* Summary Measures for Clinical Trials with Survival Outcomes* (

**ClinicalTrialSummary**)

Provides estimates of the several summary measures for clinical trials including the average hazard ratio, the weighted average hazard ratio, the restricted superiority probability ratio, the restricted mean survival difference and the ratio of restricted mean times lost, based on the short-term and long-term hazard ratio model (Yang, 2005 <doi:10.1093/biomet/92.1.1>) which accommodates various non-proportional hazards scenarios. The inference procedures and the asymptotic results for the summary measures are discussed in Yang (2017, pre-print).

*(*

**Normalizing Transformation Functions****bestNormalize**)

Estimate a suite of normalizing transformations, including a new technique based on ranks which can guarantee normally distributed transformed data if there are no ties: Ordered Quantile Normalization. The package is built to estimate the best normalizing transformation for a vector consistently and accurately. It implements the Box-Cox transformation, the Yeo-Johnson transformation, three types of Lambert WxF transformations, and the Ordered Quantile normalization transformation.

*(*

**Survival Analysis for Pathways****survClip**)

Survival analysis using pathway topology. Data reduction techniques with graphical models are used to identify pathways or modules that are associated to survival.

*(*

**Bayesian Analysis to Compare Models using Resampling Statistics****tidyposterior**)

Bayesian analysis used here to answer the question: ‘when looking at resampling results, are the differences between models ‘real’?’ To answer this, a model can be created were the performance statistic is the resampling statistics (e.g. accuracy or RMSE). These values are explained by the model types. In doing this, we can get parameter estimates for each model’s affect on performance and make statistical (and practical) comparisons between models. The methods included here are similar to Benavoli et al (2017) <http://…/16-305.html>.

*(*

**Play with the Tribe of Attributes****tribe**)

Functions to make manipulation of object attributes easier. It also contains a few functions that extend the ‘dplyr’ package for data manipulation, and it provides new pipe operators, including the pipe ‘%@>%’ similar to the ‘magrittr’ ‘%>%’, but with the additional functionality to enable attributes propagation.

*(*

**Transforms Contingency Tables to Data Frames, and Analyses Them****flatr**)

Contingency Tables are a pain to work with when you want to run regressions. This package takes them, flattens them into a long data frame, so you can more easily analyse them! As well, you can calculate other related statistics. All of this is done so in a ‘tidy’ manner, so it should tie in nicely with ‘tidyverse’ series of packages.