Rawan Allozi

Rawan Allozi

9 packages on CRAN

MultiVarMI

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Fully parametric Bayesian multiple imputation framework for massive multivariate data of different variable types as seen in Demirtas, H. (2017) <doi:10.1007/978-981-10-3307-0_8>.

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Modeling the correlation transitions under specified distributional assumptions within the realm of discretization in the context of the latency and threshold concepts. The details of the method are explained in Demirtas, H. and Vardar-Acar, C. (2017) <DOI:10.1007/978-981-10-3307-0_4>.

MultiRNG

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Pseudo-random number generation for 11 multivariate distributions: Normal, t, Uniform, Bernoulli, Hypergeometric, Beta (Dirichlet), Multinomial, Dirichlet-Multinomial, Laplace, Wishart, and Inverted Wishart. The details of the method are explained in Demirtas (2004) <DOI:10.22237/jmasm/1099268340>.

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Generation of samples from a mix of binary, ordinal and continuous random variables with a pre-specified correlation matrix and marginal distributions. The details of the method are explained in Demirtas et al. (2012) <DOI:10.1002/sim.5362>.

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Generation of a chosen number of count, binary, ordinal, and continuous random variables, with specified correlations and marginal properties.

PoisNonNor

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Generation of count (assuming Poisson distribution) and continuous data (using Fleishman polynomials) simultaneously.

UnivRNG

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Pseudo-random number generation of 17 univariate distributions.

BivUnifBin

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Simulation of bivariate uniform data with a full range of correlations based on two beta densities and computation of the tetrachoric correlation (correlation of bivariate uniform data) from the phi coefficient (correlation of bivariate binary data) and vice versa.

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Generation of multiple count, binary, ordinal and normal variables simultaneously given the marginal characteristics and association structure.