below gives the canonical model name, the parameters returned by the fit, and every registered objective-prior label. Distribution names and prior labels are matched without regard to case. Hyphens, spaces, and common aliases such as "log-normal", "negative binomial", "student-t", "el", and "nakagami" are normalized internally.

For Student t, independence-jeffreys estimates df; the other three priors require fixed = list(df = ...). The generic label "reference" is used only where its parameter ordering is unambiguous. For Gamma and Exponential-Logarithmic models, the parameter of interest is therefore stated in the label. Unsupported combinations, including objective priors known to yield an improper posterior, are rejected before sampling. Run fitdistrBayes_routes() for the precise data condition and computational engine attached to every route.

Beta observations must lie strictly between 0 and 1; chi-squared and Rician observations are positive; Poisson and geometric observations are nonnegative integers. Exponential, Gamma, geometric, lognormal, logistic, Normal, Poisson, and Weibull follow the corresponding base-R parameterizations. Geometric observations count failures before the first success. The Gumbel model is the location-scale distribution for maxima. For Lomax, the support is x >= 0; for Nakagami-m, spread is E(X^2). Negative-binomial size is known and supplied in fixed, whereas mu is estimated.

Posterior propriety and reported moments

Most objective priors are improper as prior measures. A fit is attempted only after the built-in route verifies its sample-dependent posterior-propriety condition. Typical requirements include positivity, a nonconstant sample, a minimum sample size, or multiplicity restrictions for location-scale models. The complete concise conditions are returned by fitdistrBayes_routes(), while the authoritative check is performed on the supplied x by fitdistrBayes().

Posterior propriety does not imply that every posterior moment exists. Thus, mean or sd can legitimately be NA even when medians, credible intervals, draws, and diagnostics are available. Inspect fit$moment_status and its explanatory note column before treating an NA as a computational failure.

Output and diagnostics

Printing a fit reports the model, prior, computational engine, initialization, propriety decision, posterior summaries, and diagnostic status. The full draws are in fit$draws; chain-specific matrices are in fit$chains. The summary contains posterior means when they exist, medians, central credible limits, MCSE, rank-normalized split/folded \widehat R, and bulk and tail effective sample sizes.

Use summary(), coef(), confint(), as.data.frame(), and plot() for inspection. Use predict() for posterior predictive simulation and log_lik() for a pointwise log-likelihood matrix. The last two methods are computed on demand and require retained callables.

Complete console tutorial

The installed file system.file("examples", "tutorial_fitdistrBayes_all_models.R", package = "fitdistrBayes") contains simulated data for all 19 built-in families, all 50 enabled model-prior routes, comparisons with MASS::fitdistr() where directly available, and a final diagnostic table. It prints to the console and does not save analysis results. Set quick_mode <- TRUE for a short demonstration or FALSE for the longer teaching run.

References

Hosking JRM (1990). “L-moments: Analysis and estimation of distributions using linear combinations of order statistics.” Journal of the Royal Statistical Society: Series B, 52(1), 105–124. doi:10.1111/j.2517-6161.1990.tb01775.x.

Ramos PL, Louzada F, Ramos E, Dey S (2020). “The Frechet distribution: Estimation and application—an overview.” Journal of Statistics and Management Systems, 23(3), 549–578. doi:10.1080/09720510.2019.1645400.

Ferreira PH, Ramos E, Ramos PL, et al. (2020). “Objective Bayesian analysis for the Lomax distribution.” Statistics & Probability Letters, 159, 108677. doi:10.1016/j.spl.2019.108677.

Ramos PL, Louzada F, Ramos E (2018). “Posterior properties of the Nakagami-m distribution using noninformative priors and applications in reliability.” IEEE Transactions on Reliability, 67(1), 105–117. doi:10.1109/TR.2017.2778139.

Moala FA, Achire Quispe E, Ramos PL (2026). “Objective Bayesian inference for the Exponential-Logarithmic distribution.” Journal of Statistical Computation and Simulation, 96(8), 1773–1801. doi:10.1080/00949655.2025.2608789.

Achire E, Ramos E, Ramos PL (2025). “On the posterior property of the Rician distribution.” Statistics, 59(1), 167–186. doi:10.1080/02331888.2024.2425688.

