Univariate and Bivariate Latent Change Score Modeling
c(
person("Milan", "Wiedemann", email = "milan.wiedemann@gmail.com", role = c("aut", "cre"), comment = c(ORCID = "0000-0003-1991-282X")),
person("Anke", "Ehlers", role = c("ths"), comment = c(ORCID = "0000-0002-8742-0192")),
person("Mental Health Research UK", role = c("fnd"))
)
Helper functions to implement univariate and bivariate latent change score models in R using the 'lavaan' package.
For details about Latent Change Score Modeling (LCSM) see McArdle (2009) <doi:10.1146/annurev.psych.60.110707.163612> and Grimm, An, McArdle, Zonderman and Resnick (2012) <doi:10.1080/10705511.2012.659627>.
The package automatically generates 'lavaan' syntax for different model specifications and varying timepoints.
The 'lavaan' syntax generated by this package can be returned and further specifications can be added manually.
Longitudinal plots as well as simplified path diagrams can be created to visualise data and model specifications.
Estimated model parameters and fit statistics can be extracted as data frames.
Data for different univariate and bivariate LCSM can be simulated by specifying estimates for model parameters to explore their effects.
This package combines the strengths of other R packages like 'lavaan', 'broom', and 'semPlot' by generating 'lavaan' syntax that helps these packages work together.
Milan Wiedemann [aut, cre] (<https://orcid.org/0000-0003-1991-282X>),
Anke Ehlers [ths] (<https://orcid.org/0000-0002-8742-0192>),
Mental Health Research UK [fnd]

Local inference: local multi-collinearity tests and other tools
Local multi-collinearity diagnostics and Spatial Interaction Model tools

Local Correlation, Spatial Inequalities and other tools
Local correlation coefficients, spatial inequality statistics and other spatial statistics tools

Local Correlation Coefficients (Local Pearson and Geographically Weighted Pearson), Spatial Inequality Measures (Gini, Spatial Gini, LQ, Focal LQ), Spatial Autocorrelation (Moran's I) and other Spatial Analysis tools (Geographically Weighted Statistics, Inferential Statistics)