Applies Beta Control Charts to defined values. The Beta Chart presents control limits based on the Beta probability distribution, making it suitable for monitoring fraction data from a Binomial distribution as a replacement for p-Charts. The Beta Chart has been applied in three real studies and compared with control limits from three different schemes. The comparative analysis showed that: (i) the Beta approximation to the Binomial distribution is more appropriate for values confined within the [0, 1] interval; and (ii) the proposed charts are more sensitive to the average run length (ARL) in both in-control and out-of-control process monitoring. Overall, the Beta Charts outperform the Shewhart control charts in monitoring fraction data. For more details, see Ângelo Márcio Oliveira Sant’Anna and Carla Schwengber ten Caten (2012) <doi:10.1016/j.eswa.2012.02.146>.

Calculates the prices of European options based on the universal solution provided by Bakshi, Cao and Chen (1997) <doi:10.1111/j.1540-6261.1997.tb02749.x>. This solution considers stochastic volatility, stochastic interest and random jumps. Please cite their work if this package is used.

It is very common nowadays for a study to collect multiple
    features and appropriately integrating multiple longitudinal features
    simultaneously for defining individual clusters becomes increasingly
    crucial to understanding population heterogeneity and predicting
    future outcomes.  'BCClong' implements a Bayesian consensus clustering
    (BCC) model for multiple longitudinal features via a generalized
    linear mixed model. Compared to existing packages, several key
    features make the 'BCClong' package appealing: (a) it allows
    simultaneous clustering of mixed-type (e.g., continuous, discrete and
    categorical) longitudinal features, (b) it allows each longitudinal
    feature to be collected from different sources with measurements taken
    at distinct sets of time points (known as irregularly sampled
    longitudinal data), (c) it relaxes the assumption that all features
    have the same clustering structure by estimating the feature-specific
    (local) clusterings and consensus (global) clustering.