Parameters of a user-specified probability distribution are modelled by a multi-layer perceptron artificial neural network. This framework can be used to implement probabilistic nonlinear models including mixture density networks, heteroscedastic regression models, zero-inflated models, etc. following Cannon (2012) <doi:10.1016/j.cageo.2011.08.023>.

Converts customer transaction data (ID, purchase date) into a R6 class called customer.  The class stores various customer analytics calculations at the customer level. The package also contains functionality to convert data in the R6 class to data.frames that can serve as inputs for various customer analytics models.

Hansen's (1995) Covariate-Augmented
        Dickey-Fuller (CADF) test. The only required argument is y, the
        Tx1 time series to be tested. If no stationary covariate X is
        passed to the procedure, then an ordinary ADF test is
        performed. The p-values of the test are computed using the
        procedure illustrated in Lupi (2009).

Encrypts and decrypts strings using either the Caesar cipher or a
    pseudorandom number generation (using set.seed()) method.

Simulate plasma caffeine concentrations using population pharmacokinetic model described in Lee, Kim, Perera, McLachlan and Bae (2015) <doi:10.1007/s00431-015-2581-x>.

A time series usually does not have a uniform growth rate. Compound Annual Growth Rate measures the average annual growth over a given period. More details can be found in Bardhan et al. (2022) <DOI:10.18805/ag.D-5418>.

Provides functions for the estimation of the conditional Akaike
    information in generalized mixed-effect models fitted with (g)lmer() 
    from 'lme4', lme() from 'nlme' and gamm() from 'mgcv'.
    For a manual on how to use 'cAIC4', see Saefken et al. (2021) <doi:10.18637/jss.v099.i08>.

Allows to plot a number of information related to the interpretation of Correspondence Analysis' results. It provides the facility to plot the contribution of rows and columns categories to the principal dimensions, the quality of points display on selected dimensions, the correlation of row and column categories to selected dimensions, etc. It also allows to assess which dimension(s) is important for the data structure interpretation by means of different statistics and tests. The package also offers the facility to plot the permuted distribution of the table total inertia as well as of the inertia accounted for by pairs of selected dimensions. Different facilities are also provided that aim to produce interpretation-oriented scatterplots. Reference: Alberti 2015 <doi:10.1016/j.softx.2015.07.001>.