此插件绘制并比较各种概率密度函数的尾部区域。在解释频率论 p 值或贝叶斯单边后验概率时，这种比较会很有帮助。该插件也是了解概率和统计中最常用密度的形状和形式的便捷方式。
通过将 .jmpaddin 文件拖到 JMP 窗口来安装插件后，您必须创建一个小的 JMP 表来描述要绘制的密度。每一行对应一个不同的密度，需要几个具有特定名称的列来完全指定每个密度和尾部切割点。表格设置好后，确保它处于焦点，然后单击加载项 > 概率密度尾区。
附加了几个示例表，名为 pdta_normal.jmp 的表生成以下输出。 它说明了单边 p 值和贝叶斯后验概率的等价性，即在对均值进行正常推断的情况下具有错误符号。
您可以通过三种不同的方式动态更改截止值：1。 单击并拖动垂直虚线参考线。 2. 在剪切框中输入值。 3. 单击并拖动滑块条中的菱形。
输入表必须包含以下列：
- 密度（用作图中的标签）
- 形式（密度种类的关键字，见下表）
- 尾巴（右或左）
- cut（绘制初始切割点的数值）
此外，您必须指定与所需密度参数相对应的数字列。以下摘自加载项的 JSL 代码显示了当前可用的密度以及随附的参数名称：
required = associativearray();
required["beta"] = {"alpha","beta","theta","sigma"};
required["betabinomial"] = {"p","n","delta"};
required["binomial"] = {"p","n"};
required["cauchy"] = {"mu","sigma"};
required["chisquare"] = {"df","noncentrality"};
required["exp"] = {"theta"};
required["f"] = {"dfnum","dfden","noncentrality"};
required["frechet"] = {"mu","sigma"};
required["gamma"] = {"alpha","scale","threshold"};
required["gengamma"] = {"mu","sigma","lambda"};
required["gammapoisson"] = {"lambda","sigma"};
required["glog"] = {"mu","sigma","lambda"};
required["hypergeometric"] = {"s","k","n"};
required["johnsonsb"] = {"gamma","delta","theta","sigma"};
required["johnsonsl"] = {"gamma","delta","theta","sigma"};
required["johnsonsu"] = {"gamma","delta","theta","sigma"};
required["lev"] = {"mu","sigma"};
required["loggengamma"] = {"mu","sigma","lambda"};
required["logistic"] = {"mu","sigma"};
required["loglogistic"] = {"mu","sigma"};
required["lognormal"] = {"mu","sigma"};
required["negbinomial"] = {"p","n"};
required["normal"] = {"mu","sigma"};
required["poisson"] = {"lambda"};
required["sev"] = {"mu","sigma"};
required["shash"] = {"gamma","delta","theta","sigma"};
required["t"] = {"mu","sigma","df"};
required["weibull"] = {"shape","scale","threshold"};
有关这些密度和参数的更多详细信息，请参阅 JSL 文档。
