umx

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

citation("umx")

You should cite: Timothy C. Bates, Michael C. Neale, Hermine H. Maes, (2019). umx: A library for Structural Equation and Twin Modelling in R. Twin Research and Human Genetics, 22, 27-41. DOI:10.1017/thg.2019.2

And Luis F.S. Castro-de-Araujo, Nathan A. Gillespie, Michael C. Neale, and Timothy Charles Bates (2026). umx version 4.5: Extending Twin and Path-Based SEM in R with CLPM, MR-DoC, Definition Variables, Onyx Integration, and Censored Distributions. Twin Research and Human Genetics, 1-6. DOI:10.1017/thg.2026.10056

Overview Road map, and Tutorials.

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

Install it from CRAN:

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. For users, the two most import families in umx are:

1. umxRAM. This makes path-based SEM in R straightforward, handling ordinal thresholds, start values, labels automatically. It can also interpret basic lavaan if you get a script in that language.
2. A suite of twin modelling functions, such as umxACE.

Both suites are supported by umxSummary which generates publication-ready tables, and and plot which makes graphical display of your models easy and flexible. There are many other helpers for data-wrangling twin data, working with prolific, scoring scales, printing to models (try umxAPA()) among other tasks.

Some highlights include:

1. Building Path Models
   • umxRAM() # Take umxPaths + data data = run and return a model, along with a plot and umxSummary
   • umxPath() # write paths with human-readable language like var = , mean = cov =, fixedAt=. Quickly define a variance and mean (‘v.m. =’) and more.
2. Reporting output
   • umxSummary(model) # Nice summary table, in markdown or browser. Designed for journal reporting (Χ², p, CFI, TLI, & RMSEA). Optionally show path loadings
   • plot(model, std=TRUE, digits = 3, ...) # Graphical model in your browser! or edit in programs like OmniGraffle
   • parameters(m1, "below", .1, pattern="_to_")) # A powerful assistant to get labels and values from a model (e.g. all ‘to’ params, below .1 in value)
   • residuals(m1, supp=.1) # Show residual covariances filtered for magnitude
3. Modify models
   • umxModify(model, update = ) # Modify and re-run a model. You can add objects, drop or add paths, including by wildcard label matching), re-name the model, and even return the comparison. All in 1 line
4. Twin modeling!
   • umxACE # Twin ACE modeling with aplomb paths are labeled! Works with plot() and umxSummary!
   • umxCP, umxIP, umxGxE, umxCP, umxGxEbiv, umxSexLim

   • 

5. Easy-to-remember options
   • umx_set_cores()
   • umx_set_optimizer()
6. Many more miscellaneous helpers e.g.
   • umx_time(model1, model2) reports and compares run times in a compact programmable format (also “start” and “stop” a timer)
   • umxHetcor(data, use = "pairwise.complete.obs") # Compute appropriate pair-wise correlations for mixed data types.
   • Dozens more: Check out the “family links” in ?umx and in any help file!

Code and requests welcome via Github. Tell your friends! Publish 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