Modeling Interpersonal Dynamics
Takes individual cross-sectional data from dyads and turns it into act...
Takes individual repeated measures data from dyads and turns it into a...
Produces auto-correlation plots of the observed state variable for lag...
Provides the equation for a coupled oscillator model for the different...
Plots the bivariate state variable's clo model-predicted temporal traj...
Produces histograms of the residuals from the oscillator model for eac...
Provides the equation for an un-coupled oscillator model for the diffe...
Produces cross-correlation plots of the observed state variable for la...
Reformat a user-provided dataframe in a generic form appropriate for *...
Produces plots for sysVarIn when sysVar is dyadic.
Estimates first and second derivatives of an oberved state variable
Histograms for all numeric variables in a dataframe.
Produces plots for sysVarIn when sysVar is categorical and there are 2...
Produces plots for sysVarIn when sysVar is continuous and there are 2 ...
Produces plots for sysVarIn when sysVar is categorical and there are 3...
Produces plots for sysVarIn when sysVar is continuous and there are 3 ...
Produces plots for sysVarIn when sysVar is categorical and there are 4...
Produces plots for sysVarIn when sysVar is continuous and there are 4 ...
Estimates either an uncoupled or coupled oscillator model for each dya...
Compares model fit for the uncoupled and coupled oscillator for each d...
Produces plots of either an uncoupled or coupled oscillator model-pred...
Estimates versions of the inertia-coordination model for each dyad.
Compares model fit for the inertia-only, coordination-only and full in...
Produces plots of the inertia-coordination model-predicted trajectorie...
Plots the bivariate state variables' model-predicted temporal trajecto...
Produces histograms of the residuals from the inertia-coordination mod...
Provides information to help decide how many profiles to use for subse...
Calculates cross-correlations for a given variable and returns a dataf...
Create a distinguishing variable (called "dist") for non-distinguishab...
Combines profile membership data from the latent profile analysis with...
A helper function for makeCrossCorr
Plots of de-trended observed variable over time, with dyads separated ...
Plots of observed variable over time by dyad.
Remove data for specified dyads from a dataframe
Provides results for predicting couples' latent profile membership fro...
Produces plots for interpreting the results from sysVarIn.
Produces results from sysVarIn.
Provides results for predicting the system variable from the latent pr...
Produces plots for interpreting the results from sysVarIn.
Produces results from sysVarOut.
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>.