Carrying out inferences about any linear combination of proportions and the ratio of two proportions.

Propagation of uncertainty using higher-order Taylor expansion and Monte Carlo simulation.

Computes two-sample confidence intervals for single, paired and independent proportions.

Implementation of propensity clustering and
        decomposition as described in Ranola et al. (2013) <doi:10.1186/1752-0509-7-21>. 
        Propensity decomposition can be viewed on the
        one hand as a generalization of the eigenvector-based
        approximation of correlation networks, and on the other hand as
        a generalization of random multigraph models and
        conformity-based decompositions.

Allows to parse 'Java' properties files in the context of
  'R Service Bus' applications.

Implements a procedure for forecasting time series data based on
    an additive model where non-linear trends are fit with yearly, weekly, and
    daily seasonality, plus holiday effects. It works best with time series
    that have strong seasonal effects and several seasons of historical data.
    Prophet is robust to missing data and shifts in the trend, and typically
    handles outliers well.

Calculate seat apportionment for legislative bodies with 
    various methods. The algorithms include divisor or highest averages methods
    (e.g. Jefferson, Webster or Adams), largest remainder methods and 
    biproportional apportionment.
    Gaffke, N. & Pukelsheim, F. (2008) <doi:10.1016/j.mathsocsci.2008.01.004>
    Oelbermann, K. F. (2016) <doi:10.1016/j.mathsocsci.2016.02.003>.

A package for selecting the most relevant features (genes) in the high-dimensional binary classification problems. The discriminative features are identified using analyzing the overlap between the expression values across both classes. The package includes functions for measuring the proportional overlapping score for each gene avoiding the outliers effect. The used measure for the overlap is the one defined in the "Proportional Overlapping Score (POS)" technique for feature selection. A gene mask which represents a gene's classification power can also be produced for each gene (feature). The set size of the selected genes might be set by the user. The minimum set of genes that correctly classify the maximum number of the given tissue samples (observations) can be also produced.