Closed testing has been proved powerful for true discovery guarantee. The computation of closed testing is, however, quite burdensome. A general way to reduce computational complexity is to combine partial closed testings for some prespecified feature sets of interest. Partial closed testings are performed at Bonferroni-corrected alpha level to guarantee the lower bounds for the number of true discoveries in prespecified sets are simultaneously valid. For any post hoc chosen sets of interest, coherence property is used to get the lower bound. In this package, we implement closed testing with globaltest to calculate the lower bound for number of true discoveries, see Ningning Xu et.al (2021) <arXiv:2001.01541> for detailed description.

Measure the dependence structure between two random variables with a new correlation coefficient and extend it to hypothesis test, feature screening and false discovery rate control.

Extends the classical Newman studentized range statistic
  in various ways that can be applied to genome-scale transcriptomic
  or other expression data.

Interface to gather news from the 'News API', based on a multilevel query <https://newsapi.org/>. A personal API key is required.

Programmatically collect normalized news from
    (almost) any website. An 'R' clone of the
    <https://github.com/kotartemiy/newscatcher> 'Python' module.

Semissupervised model for geographical document classification (Watanabe 2018) <doi:10.1080/21670811.2017.1293487>. 
    This package currently contains seed dictionaries in English, German, French, Spanish, Italian, Russian, Hebrew, Arabic, Turkish, Japanese and Chinese (Simplified and Traditional).

Adding updates (version or bullet points) to the NEWS.md
    file.

Implements the routines to compare the survival curves with recurrent events, including the estimations of survival curves. The first model is a  model for recurrent event, when the data are correlated or not  correlated. It was proposed by Wang and Chang (1999) <doi:10.2307/2669690>. In the independent case, the survival function can be  estimated by the generalization of the limit product model of Pena (2001) <doi:10.1198/016214501753381922>.