📚 Escolha o idioma · 选择语言 · Choose language
🇧🇷 Português ·
🇨🇳 中文 ·
🇬🇧 English
fastsemR is an R interface to fastsem, a compiled Nim SEM engine that supports:
- ML (full-data maximum likelihood) with analytical gradient and optional GPU acceleration
- FIML (full-information ML for missing data and definition variables)
- DWLS / WLSMV (diagonally weighted least squares for ordinal indicators)
- Multi-group models with configural or constrained parameters
- Equality constraints, parameter bounds, and derived parameters (delta-method SEs)
- Cluster-robust standard errors
- Standardised estimates (StdAll) with SEs via the delta method
The package translates umx / OpenMx RAM model objects into fastsem lavaan syntax,
fits the model, and injects estimates back into the model object so that
summary(), omxGetParameters(), and umxCompare() continue to work.
# Install from GitHub (requires devtools)
devtools::install_github("lf-araujo/fastsemR")The compiled shared library is downloaded automatically from GitHub Releases the first time the package is loaded. To force a re-download (e.g. after a fastsem update):
library(fastsemR)
fastsem_update()If you have built the library yourself from the fastsem source tree
(nimble buildRnim), point the package to your local build:
fastsem_load("~/fastsem/libfastsem_r.so")Decompose the effect of engine displacement (disp) on fuel economy
(mpg) through vehicle weight (wt), labelling paths a, b, and
c_prime so that the indirect effect ab := a * b is computed via the
delta method.
library(fastsemR)
library(umx)
df <- as.data.frame(scale(mtcars[, c("mpg", "disp", "wt")]))
m <- umxRAM("Mediation",
umxPath("disp", to = "wt", labels = "a"),
umxPath("wt", to = "mpg", labels = "b"),
umxPath("disp", to = "mpg", labels = "c_prime"),
umxPath(var = c("disp", "wt", "mpg")),
umxPath(means = c("disp", "wt", "mpg")),
data = df, autoRun = FALSE
)
m_fit <- run_fastsem(m)
summary(m_fit)fastsem -- FIML [SE: OIM]
N=32 free params=9 df=0
# Parameter Estimate SE z
─────────────────────────────────────────────────────────────
1 a (disp->wt) 0.888 0.038 23.36
2 b (wt->mpg) -0.541 0.112 -4.83
3 c_prime (disp->mpg) -0.408 0.112 -3.64
...
The indirect effect and its SE are computed automatically when a
derived_params line is added to the syntax, e.g.:
# Append to the umxPath-generated syntax before fitting
syntax <- paste(umx_to_lavaan(m), "ab := a * b", sep = "\n")
res <- fastsem_fit(syntax, df)
cat("Indirect effect:", res$estimates[res$paramNames == "ab"],
" SE:", res$se[res$paramNames == "ab"], "\n")The ACE model partitions phenotypic variance into additive genetic
(A), shared-environment (C), and unique-environment (E) components.
It is estimated as a two-group (MZ / DZ) SEM with separate A factors
per twin (A1, A2) and a per-group cross-twin genetic covariance
(c(1, 0.5) for MZ / DZ).
library(fastsemR)
# ── Simulate twin data ─────────────────────────────────────────────────────────
set.seed(42)
sim_twins <- function(n, h2 = 0.5, c2 = 0.2) {
e2 <- 1 - h2 - c2
a <- sqrt(h2); cc <- sqrt(c2); e <- sqrt(e2)
A_mz <- rnorm(n); C_mz <- rnorm(n)
A_dz1 <- rnorm(n)
A_dz2 <- 0.5 * A_dz1 + sqrt(1 - 0.5^2) * rnorm(n)
C_dz <- rnorm(n)
mz <- data.frame(
T1 = a*A_mz + cc*C_mz + e*rnorm(n),
T2 = a*A_mz + cc*C_mz + e*rnorm(n), grp = 1L
)
dz <- data.frame(
T1 = a*A_dz1 + cc*C_dz + e*rnorm(n),
T2 = a*A_dz2 + cc*C_dz + e*rnorm(n), grp = 2L
)
rbind(mz, dz)
