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Morisita-Horn指数对小物种的影响较小，这在很大程度上是因为该方法抗欠采样的原因。同时，它使该方法对小物种携带的模式不敏感。
在perMANOVA和指示物种分析中增加块或组的大数目到1,000
新Summary | Write距离矩阵选项
物种面积曲线的自举置信区间
PCA选项将预测方程写入文本文件
PCA选项将特征值从随机化写入电子表格
使用Peres-Neto et al.(2006)方法在CCA中增加R²perm调整方差
使用Peres-Neto et al.(2006)方法在RDA中增加Ezekiel和R²perm调整方差
添加了拟合方法，包括基于随机的方法到NMS中
Dust bunny指数从多元正态到dust bunny分布作为出发度量
新Summary | Write距离矩阵选项：
√ 只写次对角线距离（对于连续样本）
√ 写对子对角线距离（配对样本）
Principal Coordinates Analysis (PCoA)
Principal Coordinates Analysis is an eigenanalysis technique similar to PCA, except that one extracts eigenvectors from a distance matrix among sample units (rows), rather than from a correlation or covariance matrix. In PCoA one can use any square symmetrical distance matrix, including semi-metrics such as Sorensen distance, as well as metric distance measures such as Euclidean distance.
SumF
A simple but surprisingly effective method of comparing two or more groups of sample units is to calculate a univariate F statistic for each variable, sum those F statistics, then compare the resulting sum to the distribution of F statistics based on randomizing the data under the null hypothesis. This is the core of the SumF method, as suggested by Edginton (1995). Good performance of this method, as compared to distance-based methods, was found by Warton and Hudson (2004). An advantage to this method is that by aggregating a simple, well-known test statistic, the F ratio, into a summary statistic across multiple variables, we simultaneously obtain information about differences between groups both across all variables and for individual variables. Thus for the generic question, "Do communities differ between groups?", the SumF method allows us to report an answer for communities as a whole as well as for individual species.
New Matrix Format
Categorical values can now be either numerical or text with a maximum of 20 characters
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