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Road map, andTutorials (let me know what you'd like, or perhaps a book?)

umx is a package designed to makestructural 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

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 attbates.github.io.

umx stands for "user" OpenMx functions. It provides over 100 functions, but most importantly:

1. umxRAM that makes path-based SEM in R straightforward, withumxSummary andplot for table and graphical display of your models. It can also interpret basic lavaan if you get a script in that language.
2. A suite of twin modelling functions, such asumxACE.

These are supported by many low-level functions automating activities such as parameter labels, start values etc., as well as helping with data-wrangling, journal-ready presentation (tryumxAPA() among other tasks.

Some highlights include:

1. Building Path Models
   • umxRAM()# Take umxPaths + datadata = run and return a model, along with aplot andumxSummary
   • umxPath()# write paths with human-readable language likevar = ,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 withplot() andumxSummary!
   • 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