A procedure for comparing multivariate samples associated with
    different groups. It uses principal component analysis to convert
    multivariate observations into a set of linearly uncorrelated statistical
    measures, which are then compared using a number of statistical methods. The
    procedure is independent of the distributional properties of samples and
    automatically selects features that best explain their differences, avoiding
    manual selection of specific points or summary statistics. It is appropriate
    for comparing samples of time series, images, spectrometric measures or
    similar multivariate observations. This package is described in Fachada et
    al. (2016) <doi:10.32614/RJ-2016-055>.

Fits Semiparametric Promotion Time Cure Models, taking into account (using a 
			 corrected score approach or the SIMEX algorithm) or not the measurement error
			 in the covariates, using a backfitting approach to maximize the likelihood.

A nature-inspired metaheuristic algorithm based on the echolocation behavior of microbats that uses frequency tuning to optimize problems in both continuous and discrete dimensions. This R package makes it easy to implement the standard bat algorithm on any user-supplied function. The algorithm was first developed by Xin-She Yang in 2010 (<DOI:10.1007/978-3-642-12538-6_6>, <DOI:10.1109/CINTI.2014.7028669>).

Provides infrastructure to accurately measure and compare
        the execution time of R expressions.

Provides functions to enhance the available 
            statistical analysis procedures in R by providing simple functions to 
            analysis and visualize the 16S rRNA data.Here we present a tutorial 
            with minimum working examples to demonstrate usage and dependencies.

Fit growth curves to various known microbial growth models automatically to estimate growth parameters. Growth curves can be plotted with their uncertainty band. Growth models are: modified Gompertz model (Zwietering et al. (1990) <doi:10.1128/aem.56.6.1875-1881.1990>), Baranyi model (Baranyi and Roberts (1994) <doi:10.1016/0168-1605%2894%2990157-0>), Rosso model (Rosso et al. (1993) <doi:10.1006/jtbi.1993.1099>) and linear model (Dantigny (2005) <doi:10.1016/j.ijfoodmicro.2004.10.013>).