TraitLab is a software package for simulating, fitting and analysing tree-like binary data under a stochastic Dollo model of evolution. The model also allows for rate heterogeneity through catastrophes, evolutionary events where many traits are simultaneously lost while new ones arise, and borrowing, whereby traits transfer laterally between species as well as through ancestral relationships. The core of the package is a Markov chain Monte Carlo (MCMC) sampling algorithm that enables the user to sample from the Bayesian joint posterior distribution for tree topologies, clade and root ages, and the trait loss, catastrophe and borrowing rates for a given data set. Data can be simulated according to the fitted Dollo model or according to a number of generalized models that allow for heterogeneity in the trait loss rate, biases in the data collection process and borrowing of traits between lineages. Coupled pairs of Markov chains can be used to diagnose MCMC mixing and convergence and to debias MCMC estimators. The raw data, MCMC run output, and model fit can be inspected using a number of useful graphical and analytical tools provided within the package or imported into other popular analysis programs. TraitLab is freely available and runs within the Matlab computing environment with its Statistics and Machine Learning toolbox, no other additional toolboxes are required.

翻译：TraitLab是一个软件包，用于在随机Dollo进化模型下模拟、拟合和分析树状二元数据。该模型还允许通过灾变（即许多性状同时丧失而新性状产生的进化事件）实现速率异质性，以及通过水平基因转移（即性状在物种间通过祖先关系之外的方式横向传递）实现性状借用。该软件包的核心是马尔可夫链蒙特卡洛（MCMC）采样算法，使用户能够从特定数据集的树拓扑结构、分支和根年龄以及性状丧失、灾变和借用率的贝叶斯联合后验分布中进行采样。数据可以根据拟合的Dollo模型或一系列广义模型进行模拟，这些模型允许性状丧失速率的异质性、数据收集过程中的偏差以及谱系间的性状借用。耦合马尔可夫链对可用于诊断MCMC的混合与收敛性，并消除MCMC估计量的偏差。原始数据、MCMC运行输出和模型拟合可通过软件包内提供的多种实用图形和分析工具进行检查，或导入至其他常用分析程序。TraitLab是免费软件，可在Matlab计算环境及其统计与机器学习工具箱中运行，无需其他额外工具箱。