计算机科学
阿卡克信息准则
线性模型
广义线性模型
航程(航空)
贝叶斯概率
多级模型
协方差
泊松分布
负二项分布
背景(考古学)
统计
数学
人工智能
机器学习
古生物学
材料科学
复合材料
生物
作者
Paul‐Christian Bürkner
标识
DOI:10.18637/jss.v080.i01
摘要
The brms package implements Bayesian multilevel models in R using the probabilistic programming language Stan.A wide range of distributions and link functions are supported, allowing users to fit -among others -linear, robust linear, binomial, Poisson, survival, ordinal, zero-inflated, hurdle, and even non-linear models all in a multilevel context.Further modeling options include autocorrelation of the response variable, user defined covariance structures, censored data, as well as meta-analytic standard errors.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 the Watanabe-Akaike information criterion and leave-one-out cross-validation.
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