Analyzes non-normal data via the Multiple Comparison Procedures and Modeling approach (MCP-Mod). Many functions rely on the 'DoseFinding' package. This package makes it so the user does not need to provide or calculate the mu vector and S matrix. Instead, the user typically supplies the data in its raw form, and this package will calculate the needed objects and passes them into the 'DoseFinding' functions. If the user wishes to primarily use the functions provided in the 'DoseFinding' package, a singular function (prepareGen()) will provide mu and S. The package currently handles power analysis and the MCP-Mod procedure for negative binomial, Poisson, and binomial data. The MCP-Mod procedure can also be applied to survival data, but power analysis is not available.
	Bretz, F., Pinheiro, J. C., and Branson, M. (2005) <doi:10.1111/j.1541-0420.2005.00344.x>.
	Buckland, S. T., Burnham, K. P. and Augustin, N. H. (1997) <doi:10.2307/2533961>.
	Pinheiro, J. C., Bornkamp, B., Glimm, E. and Bretz, F. (2014) <doi:10.1002/sim.6052>.

An efficient implementation of the MCPMod (Multiple Comparisons and Modeling) method to support a simulation-based design and analysis of dose-finding trials with normally distributed, binary and count endpoints (Bretz et al. (2005) <doi:10.1111/j.1541-0420.2005.00344.x>).

Calculation of signed root deviance profiles for linear combinations of parameters in a generalized linear model. Multiple tests and simultaneous confidence intervals are provided.

Tools for monitoring progress during parallel processing. Lightweight package which acts as a wrapper around mclapply() and adds a progress bar to it in 'RStudio' or 'Linux' environments. Simply replace your original call to mclapply() with pmclapply(). A progress bar can also be displayed during parallelisation via the 'foreach' package. Also included are functions to safely print messages (including error messages) from within parallelised code, which can be useful for debugging parallelised R code.

Performs the execution of the main procedures of multiple 
    comparisons in the literature, Scott-Knott (1974) <http://www.jstor.org/stable/2529204>, 
    Batista (2016) <http://repositorio.ufla.br/jspui/handle/1/11466>, including graphic representations 
    and export to different extensions of its results. An additional 
    part of the package is the presence of the performance evaluation 
    of the tests (Type I error per experiment and the power). This will 
    assist the user in making the decision for the chosen test.