User-friendly Shiny apps for designing and evaluating phase I cancer clinical trials, with the aim to estimate the maximum tolerated dose (MTD) of a novel drug, using a Bayesian decision procedure based on logistic regression.

Function ModEstM() is the only one of this package, it estimates the modes of an empirical univariate distribution. It relies on the stats::density() function, even for input control. Due to very good performance of the density estimation, computation time is not an issue. The multiple modes are handled using dplyr::group_by(). For conditions and rates of convergences, see Eddy (1980) <doi:10.1214/aos/1176345080>.

Compute important quantities when we consider stochastic systems that are observed continuously. 
             Such as, Cost model, Limiting distribution, Transition matrix, Transition distribution and Occupancy matrix. 
             The methods are described, for example, Ross S. (2014), Introduction to Probability Models. Eleven Edition. Academic Press.

Analyses species distribution models and evaluates their performance. It includes functions for variation partitioning, extracting variable importance, computing several metrics of model discrimination and calibration performance, optimizing prediction thresholds based on a number of criteria, performing multivariate environmental similarity surface (MESS) analysis, and displaying various analytical plots. Initially described in Barbosa et al. (2013) <doi:10.1111/ddi.12100>.

Generation of mock data from a real dataset using rank normal inverse transformation.

Algorithms for multivariate outlier detection when missing values
    occur. Algorithms are based on Mahalanobis distance or data depth.
    Imputation is based on the multivariate normal model or uses nearest
    neighbour donors. The algorithms take sample designs, in particular
    weighting, into account. The methods are described in Bill and Hulliger
    (2016) <doi:10.17713/ajs.v45i1.86>.

Power of non-parametric Mann-Kendall test and Spearman’s Rho test is highly influenced by serially correlated data. To address this issue, trend tests may be applied on the modified versions of the time series data by  Block Bootstrapping (BBS), Prewhitening (PW) , Trend Free Prewhitening (TFPW), Bias Corrected Prewhitening and Variance Correction Approach by calculating effective sample size.
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