* Rattle Datasets* (

**rattle.data**)

Contains the datasets used as default examples by the rattle package. The datasets themselves can be used independently of the rattle package to illustrate analytics, data mining, and data science tasks.

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**R Bayesian Evidence Synthesis Tools****RBesT**)

Tool-set to support Bayesian evidence synthesis. This includes meta-analysis, (robust) prior derivation from historical data, operating characteristics and analysis (1 and 2 sample cases).

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**Simulation of Correlated Data with Multiple Variable Types****SimMultiCorrData**)

Generate continuous (normal or non-normal), binary, ordinal, and count (Poisson or Negative Binomial) variables with a specified correlation matrix. It can also produce a single continuous variable. This package can be used to simulate data sets that mimic real-world situations (i.e. clinical data sets, plasmodes). All variables are generated from standard normal variables with an imposed intermediate correlation matrix. Continuous variables are simulated by specifying mean, variance, skewness, standardized kurtosis, and fifth and sixth standardized cumulants using either Fleishman’s Third-Order (<DOI:10.1007/BF02293811>) or Headrick’s Fifth-Order (<DOI:10.1016/S0167-9473(02)00072-5>) Polynomial Transformation. Binary and ordinal variables are simulated using a modification of GenOrd’s ordsample function. Count variables are simulated using the inverse cdf method. There are two simulation pathways which differ primarily according to the calculation of the intermediate correlation matrix. In Method 1, the intercorrelations involving count variables are determined using a simulation based, logarithmic correlation correction (adapting Yahav and Shmueli’s 2012 method, <DOI:10.1002/asmb.901>). In Method 2, the count variables are treated as ordinal (adapting Barbiero and Ferrari’s 2015 modification of GenOrd, <DOI:10.1002/asmb.2072>). There is an optional error loop that corrects the final correlation matrix to be within a user-specified precision value of the target matrix. The package also includes functions to calculate standardized cumulants for theoretical distributions or from real data sets, check if a target correlation matrix is within the possible correlation bounds (given the distributions of the simulated variables), summarize results (numerically or graphically), to verify valid power method pdfs, and to calculate lower standardized kurtosis bounds.

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**Joint Regression Modelling****JRM**)

Routines for fitting various joint regression models, with several types of covariate effects, in the presence of associated error equations, endogeneity, non-random sample selection or partial observability.

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**Preliminary Data Visualisation****visdat**)

Create preliminary exploratory data visualisations of an entire dataset to identify problems or unexpected features using ‘ggplot2’.