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OrdNor - Concurrent Generation of Ordinal and Normal Data with Given Correlation Matrix and Marginal Distributions

Implementation of a procedure for generating samples from a mixed distribution of ordinal and normal random variables with a pre-specified correlation matrix and marginal distributions. The details of the method are explained in Demirtas et al. (2015) <DOI:10.1080/10543406.2014.920868>.

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2.38 score 2 stars 4 dependents 10 scripts 249 downloads

MultiRNG - Multivariate Pseudo-Random Number Generation

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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2.27 score 1 dependents 62 scripts 469 downloads

BinOrdNonNor - Concurrent Generation of Binary, Ordinal and Continuous Data

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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2.00 score 3 dependents 11 scripts 293 downloads

BinNonNor - Data Generation with Binary and Continuous Non-Normal Components

Generation of multiple binary and continuous non-normal variables simultaneously given the marginal characteristics and association structure based on the methodology proposed by Demirtas et al. (2012) <DOI:10.1002/sim.5362>.

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1.82 score 2 dependents 11 scripts 288 downloads

MultiOrd - Generation of Multivariate Ordinal Variates

A method for multivariate ordinal data generation given marginal distributions and correlation matrix based on the methodology proposed by Demirtas (2006) <DOI:10.1080/10629360600569246>.

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1.78 score 2 dependents 9 scripts 270 downloads

CorrToolBox - Modeling Correlational Magnitude Transformations in Discretization Contexts

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>.

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1.71 score 1 dependents 17 scripts 213 downloads

PoisBinOrdNor - Data Generation with Poisson, Binary, Ordinal and Normal Components

Generation of multiple count, binary, ordinal and normal variables simultaneously given the marginal characteristics and association structure. The details of the method are explained in Demirtas et al. (2012) <DOI:10.1002/sim.5362>.

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1.52 score 1 dependents 11 scripts 243 downloads

PoisNonNor - Simultaneous Generation of Count and Continuous Data

Generation of count (assuming Poisson distribution) and continuous data (using Fleishman polynomials) simultaneously. The details of the method are explained in Demirtas et al. (2012) <DOI:10.1002/sim.5362>.

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1.48 score 1 dependents 10 scripts 243 downloads

BinNor - Simultaneous Generation of Multivariate Binary and Normal Variates

Generating multiple binary and normal variables simultaneously given marginal characteristics and association structure based on the methodology proposed by Demirtas and Doganay (2012) <DOI:10.1080/10543406.2010.521874>.

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1.36 score 23 scripts 285 downloads

UnivRNG - Univariate Pseudo-Random Number Generation

Pseudo-random number generation of 17 univariate distributions proposed by Demirtas. (2005) <DOI:10.22237/jmasm/1114907220>.

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1.32 score 21 scripts 188 downloads

PoisBinOrdNonNor - Generation of Up to Four Different Types of Variables

Generation of a chosen number of count, binary, ordinal, and continuous random variables, with specified correlations and marginal properties. The details of the method are explained in Demirtas (2012) <DOI:10.1002/sim.5362>.

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1.26 score 18 scripts 213 downloads

PoisBinNonNor - Data Generation with Poisson, Binary and Continuous Components

Generation of multiple count, binary and continuous variables simultaneously given the marginal characteristics and association structure. Throughout the package, the word 'Poisson' is used to imply count data under the assumption of Poisson distribution. The details of the method are explained in Amatya et al. (2015) <DOI:10.1080/00949655.2014.953534>.

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1.15 score 14 scripts 227 downloads

PoisBinOrd - Data Generation with Poisson, Binary and Ordinal Components

Generation of multiple count, binary and ordinal variables simultaneously given the marginal characteristics and association structure. Throughout the package, the word 'Poisson' is used to imply count data under the assumption of Poisson distribution. The details of the method are explained in Amatya, A. and Demirtas, H. (2015) <DOI:10.1080/00949655.2014.953534>.

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1.08 score 12 scripts 191 downloads

PoisNor - Simultaneous Generation of Multivariate Data with Poisson and Normal Marginals

Generates multivariate data with count and continuous variables with a pre-specified correlation matrix. The count and continuous variables are assumed to have Poisson and normal marginals, respectively. The data generation mechanism is a combination of the normal to anything principle and a connection between Poisson and normal correlations in the mixture. The details of the method are explained in Yahav et al. (2012) <DOI:10.1002/asmb.901>.

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1.04 score 11 scripts 257 downloads

BivUnifBin - Generation of Bivariate Uniform Data and Its Relation to Bivariate Binary Data

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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1.00 score 4 scripts 257 downloads