Design a Bayesian seamless multi-arm biomarker-enriched phase II/III 
             design with the survival endpoint with allowing sample size re-estimation.
             James M S Wason, Jean E Abraham, Richard D Baird, Ioannis Gournaris, Anne-Laure Vallier, James D Brenton, Helena M Earl, Adrian P Mander (2015) <doi:10.1038/bjc.2015.278>.
             Guosheng Yin, Nan Chen, J. Jack Lee (2018) <doi:10.1007/s12561-017-9199-7>.
             Ying Yuan, Beibei Guo, Mark Munsell, Karen Lu, Amir Jazaeri (2016) <doi:10.1002/sim.6971>.

A novel semi-supervised machine learning algorithm to predict phenotype event times using Electronic Health Record (EHR) data.

In a clinical trial with repeated measures designs, outcomes are often taken from subjects at fixed time-points.  The focus of the trial may be to compare the mean outcome in two or more groups at some pre-specified time after enrollment. In the presence of missing data auxiliary assumptions are necessary to perform such comparisons.  One commonly employed assumption is the missing at random assumption (MAR).   The 'samon' package allows the user to perform a (parameterized) sensitivity analysis of this assumption.  In particular it can be used to examine the sensitivity of tests in the difference in outcomes to violations of the MAR assumption.  The sensitivity analysis can be performed under two scenarios, a) where the data exhibit a monotone missing data pattern (see the samon() function), and, b) where in addition to a monotone missing data pattern the data exhibit intermittent missing values (see the samonIM() function).

Evaluating the biasing impact of geographic features such as airports, cities, roads, rivers in datasets of coordinates based biological collection datasets, by Bayesian estimation of the parameters of a Poisson process. Enables also spatial visualization of sampling bias and includes a set of convenience functions for publication level plotting. Also available as 'shiny' app. The reference for the methodology is: Zizka et al. (2020) <doi:10.1111/ecog.05102>.