关键词: 共性分析, 广义混合效应模型, 层次分割, 边际R2, 多元回归, 相对重要性, 方差分解, 零膨胀模型

Abstract: glmm.hp is an R package designed to evaluate the relative importance of collinear predictors within generalized linear mixed models (GLMMs). Since its initial release in January 2022, it has been rapidly gained recognition and popularity among ecologists. However, the previous glmm.hp package was limited to work GLMMs derived exclusively from the lme4 and nlme packages. The latest glmm.hp package has extended its functions. It has integrated results obtained from the glmmTMB package, thus enabling it to handle zero-inflated generalized linear mixed models (ZIGLMMs) effectively. Furthermore, it has introduced the new functionalities of commonality analysis and hierarchical partitioning for multiple linear regression models by considering both unadjusted R₂ and adjusted R₂

Key words: commonality analysis, GLMMs, hierarchical partitioning, marginal R2, multiple regression, relative importance, variance partitioning, zero-Inflated model