Examples

# A first exact-posterior example.
set.seed(1)
x <- rexp(30, rate = 2)
fit <- fitdistrBayes(x, "exponential", "jeffreys",
                     iter = 400, warmup = 100, chains = 2, seed = 2)
coef(fit)
confint(fit)
fitdistrBayes_routes("exponential")

# Group different objective priors for the same Gamma data.
set.seed(3)
x_gamma <- rgamma(40, shape = 2.5, rate = 1.3)
gamma_fits <- list(
  Jeffreys = fitdistrBayes(x_gamma, "gamma", "jeffreys",
    iter = 400, warmup = 100, chains = 2, seed = 4,
    control = list(warn_convergence = FALSE)),
  Reference_shape = fitdistrBayes(x_gamma, "gamma", "reference-shape",
    iter = 400, warmup = 100, chains = 2, seed = 5,
    control = list(warn_convergence = FALSE)),
  Reference_rate = fitdistrBayes(x_gamma, "gamma", "reference-rate",
    iter = 400, warmup = 100, chains = 2, seed = 6,
    control = list(warn_convergence = FALSE))
)
lapply(gamma_fits, coef)

# A fixed parameter: negative-binomial size is known and mu is estimated.
set.seed(7)
x_nb <- rnbinom(40, size = 5, mu = 8)
fit_nb <- fitdistrBayes(x_nb, "negative binomial", "reference",
  fixed = list(size = 5), iter = 400, warmup = 100,
  chains = 2, seed = 8)
coef(fit_nb)

# Student t: fixed df versus estimated df.
set.seed(9)
x_t <- 1 + 2 * rt(50, df = 7)
fit_t_fixed <- fitdistrBayes(x_t, "t", "jeffreys",
  fixed = list(df = 7), iter = 400, warmup = 100,
  chains = 2, seed = 10, control = list(warn_convergence = FALSE))
fit_t_unknown <- fitdistrBayes(x_t, "t", "independence-jeffreys",
  iter = 400, warmup = 100, chains = 2, seed = 11,
  control = list(warn_convergence = FALSE))
coef(fit_t_fixed)
coef(fit_t_unknown)

# One short fit for every built-in family. The installed tutorial additionally
# runs every available prior for each family.
quick <- list(warn_convergence = FALSE)

set.seed(101)
fit_beta <- fitdistrBayes(rbeta(30, 2, 5), "beta", "jeffreys",
  iter = 120, warmup = 40, chains = 2, seed = 102, control = quick)
fit_cauchy <- fitdistrBayes(rcauchy(40, 1, 2), "cauchy", "reference",
  iter = 120, warmup = 40, chains = 2, seed = 103, control = quick)
fit_chisq <- fitdistrBayes(rchisq(30, 6), "chi-squared", "jeffreys",
  iter = 120, warmup = 40, chains = 2, seed = 104, control = quick)
fit_exponential <- fitdistrBayes(rexp(30, 1.5), "exponential", "mdi",
  iter = 120, warmup = 40, chains = 2, seed = 105, control = quick)
fit_gamma <- fitdistrBayes(rgamma(30, 2.5, rate = 1.3),
  "gamma", "reference-shape", iter = 120, warmup = 40,
  chains = 2, seed = 106, control = quick)
fit_geometric <- fitdistrBayes(rgeom(30, 0.35), "geometric", "reference",
  iter = 120, warmup = 40, chains = 2, seed = 107, control = quick)
fit_lognormal <- fitdistrBayes(rlnorm(30, 1, 0.6), "lognormal", "jeffreys",
  iter = 120, warmup = 40, chains = 2, seed = 108, control = quick)
fit_logistic <- fitdistrBayes(rlogis(40, 1, 2), "logistic", "mdi",
  iter = 120, warmup = 40, chains = 2, seed = 109, control = quick)
fit_nb <- fitdistrBayes(rnbinom(30, size = 5, mu = 8),
  "negative binomial", "jeffreys", fixed = list(size = 5),
  iter = 120, warmup = 40, chains = 2, seed = 110, control = quick)
fit_normal <- fitdistrBayes(rnorm(30, 3, 2), "normal", "reference",
  iter = 120, warmup = 40, chains = 2, seed = 111, control = quick)
fit_poisson <- fitdistrBayes(rpois(30, 4), "Poisson", "mdi",
  iter = 120, warmup = 40, chains = 2, seed = 112, control = quick)
fit_t <- fitdistrBayes(rt(40, 7), "t", "jeffreys", fixed = list(df = 7),
  iter = 120, warmup = 40, chains = 2, seed = 113, control = quick)
fit_weibull <- fitdistrBayes(rweibull(30, 1.6, 2), "weibull", "reference",
  iter = 120, warmup = 40, chains = 2, seed = 114, control = quick)

x_gumbel <- 1 - 2 * log(-log(runif(40)))
fit_gumbel <- fitdistrBayes(x_gumbel, "gumbel", "reference",
  iter = 120, warmup = 40, chains = 2, seed = 115, control = quick)

x_frechet <- (4 / rexp(40))^(1 / 2.5)
fit_frechet <- fitdistrBayes(x_frechet, "frechet", "jeffreys",
  iter = 120, warmup = 40, chains = 2, seed = 116, control = quick)

x_lomax <- 2 * expm1(-log(runif(40)) / 3)
fit_lomax <- fitdistrBayes(x_lomax, "lomax", "jeffreys",
  iter = 120, warmup = 40, chains = 2, seed = 117, control = quick)

