## Usage

```
dist_fit(
values = NULL,
samples = 1000,
cores = 1,
chains = 2,
dist = "exp",
verbose = FALSE,
backend = "rstan"
)
```

## Arguments

- values
Numeric vector of values

- samples
Numeric, number of samples to take. Must be >= 1000. Defaults to 1000.

- cores
Numeric, defaults to 1. Number of CPU cores to use (no effect if greater than the number of chains).

- chains
Numeric, defaults to 2. Number of MCMC chains to use. More is better with the minimum being two.

- dist
Character string, which distribution to fit. Defaults to exponential (

`"exp"`

) but gamma (`"gamma"`

) and lognormal (`"lognormal"`

) are also supported.- verbose
Logical, defaults to FALSE. Should verbose progress messages be printed.

- backend
Character string indicating the backend to use for fitting stan models. Supported arguments are "rstan" (default) or "cmdstanr".

## Examples

```
# \donttest{
# integer adjusted exponential model
dist_fit(rexp(1:100, 2),
samples = 1000, dist = "exp",
cores = ifelse(interactive(), 4, 1), verbose = TRUE
)
#> Warning: `backend` option will be ignored as a stan model object has been passed.
#>
#> SAMPLING FOR MODEL 'dist_fit' NOW (CHAIN 1).
#> Chain 1:
#> Chain 1: Gradient evaluation took 4.9e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.49 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1:
#> Chain 1:
#> Chain 1: Iteration: 1 / 1500 [ 0%] (Warmup)
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#> Chain 1:
#> Chain 1: Elapsed Time: 0.18 seconds (Warm-up)
#> Chain 1: 0.105 seconds (Sampling)
#> Chain 1: 0.285 seconds (Total)
#> Chain 1:
#>
#> SAMPLING FOR MODEL 'dist_fit' NOW (CHAIN 2).
#> Chain 2:
#> Chain 2: Gradient evaluation took 4.6e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.46 seconds.
#> Chain 2: Adjust your expectations accordingly!
#> Chain 2:
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#> Chain 2:
#> Chain 2: Elapsed Time: 0.181 seconds (Warm-up)
#> Chain 2: 0.105 seconds (Sampling)
#> Chain 2: 0.286 seconds (Total)
#> Chain 2:
#> Inference for Stan model: dist_fit.
#> 2 chains, each with iter=1500; warmup=1000; thin=1;
#> post-warmup draws per chain=500, total post-warmup draws=1000.
#>
#> mean se_mean sd 2.5% 25% 50% 75% 97.5% n_eff Rhat
#> lambda[1] 2.01 0.01 0.27 1.53 1.83 1.99 2.18 2.58 332 1
#> lp__ -28.44 0.04 0.67 -30.40 -28.59 -28.18 -28.00 -27.95 283 1
#>
#> Samples were drawn using NUTS(diag_e) at Mon Feb 26 12:43:53 2024.
#> For each parameter, n_eff is a crude measure of effective sample size,
#> and Rhat is the potential scale reduction factor on split chains (at
#> convergence, Rhat=1).
# integer adjusted gamma model
dist_fit(rgamma(1:100, 5, 5),
samples = 1000, dist = "gamma",
cores = ifelse(interactive(), 4, 1), verbose = TRUE
)
#> Warning: `backend` option will be ignored as a stan model object has been passed.
#>
#> SAMPLING FOR MODEL 'dist_fit' NOW (CHAIN 1).
#> Chain 1:
#> Chain 1: Gradient evaluation took 0.000345 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 3.45 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1:
#> Chain 1:
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#> Chain 1:
#>
#> SAMPLING FOR MODEL 'dist_fit' NOW (CHAIN 2).
#> Chain 2:
#> Chain 2: Gradient evaluation took 0.000296 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 2.96 seconds.
#> Chain 2: Adjust your expectations accordingly!
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#> Chain 2:
#> Chain 2: Elapsed Time: 2.561 seconds (Warm-up)
#> Chain 2: 1.056 seconds (Sampling)
#> Chain 2: 3.617 seconds (Total)
#> Chain 2:
#> Inference for Stan model: dist_fit.
#> 2 chains, each with iter=1500; warmup=1000; thin=1;
#> post-warmup draws per chain=500, total post-warmup draws=1000.
#>
#> mean se_mean sd 2.5% 25% 50% 75% 97.5% n_eff Rhat
#> alpha_raw[1] 0.80 0.03 0.50 0.09 0.43 0.73 1.11 2.00 242 1.01
#> beta_raw[1] 0.88 0.03 0.47 0.16 0.51 0.82 1.20 1.90 265 1.01
#> alpha[1] 4.69 0.03 0.50 3.98 4.31 4.62 5.00 5.89 242 1.01
#> beta[1] 4.82 0.03 0.47 4.10 4.45 4.76 5.14 5.84 265 1.01
#> lp__ -20.87 0.08 1.19 -23.86 -21.48 -20.49 -19.92 -19.57 207 1.00
#>
#> Samples were drawn using NUTS(diag_e) at Mon Feb 26 12:44:01 2024.
#> For each parameter, n_eff is a crude measure of effective sample size,
#> and Rhat is the potential scale reduction factor on split chains (at
#> convergence, Rhat=1).
# integer adjusted lognormal model
dist_fit(rlnorm(1:100, log(5), 0.2),
samples = 1000, dist = "lognormal",
cores = ifelse(interactive(), 4, 1), verbose = TRUE
)
#> Warning: `backend` option will be ignored as a stan model object has been passed.
#>
#> SAMPLING FOR MODEL 'dist_fit' NOW (CHAIN 1).
#> Chain 1:
#> Chain 1: Gradient evaluation took 7.1e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.71 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1:
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#> Chain 1:
#>
#> SAMPLING FOR MODEL 'dist_fit' NOW (CHAIN 2).
#> Chain 2:
#> Chain 2: Gradient evaluation took 6.8e-05 seconds
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#> Chain 2: Adjust your expectations accordingly!
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#> Chain 2: 0.6 seconds (Total)
#> Chain 2:
#> Inference for Stan model: dist_fit.
#> 2 chains, each with iter=1500; warmup=1000; thin=1;
#> post-warmup draws per chain=500, total post-warmup draws=1000.
#>
#> mean se_mean sd 2.5% 25% 50% 75% 97.5% n_eff Rhat
#> mu[1] 1.61 0.00 0.02 1.58 1.60 1.61 1.63 1.65 732 1.00
#> sigma[1] 0.15 0.00 0.02 0.12 0.14 0.15 0.16 0.19 660 1.00
#> lp__ -68.67 0.04 0.99 -71.29 -69.06 -68.35 -67.98 -67.70 582 1.01
#>
#> Samples were drawn using NUTS(diag_e) at Mon Feb 26 12:44:02 2024.
#> For each parameter, n_eff is a crude measure of effective sample size,
#> and Rhat is the potential scale reduction factor on split chains (at
#> convergence, Rhat=1).
# }
```