}
all_dat <- sim_twins(500) # 500 pairs per zygosity
# ── ACE syntax ────────────────────────────────────────────────────────────────
# Separate A factors per twin with cross-twin genetic covariance c(1, 0.5).
# Setting Var(A1)=Var(A2)=1 means a²+c²+e² = 1 in both groups.
ace_syntax <- "
group: grp
A1 =~ a*T1
A2 =~ a*T2
C =~ c*T1 + c*T2
E1 =~ e*T1
E2 =~ e*T2
A1 ~~ 1*A1; A2 ~~ 1*A2
C ~~ 1*C
E1 ~~ 1*E1; E2 ~~ 1*E2
A1 ~~ c(1, 0.5)*A2 # genetic covariance: 1 for MZ, 0.5 for DZ
A1 ~~ 0*C; A2 ~~ 0*C
A1 ~~ 0*E1; A1 ~~ 0*E2
A2 ~~ 0*E1; A2 ~~ 0*E2
C ~~ 0*E1; C ~~ 0*E2
E1 ~~ 0*E2
T1 ~~ 0*T1; T2 ~~ 0*T2
T1 ~ 1; T2 ~ 1
"
res <- fastsem_fit(ace_syntax, all_dat)
print_fastsem(res)
# Labeled parameters appear in paramNames under their label
a_hat <- res$estimates[res$paramNames == "a"]
c_hat <- res$estimates[res$paramNames == "c"]
e_hat <- res$estimates[res$paramNames == "e"]
cat(sprintf("\nh²=%.3f c²=%.3f e²=%.3f (total=%.3f)\n",
a_hat^2, c_hat^2, e_hat^2, a_hat^2 + c_hat^2 + e_hat^2))h²=0.349 c²=0.293 e²=0.339 (total=0.981)
Note: ML estimates can differ from method-of-moments (2*(MZ-DZ covariance) for h²) because FIML accounts for the full variance structure — including that Var(T1) ≠ Var(T2) in finite samples — while MOM uses only covariance differences. The chi-square test (df=4, p=0.42) confirms good model fit.
| Feature | fastsemR |
|---|---|
| ML (complete data) | ✓ |
| FIML (missing data) | ✓ |
| FIML with definition variables | ✓ |
| DWLS / WLSMV (ordinal data) | ✓ |
| Multi-group models | ✓ |
| Equality constraints | ✓ |
| Parameter bounds | ✓ |
| Derived parameters + delta-method SEs | ✓ |
| Cluster-robust SEs | ✓ |
| Standardised estimates (StdAll) | ✓ |
| GPU acceleration (OpenCL) | ✓ (engine only) |
umx / OpenMx bridge (run_fastsem) |
✓ |
- Getting started with fastsemR — path models, CFA, FIML, labels, bounds, derived parameters.
- Behaviour genetics — univariate ACE twin model, bivariate Cholesky, RI-CLPM.
- State-space models — Kalman filtering, latent trajectories, longitudinal data.
See also the website articles for comparison with other SEM software, benchmarks, and the roadmap.
If you use fastsemR in your research, please cite it. A BibTeX entry is generated by:
citation("fastsemR")A peer-reviewed methods paper is in preparation; this section will be updated when the preprint is available.
Contributions are welcome. Please open an issue at https://github.com/lf-araujo/fastsemR/issues for bug reports, feature requests, or questions about the R interface. For issues specific to the underlying Nim engine (numerical results, GPU kernels, performance), file at https://github.com/lf-araujo/fastsem/issues.
Pull requests targeting main should pass R CMD check with no
errors or warnings; the GitHub Actions workflow runs the same checks
on Linux, macOS, and Windows.
fastsemR is released under the MIT License. See LICENSE for details. The compiled fastsem engine is distributed under its own license; see the fastsem repository for terms.