x_nakagami <- sqrt(rgamma(40, 2.5, rate = 2.5 / 4))
fit_nakagami <- fitdistrBayes(x_nakagami, "nakagami-m", "reference",
  iter = 120, warmup = 40, chains = 2, seed = 118, control = quick)

theta_el <- 0.4
rate_el <- 1.3
u_el <- runif(40)
x_el <- -(log(-expm1((1 - u_el) * log(theta_el))) -
  log1p(-theta_el)) / rate_el
fit_el <- fitdistrBayes(x_el, "exponential-logarithmic", "reference-rate",
  iter = 120, warmup = 40, chains = 2, seed = 119, control = quick)

x_rician <- sqrt(rnorm(40, 5, 2)^2 + rnorm(40, 0, 2)^2)
fit_rician <- fitdistrBayes(x_rician, "rician", "jeffreys",
  iter = 120, warmup = 40, chains = 2, seed = 120, control = quick)

tutorial <- system.file(
  "examples", "tutorial_fitdistrBayes_all_models.R",
  package = "fitdistrBayes"
)
tutorial
if (interactive()) file.show(tutorial)

Methods for Objective Bayesian Distribution Fits

Description

Methods for inspecting posterior summaries and draws, extracting posterior medians and credible intervals, producing standard diagnostic plots, simulating posterior predictive observations, and computing pointwise log-likelihood matrices. Prediction and pointwise log-likelihood are computed on demand. They are unavailable when the fitted object was created with control = list(store_callables = FALSE); inspect object$capabilities before calling them in reusable workflows.

Usage

## S3 method for class 'fitdistrBayes'
print(x, digits = max(3L, getOption("digits") - 3L), ...)
## S3 method for class 'fitdistrBayes'
summary(object, ...)
## S3 method for class 'summary.fitdistrBayes'
print(x,
  digits = max(3L, getOption("digits") - 3L), ...)
## S3 method for class 'fitdistrBayes'
coef(object, ...)
## S3 method for class 'fitdistrBayes'
confint(object, parm = object$model$parameters,
  level = 0.95, ...)
## S3 method for class 'fitdistrBayes'
as.data.frame(x, row.names = NULL,
  optional = FALSE, ...)
## S3 method for class 'fitdistrBayes'
plot(x,
  type = c("trace", "density", "acf", "pairs"),
  pars = x$model$parameters, ...)
## S3 method for class 'fitdistrBayes'
predict(object, draws = 1000L, size = 1L,
  seed = NULL, ...)
log_lik(object, ...)
## S3 method for class 'fitdistrBayes'
log_lik(object, draws = NULL, seed = NULL, ...)

Arguments

Value

The returned value depends on the method:

• print.fitdistrBayes() returns x, invisibly, and prints the model, prior, computational engine, posterior summaries, and diagnostic status.

• summary.fitdistrBayes() returns an object of class "summary.fitdistrBayes". It is a list containing the original call; model, prior, initialization, and computational-engine records; the posterior summary table; the posterior-moment audit; convergence diagnostics; and the available prediction and log-likelihood capabilities.

• print.summary.fitdistrBayes() returns x, invisibly, and prints the contents of the summary object.

• coef.fitdistrBayes() returns a named numeric vector containing the posterior median of each estimated parameter. These medians are the default point estimates used by the package.

• confint.fitdistrBayes() returns a numeric matrix with one row per requested parameter and two columns containing the lower and upper equal-tail posterior credible limits.

• as.data.frame.fitdistrBayes() returns a data frame of the combined posterior draws. The columns .chain, .iteration, and .draw identify the simulation draw; the remaining columns contain parameter values.

• plot.fitdistrBayes() returns x, invisibly, and produces the requested diagnostic plot as a side effect.

• predict.fitdistrBayes() returns posterior predictive observations. It is a numeric vector of length draws when size = 1, and otherwise a numeric matrix with draws rows and size columns. Each row is one replicated data set generated after selecting a posterior draw.

• log_lik() and log_lik.fitdistrBayes() return a numeric matrix whose rows correspond to posterior draws and whose columns correspond to observations. Each entry is that observation's log-likelihood contribution at the selected posterior draw.

List the Built-In Objective Bayesian Fitting Routes

Description

Returns the machine-readable catalogue used to document and test the available model–prior combinations. The condition column is a concise summary; the fitting function remains the authoritative executable check for the observed sample.

Usage

fitdistrBayes_routes(model = NULL)

Arguments

Value

A data frame with one row for every enabled model–prior combination and the following character columns:

model

Canonical distribution name.

prior

Accepted objective-prior label.

parameters

Comma-separated parameters estimated by the route.

fixed

Parameters that the user must supply in fixed, or an empty string when none are required.

engine

Posterior simulation or numerical-integration strategy used by the route.

condition

Concise sample condition under which the posterior is proper. The fitting function performs the authoritative check on the supplied observations.

Examples

fitdistrBayes_routes()
fitdistrBayes_routes("gamma")
fitdistrBayes_routes("t")