Multinomial count data are common in trophic ecology, spatial ecology, and community ecology. Compared to generalized linear models (GLMs) for univariate data, fewer statistical programs have been developed for the multivariate counterpart of GLMs. Studies of spatiotemporal community dynamics and spatial ecology on large spatial scales may generate large or big data on compositional counts or proportions. Fast, reliable computational methods can promote applications of multivariate generalized linear models (MGLMs) in predictive ecology and ecological informatics. The main objective of this study was to build Dirichlet-multinomial (D-M) models for compositional counts using frequentist's maximum likelihood estimation (MLE) with template model builder (TMB), full Bayesian estimation with programs JAGS and Stan, and variational Bayes in Stan. Bayesian and frequentist D-M models were applied to the compositional counts of radio telemetry locations of ring-necked pheasants (Phasianus colchicus) and American beaver (Castor canadensis) in different habitat-cover types to estimate habitat selection index. Bayesian D-M models with JAGS and Stan produced similar estimates as TMB programs. However, TMB programs ran a few hundred times faster than the Bayesian models. Template model builder and Stan are flexible in building complex statistical models and can accommodate a large number of parameters and random effects. The joint use of TMB and Stan may help develop MGLMs and multivariate generalized linear mixed models for large ecological data.

在营养生态学，空间生态学和社区生态学中，多项式计数数据是常见的。与用于单变量数据的广义线性模型（GLM）相比，针对GLM的多元对应物开发的统计程序更少。在较大的空间尺度上研究时空群落动力学和空间生态学，可能会产生有关组成数量或比例的大数据或大数据。快速，可靠的计算方法可以促进多元广义线性模型（MGLM）在预测生态学和生态信息学中的应用。这项研究的主要目的是使用模板模型生成器（TMB），常客的最大似然估计（MLE），JAGS和Stan程序进行全贝叶斯估计以及Stan中的变分贝叶斯模型来建立Dirichlet多项式（DM）模型用于成分计数。在不同的生境覆盖类型下，将Phasianus colchicus和美洲海狸（Castor canadensis）估算出生境选择指数。带有JAGS和Stan的贝叶斯DM模型产生的估算值与TMB程序相似。但是，TMB程序的运行速度比贝叶斯模型快了几百倍。模板模型构建器和Stan可以灵活地构建复杂的统计模型，并且可以容纳大量参数和随机效应。TMB和Stan的联合使用可能有助于开发MGLM和用于大型生态数据的多元广义线性混合模型。