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jmgirard

circumplex:Analysis and Visualization of Circular Data

Circumplex models, which organize constructs in a circle around two underlying dimensions, are popular for studying interpersonal functioning, mood/affect, and vocational preferences/environments. This package provides tools for analyzing and visualizing circular data, including scoring functions for relevant instruments and a generalization of the bootstrapped structural summary method from Zimmermann & Wright (2017) <doi:10.1177/1073191115621795> and functions for creating publication-ready tables and figures from the results.

Maintained by Jeffrey Girard. Last updated 5 months ago.

circularcircumplexdata-analysisggplot2interpersonalpsychologyrcpparmadillotidyverseopenblascppopenmp

35.2 match 11 stars 6.54 score 52 scripts

donaldrwilliams

BGGM:Bayesian Gaussian Graphical Models

Fit Bayesian Gaussian graphical models. The methods are separated into two Bayesian approaches for inference: hypothesis testing and estimation. There are extensions for confirmatory hypothesis testing, comparing Gaussian graphical models, and node wise predictability. These methods were recently introduced in the Gaussian graphical model literature, including Williams (2019) <doi:10.31234/osf.io/x8dpr>, Williams and Mulder (2019) <doi:10.31234/osf.io/ypxd8>, Williams, Rast, Pericchi, and Mulder (2019) <doi:10.31234/osf.io/yt386>.

Maintained by Philippe Rast. Last updated 3 months ago.

bayes-factorsbayesian-hypothesis-testinggaussian-graphical-modelsopenblascppopenmp

3.6 match 55 stars 9.64 score 102 scripts 1 dependents

ebmtnprof

rties:Modeling Interpersonal Dynamics

The name of this package grew out of our research on temporal interpersonal emotion systems (TIES), hence 'rties'. It provides tools for using a set of models to investigate temporal processes in bivariate (e.g., dyadic) systems. The general approach is to model, one dyad at a time, the dynamics of a variable that is assessed repeatedly from both partners, extract the parameter estimates for each dyad, and then use those parameter estimates as input to a latent profile analysis to extract groups of dyads with qualitatively distinct dynamics. Finally, the profile memberships can be used to either predict, or be predicted by, another variable of interest. Currently, 2 models are supported: 1) inertia-coordination, and 2) a coupled-oscillator. Extended documentation is provided in vignettes. Theoretical background can be found in Butler (2011) <doi:10.1177/1088868311411164> and Butler & Barnard (2019) <doi:10.1097/PSY.0000000000000703>.

Maintained by Emily Butler. Last updated 3 years ago.

3.8 match 11 stars 3.94 score 16 scripts

donaldrwilliams

GGMnonreg:Non-Regularized Gaussian Graphical Models

Estimate non-regularized Gaussian graphical models, Ising models, and mixed graphical models. The current methods consist of multiple regression, a non-parametric bootstrap <doi:10.1080/00273171.2019.1575716>, and Fisher z transformed partial correlations <doi:10.1111/bmsp.12173>. Parameter uncertainty, predictability, and network replicability <doi:10.31234/osf.io/fb4sa> are also implemented.

Maintained by Donald Williams. Last updated 3 years ago.

3.6 match 6 stars 3.48 score 4 scripts

kjetil1001

SenSrivastava:Datasets from Sen & Srivastava

Collection of datasets from Sen & Srivastava: "Regression Analysis, Theory, Methods and Applications", Springer. Sources for individual data files are more fully documented in the book.

Maintained by Kjetil B Halvorsen. Last updated 1 years ago.

3.5 match 1.76 score 57 scripts

programgirl

PopulateR:Create Data Frames for the Micro-Simulation of Human Populations

Tools for constructing detailed synthetic human populations from frequency tables. Add ages based on age groups and sex, create households, add students to education facilities, create employers, add employers to employees, and create interpersonal networks.

Maintained by Michelle Gosse. Last updated 1 months ago.

0.5 match 1 stars 3.88 score

dscolby

spacejamr:Simulate Spatial Bernoulli Networks

Social network analysis is becoming commonplace in many social science disciplines, but access to useful network data, especially among marginalized populations, still remains a formidable challenge. This package mitigates that problem by providing tools to simulate spatial Bernoulli networks as proposed in Carter T. Butts (2002, ISBN:978-0-493-72676-2), "Spatial models of large-scale interpersonal networks." Using this package, network analysts can simulate a spatial point process or sequence with a given number of nodes inside a geographical boundary and estimate the probability of a tie formation between all node pairs. When simulating a network, an analyst can choose between five spatial interaction functions. The package also enables quick comparison of summary statistics for simulated networks and provides simple to use plotting methods for its classes that return plots which can be further refined with the 'ggplot2' package.

Maintained by Darren Colby. Last updated 3 years ago.

0.5 match 1 stars 2.70 score 8 scripts

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