#
brms
Here are 78 public repositories matching this topic...
Bayesian analysis + tidy data + geoms (R package)
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Feb 22, 2022 - R
The bookdown version lives here: https://bookdown.org/content/3890
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May 5, 2022 - HTML
PHP Rule Engine - Parses & Evaluates JavaScript-like expressions
parser
workflow
rule-engine
dsl
evaluator
brms
rule-parser
business-rules
rule-system
php-rule-engine
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Mar 20, 2022 - PHP
A pragmatic business rule management system
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Apr 10, 2021 - Java
Covers the basics of mixed models, mostly using @lme4
r
multilevel-models
brms
linear-mixed-models
covariance
variance-components
mixed-models
random-effects
lme4
hierarchical-linear-models
random-intercepts
random-slopes
generalized-linear-mixed-models
crossed-random-effects
nested-random-effects
nlme
glmmtmb
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Jan 30, 2022 - R
r
bayesian-methods
rstan
bayesian
bayesian-inference
stan
brms
rstanarm
mcmc
regression-models
likelihood
bayesian-data-analysis
hamiltonian-monte-carlo
bayesian-statistics
bayesian-analysis
posterior-probability
metropolis-hastings
gibbs
prior
posterior-predictive
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Feb 21, 2022 - Stan
visualization
r
heatmap
gam
stan
brms
r-package
palettes
mixed-models
ggplot2-themes
mgcv
random-effects
plotly-theme
adjacent
coefficients
complementary
colorgorical
triadic
tetradic
mermod
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Oct 28, 2020 - R
An R package for extracting results from mixed models that are easy to use and viable for presentation.
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Apr 15, 2021 - R
hsbadr
commented
May 18, 2021
Apparently, we need to add more automated tests to immediately detect and fix incorrect or unexpected behavior before being introduced with any future code changes of feature updates. We aim for getting incrementally closer to 100% coverage.
good first issue
Good for newcomers
Helper functions for brmsfit objects (DEPRECATED)
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Jul 29, 2019 - R
Workshop on using Mixed Models with R
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Feb 7, 2019 - R
Hyperon - Motor Insurance Demo App. This is a sample application to demonstrate capabilities of Hyperon.io library (Java Business Rules Engine (BRE)/Java Pricing Engine). The application demonstrates responsive quotations for Car/Motor Insurance based on decision tables and Rhino functions (for math calculations). It shows different possible business rules configuration and overall BRMS performance as well.
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Mar 15, 2022 - Java
REST server + UI for Rulette
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Dec 27, 2020 - Java
Demonstration of alternatives to lme4
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Aug 12, 2019 - R
Formation doctorale d'introduction à la modélisation statistique bayésienne
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Nov 6, 2020 - HTML
A quick reference for how to run many models in R.
machine-learning
r
statistics
time-series
pca
psych
survival-analysis
regularization
spatial-analysis
brms
sem
mixture-model
cluster-analysis
statistical-models
mixed-models
additive-models
mgcv
lme4
bayesian-models
catwalk
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May 19, 2018 - R
Business Rules Integration Engine
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Mar 27, 2022 - JavaScript
RHDM 7.9.0 demo - loan pre-approval decision service for Quick Loan Bank
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Jul 14, 2021 - Java
An R package providing a GUI ('shiny' app) for the R package 'brms'.
gui
r
statistics
shiny
statistical-analysis
statistical-inference
rstan
bayesian
bayesian-inference
stan
brms
bayes
r-package
mcmc
bayesian-data-analysis
bayesian-statistics
statistical-models
shiny-app
cmdstanr
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Apr 28, 2022 - R
Workshop on applied bayesian modelling in R with brms
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Oct 21, 2020 - TeX
Can visualization alleviate dichotomous thinking? Effects of visual representations on the cliff effect
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Apr 21, 2021 - HTML
BRMS [Drools] :: BRMS Decision Tables Lessons Learned
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Aug 6, 2017
BRMS (Drools) Rules Example application to deploy as KJar into Kie-Server
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Oct 15, 2020 - Java
Quick Loan Bank UI is an example demo invoking a decision service based on Red Hat Decision Manager 7.
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Jan 24, 2019 - CSS
Workshop to introduce participants to rstanarm and brms.
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Nov 25, 2018 - R
scikit-learn wrapper for generalized linear mixed model methods in R
python
statistics
time-series
scikit-learn
bayesian
bayesian-inference
stan
brms
mixed-effects
bayesian-statistics
statistical-models
mixed-models
lme4
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Jan 7, 2021 - Python
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Hi,
is there any plan to implement the Generalized Pareto Distribution in
brms(paul-buerkner/brms#110 (comment))? I am playing around with an extreme values analysis and it looks like extremes collected as Peak Over Threshold are better represented by the GPD instead of the generalized extreme value distribution, which I am so happy to see already in `b