Perform the Meta-Analysis for Pathway Enrichment Analysis (MAPE) (MetaPath)
Perform the Meta-analysis for Pathway Enrichment (MAPE) methods introduced by Shen and Tseng (2010). It includes functions to automatically perform MAPE_G (integrating multiple studies at gene level), MAPE_P (integrating multiple studies at pathway level) and MAPE_I (a hybrid method integrating MAEP_G and MAPE_P methods). In the simulation and real data analyses in the paper, MAPE_G and MAPE_P have complementary advantages and detection power depending on the data structure. In general, the integrative form of MAPE_I is recommended to use. In the case that MAPE_G (or MAPE_P) detects almost none pathway, the integrative MAPE_I does not improve performance and MAPE_P (or MAPE_G) should be used. Reference: Shen, Kui, and George C Tseng. Meta-analysis for pathway enrichment analysis when combining multiple microarray studies.Bioinformatics (Oxford, England) 26, no. 10 (April 2010): 1316-1323. doi:10.1093/bioinformatics/btq148. http://…/20410053.
Efficient Computation of the t* Statistic of Bergsma and Dassios (2014) (TauStar)
Computes the t* statistic corresponding to the tau star population coefficient introduced by Bergsma and Dassios (Bernoulli 20(2), 2014, 1006-1028) and does so in O(n^2*log(n)) time. Can provide both the V-statistic and U-statistic related to the tau star measure depending on user preference.
Statistical Analysis of Non-Detects (STAND)
Provides functions for the analysis of occupational and environmental data with non-detects. Maximum likelihood (ML) methods for censored log-normal data and non-parametric methods based on the product limit estimate (PLE) for left censored data are used to calculate all of the statistics recommended by the American Industrial Hygiene Association (AIHA) for the complete data case. Functions for the analysis of complete samples using exact methods are also provided for the lognormal model. Revised from 2007-11-05 ‘survfit~1’.
Categorical Data Analysis and Visualization (extracat)
Categorical Data Analysis and Visualization.
Lightweight Logging for R Scripts (luzlogr)
Provides flexible but lightweight logging facilities for R scripts. Supports priority levels for logs and messages, flagging messages, capturing script output, switching logs, and logging to files or connections.

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