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1/ Mixed models are a powerful statistical tool for analyzing complex data with both fixed and random effects. R has several great packages for fitting mixed models. #rstats #datascience 
2/ One of the most popular packages for mixed models in R is "lme4". This package provides functions for fitting linear and generalized linear mixed models, including models with crossed and nested random effects. #rstats #lme4 cran.r-project.org/web/packages/l…
3/ Another popular mixed model package in R is "nlme". It has similar functionality to "lme4" but is designed to handle longitudinal or repeated-measures data. #rstats #nlme cran.r-project.org/web/packages/n…
Volume 100 of @jstatsoft: Software for Bayesian Statistics
Guest editors: @micameletti & @precariobecario
20 contributions on a wide range of methods #rstats #python #bayesian #inla @mcmc_stan #nimble #brms #rjags
URL: jstatsoft.org/v100
Guest editors: @micameletti & @precariobecario
20 contributions on a wide range of methods #rstats #python #bayesian #inla @mcmc_stan #nimble #brms #rjags
URL: jstatsoft.org/v100
Van Niekerk, Bakka, Rue, Schenk:
New Frontiers in Bayesian Modeling Using the INLA Package in R
#rstats #inla #bayesian
doi.org/10.18637/jss.v…
New Frontiers in Bayesian Modeling Using the INLA Package in R
#rstats #inla #bayesian
doi.org/10.18637/jss.v…
Michaud, De Valpine, Turek, Paciorek, Nguyen:
Sequential Monte Carlo Methods in the nimble and nimbleSMC R Packages
#rstats #bayesian #nimble
doi.org/10.18637/jss.v…
Sequential Monte Carlo Methods in the nimble and nimbleSMC R Packages
#rstats #bayesian #nimble
doi.org/10.18637/jss.v…
