Calculate the win statistics (win ratio, net benefit and win odds) for prioritized multiple endpoints, plot the win statistics and win proportions over study time if at least one time-to-event endpoint is analyzed, and simulate datasets with dependent endpoints. The package can handle any type of outcomes (continuous, ordinal, binary, time-to-event) and allow users to perform stratified analysis, inverse probability of censoring weighting (IPCW) and inverse probability of treatment weighting (IPTW) analysis.

Provides simple, fast functions for maximum likelihood and Bayesian estimates of wildlife population parameters, suitable for use with simulated data or bootstraps. Early versions were indeed quick and dirty, but optional error-checking routines and meaningful error messages have been added. Includes single and multi-season occupancy, closed capture population estimation, survival, species richness and distance measures.

In the course of a genome-wide association study, the situation often arises that some phenotypes are known with greater precision than others.
    It could be that some individuals are known to harbor more micro-environmental variance than others.
    In the case of inbred strains of model organisms, it could be the case that more organisms were observed from some strains than others, so the strains with more organisms have better-estimated means.
    Package 'wISAM' handles this situation by allowing for weighting of each observation according to residual variance.
    Specifically, the 'weight' parameter to the function conduct_scan() takes the precision of each observation (one over the variance).

Adds ... to a function's argument list so that it can
    tolerate non-matching arguments.

A set of functions to run code 'with' safely and
    temporarily modified global state. Many of these functions were
    originally a part of the 'devtools' package, this provides a simple
    package with limited dependencies to provide access to these
    functions.