Here's how PAUL-BUERKNER.GITHUB.IO makes money* and how much!

*Please read our disclaimer before using our estimates.
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PAUL-BUERKNER . GITHUB . IO {}

  1. Analyzed Page
  2. Matching Content Categories
  3. CMS
  4. Monthly Traffic Estimate
  5. How Does Paul-buerkner.github.io Make Money
  6. Keywords
  7. Topics
  8. Questions
  9. Social Networks
  10. External Links
  11. Libraries
  12. CDN Services

We began analyzing https://paulbuerkner.com/brms/reference/brm.html, but it redirected us to https://paulbuerkner.com/brms/reference/brm.html. The analysis below is for the second page.

Title[redir]:
Fit Bayesian Generalized (Non-)Linear Multivariate Multilevel Models — brm • brms
Description:
Fit Bayesian generalized (non-)linear multivariate multilevel models using Stan for full Bayesian inference. A wide range of distributions and link functions are supported, allowing users to fit -- among others -- linear, robust linear, count data, survival, response times, ordinal, zero-inflated, hurdle, and even self-defined mixture models all in a multilevel context. Further modeling options include non-linear and smooth terms, auto-correlation structures, censored data, meta-analytic standard errors, and quite a few more. In addition, all parameters of the response distributions can be predicted in order to perform distributional regression. Prior specifications are flexible and explicitly encourage users to apply prior distributions that actually reflect their beliefs. In addition, model fit can easily be assessed and compared with posterior predictive checks and leave-one-out cross-validation.

Matching Content Categories {📚}

  • Style & Fashion
  • Social Networks
  • Technology & Computing

Content Management System {📝}

What CMS is paul-buerkner.github.io built with?

Custom-built

No common CMS systems were detected on Paul-buerkner.github.io, but we identified it was custom coded using Bootstrap (CSS).

Traffic Estimate {📈}

What is the average monthly size of paul-buerkner.github.io audience?

🚄 Respectable Traffic: 10k - 20k visitors per month


Based on our best estimate, this website will receive around 10,019 visitors per month in the current month.
However, some sources were not loaded, we suggest to reload the page to get complete results.

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How Does Paul-buerkner.github.io Make Money? {💸}

We see no obvious way the site makes money.

While profit motivates many websites, others exist to inspire, entertain, or provide valuable resources. Websites have a variety of goals. And this might be one of them. Paul-buerkner.github.io might have a hidden revenue stream, but it's not something we can detect.

Keywords {🔍}

model, fit, data, null, stan, default, draws, false, argument, set, family, true, details, number, control, brms, prior, file, object, priors, parameters, backend, list, function, values, models, options, chains, current, option, functions, string, defaults, warmup, sampling, arguments, class, deprecated, globally, session, initial, summaryfit, bayesian, posterior, formula, rstan, brmsformula, character, passed, brmsfit,

Topics {✒️}

meta-analytic standard errors paul-christian bürkner population-level design matrices bayesian multilevel models character string naming optional character string group-level effects define additional variables perform distributional regression calculate bayes factors actual sampling progress post processing functions fit bayesian generalized linear gaussian model generating initial values brms-created stan model additional mock_fit argument brmsthreads object created fixed parameter values make results reproducible full bayesian inference defined mixture models explicitly encourage users modeling options include unused factors levels multivariate normal distribution prior predictive distribution fitted model head auto-correlation structures divergent transitions threatening generated stan code posterior predictive checks group covariance matrices brmsprior objects created flexible parallelization library slow running model independent normal distributions obtained posterior draws random number generation compression algorithms supported reasonable informative priors fitted model object make testing brms link argument allowing text file named autocorrelation terms directly parameter classes priors defined stan functions posterior distributions plot obtain prior draws

Questions {❓}

  • Should unused factors levels in the data be dropped?

Social Networks {👍}(1)

External Links {🔗}(87)

Libraries {📚}

  • Bootstrap
  • Clipboard.js
  • jQuery

CDN Services {📦}

  • Cloudflare

2.13s.