#
multilevel-models
Here are 48 public repositories matching this topic...
an R package for structural equation modeling and more
missing-data
multilevel-models
factor-analysis
latent-variables
multivariate-analysis
structural-equation-modeling
growth-curve-models
psychometrics
statistical-modeling
path-analysis
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Updated
Aug 15, 2021 - R
rstanarm R package for Bayesian applied regression modeling
r
bayesian-methods
rstan
bayesian
multilevel-models
bayesian-inference
stan
r-package
rstanarm
bayesian-data-analysis
bayesian-statistics
statistical-modeling
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Updated
Jun 21, 2021 - R
A meta-analysis package for R
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Updated
Aug 7, 2021 - R
powerlmm R package for power calculations for two- and three-level longitudinal multilevel/linear mixed models.
power
longitudinal-data
multilevel-models
r-package
linear-mixed-models
linear-mixed-effects-modelling
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Updated
Oct 27, 2020 - HTML
A unified framework for data analysis in R based on GLM/GLMM
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Updated
Aug 15, 2021 - R
visualization
data-science
ggplot2
r
statistics
linear-regression
toolbox
data-analysis
multilevel-models
r-package
anova
linear-models
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Updated
Jul 12, 2021 - R
Covers the basics of mixed models, mostly using @lme4
r
multilevel-models
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
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Nov 30, 2020 - R
mhunter1
commented
Feb 5, 2021
We want a function that takes a model gives back either (a) a model with the likelihood (or more generally the fit function) evaluated or (b) the fit function value. The function should evaluate the model at a fixed set of parameters and options (e.g., the MVN integration options).
An R package for Bayesian structural equation modeling
cran
missing-data
multilevel-models
factor-analysis
bayesian-statistics
latent-variables
multivariate-analysis
structural-equation-modeling
growth-curve-models
psychometrics
statistical-modeling
path-analysis
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Updated
Aug 14, 2021 - R
Spatial Analysis Notes
maps
cross-validation
spatial-analysis
multilevel-models
geographically-weighted-regression
spatial-econometrics
moran-i
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Updated
Apr 30, 2021 - Jupyter Notebook
Material for a Bayesian statistics workshop
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Updated
Dec 3, 2020 - R
metaSEM package
missing-data
multilevel-models
r-package
meta-analysis
multivariate-analysis
structural-equation-modeling
structural-equation-models
meta-analytic-sem
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Updated
Aug 4, 2021 - R
Tools for multiple imputation in multilevel modeling
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Updated
Feb 5, 2021 - R
statistics
rstats
multilevel-models
hierarchical-models
mixed-models
quantitative-methods
tidyversity
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Updated
Aug 2, 2018 - R
data-science
statistics
linear-regression
data-analysis
methodology
multilevel-models
workshop-materials
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Updated
Mar 8, 2021 - R
Case studies with Bayesian methods
python
evaluation
jupyter-notebook
bayesian-methods
multilevel-models
bayesian-inference
mcmc
pymc3
bayesian-data-analysis
hierarchical-models
statistical-models
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Updated
Aug 10, 2021 - Jupyter Notebook
This repository includes Matlab codes/routines that were used in our manuscript entitled "Importance sampling for a robust and efficient multilevel Monte Carlo estimator for stochastic reaction networks" that can be found in this preprint: https://arxiv.org/abs/1911.06286
matlab
biological-simulations
stochastic-process
multilevel-models
numerical-simulations
stochastic-simulation-algorithm
numerical-analysis
monte-carlo-methods
biological-networks
multilevel
importance-sampling
multilevel-monte-carlo
stochastic-reaction-networks
stochastic-biological-systems
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Jun 28, 2020 - MATLAB
# kaefa kwangwoon automated exploratory factor analysis for improving research capability to identify unexplained factor structure with complexly cross-classified multilevel structured data in R environment
exploratory-data-analysis
mirt
model-selection
exploratory-factor-analysis
exploratory-ifa
multilevel-models
factor-analysis
automated-analysis
automated-machine-learning
automated-reasoning
machine-unlearning
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Nov 1, 2019 - R
Curated list of the sources about multilevel models
statistics
modeling
awesome-list
multilevel-models
mixed-effects
linear-mixed-models
mixed-models
lmm
multilevel-analysis
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Updated
Jun 11, 2021
Individual-based and multi-scale evolutionary model, dedicated to in silico experimental evolution.
simulation
evolution
multilevel-models
complex-systems
speciation
bacterial-genomes
evolutionary-dynamics
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Updated
Nov 17, 2020 - C++
rstanarm R package for Bayesian applied regression modeling
r
bayesian-methods
rstan
bayesian
multilevel-models
bayesian-inference
stan
r-package
rstanarm
bayesian-data-analysis
bayesian-statistics
statistical-modeling
ltjmm
latent-time-joint-mixed-models
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Updated
Jan 26, 2021 - R
Code to Estévez & Takács (in preparation) 'Network brokerage and workplace gossip'.
brokerage
reputation
multilevel-models
multiplex-networks
lme4
workplace-gossip
signed-graphs
structural-holes
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Updated
May 6, 2021 - R
Slides for Godly Governance paper
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Updated
Jun 25, 2018 - HTML
Senior thesis on statistics of routes
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Updated
Nov 7, 2016 - HTML
A multi-level graph partitioning by vertex separator algorithm I implemented for my senior project
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Updated
Jan 27, 2017 - Roff
bayesian | hierarchical-models
healthcare
multilevel-models
predictive-modeling
hierarchical-models
mortality-estimation
bayesian-models
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Updated
Dec 19, 2019 - HTML
Raw files for a document providing an overview of mixed models from varying perspectives.
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Nov 24, 2017 - R
This repository contains an implementation of the Multilevel Ensemble Kalman Bucy Filter. Code for HPC implementation with MPI cores, designed to run on Supercomputer Shaheen is also included. The algorithm deals with linear Gaussian filtering problems in continuous time and has appealing performance in high dimensions.
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Updated
May 19, 2021 - Jupyter Notebook
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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