ABI Development: An extensible software platform for integrating multiple sources of data and uncertainty using hierarchical statistical models

分层统计模型允许估计复杂生物数据中的模式，同时考虑到诸如时间或空间模式或共享采样单元等关系。统计研究人员已经开发了用于分层模型的各种分析算法，但对于实验或野外生物学家等从业者来说是不可用的。其中包括许多类型的马尔可夫链蒙特卡罗，以及序贯蒙特卡罗、重要性抽样、近似贝叶斯计算和其他数值方法和近似。此外，还有许多更高级的算法将它们用作模型选择、模型平均、最大似然估计、生成预测等方法的组件。该项目将涉及开发一个开放源码的、可扩展的软件环境，以便灵活地组合分层模型和算法。该软件将包括低级组件，其中将执行算法以提高速度；高级组件，其中可以从R统计软件环境中组合和管理算法；以及中级组件，用于连接前两者。许多算法将被实施和传播，以便使用新软件进行应用。此外，它将为未来新的和改进的算法的持续开发和共享提供基础。分层统计模型被用于生物学的许多领域，以提供可靠的结论和管理指导，以利用所有可用的数据。应用领域包括野生动物保护和管理、生态系统过程，如碳循环、生物生长和发育，以及细胞生化网络。在所有这些领域，生物学家需要使用复杂的数据来估计他们的研究系统中发生变化的过程和速度。该项目将提供下一代软件，使许多研究人员可以使用各种算法来实现这一目标。这些算法将通过允许研究人员以高效的方式从其数据中提取最多的信息来促进研究工作流程。

Hierarchical statistical models allow estimation of patterns in complex biological data while accounting for relationships such as temporal or spatial patterns or shared sampling units. A great variety of analysis algorithms for hierarchical models have been developed by statistical researchers but are unavailable to practitioners such as experimental or field biologists. These include many types of Markov chain Monte Carlo, as well as sequential Monte Carlo, importance sampling, approximate Bayesian computation, and other numerical methods and approximations. In addition, there are many higher-level algorithms that use these as components of methods for model selection, model averaging, maximum likelihood estimation, generating predictions, and more. This project will involve development of an open source, extensible software environment for flexible composition of hierarchical models and algorithms. The software will include low-level components in which algorithms will be executed for speed, high-level components in which algorithms can be composed and managed from the R statistical software environment, and middle-level components to interface the first two. Many algorithms will be implemented and disseminated for application using the new software. Moreover, it will provide a foundation for ongoing development and sharing of new and improved algorithms in the future.Hierarchical statistical models are used in many domains of biology to provide robust conclusions and management guidance that harness all available data. Areas of application include wildlife conservation and management, ecosystem processes such as carbon cycling, organismal growth and development, and cellular biochemical networks. In all of these areas, biologists need to use complicated data to estimate the processes and rates of change occurring in their study system. This project will provide a next generation of software to make available numerous algorithms to many researchers to achieve this goal. These algorithms will facilitate research workflows by allowing researchers to extract the most information from their data in an efficient manner.