umx is a structural equation modeling package designed to make SEM easier, from building, to modifying and reporting.

It includes high-level functions for complex models such as multi-group twin models, as well as for graphical model output.

Grab it from CRAN with

install.packages("umx")
library(umx)
?umx

Most functions have extensive and practical examples (even figures for the twin models): so USE THE HELP :-).

See what is on offer with '?umx'. There are online tutorials at tbates.github.io.

umx stands for "user" OpenMx functions. It provides over 100 functions from high-level umxRAM and umxPath functions that make Structural Equation Modeling in R straightforward, to low-level functions to automate activities such as labelling, setting start values etc.,

Some highlights include:

1. Building Path Models
   • umxRAM() # mxModel with smart data = parameter, no need to specify type = "RAM", + auto-discovery of manifests and latents from the paths you write
   • umxPath() # mxPath on steroids: one-word settings to set var , mean cov, fixedAt 'v.m.' = and more. Great time-saver!
2. Reporting output
   • umxSummary(model) # A model fit designed for journal reporting (Χ², p, CFI, TLI, & RMSEA). Optionally show the path loadings
   • plot(model, std=T, digits = 3, file = "name") # Graphical, editable output of model in your browser!
3. Modify models
   • umxModify() *# Modify and run a model. You can add objects, drop or add paths, including by regular-expression label matching), re-name the model, re-run, and even return the comparison. All in 1 line *
   • parameters(m1, "below", .1, pattern="_to_")) # A powerful assistant to get labels from a model (e.g. all 'to' params, below .1 in value)
4. Twin modeling!
   • umxACE() # Twin ACE modeling with aplomb paths are labeled! Works with plot() and umxSummary!
   • umxCP(), umxIP(), umxGxE(), umxCP() …
   • 
5. Easy-to-remember options
   • umx_set_cores()
   • umx_set_optimizer()
6. Many more miscellaneous helpers e.g.
   • umx_time(model) # Report the time taken by a model in a compact programable format
   • umxHcor(data, use = "pairwise.complete.obs") # Compute appropriate pair-wise correlations for mixed data types.
   • Dozens more (?umx to see them all). Check out the "family links" in any help file also!

Feel free to use, and submit code and requests via Github. Tell your friends! Publish more good science :-)

For thrill-seekers and collaborators only: the bleeding-edge development version is here:

install.packages("devtools")
library("devtools")
install_github("tbates/umx")
library("umx")
?umx