Fitting and evaluation of piecewise structural equation models, complete with goodness-of-fit tests, estimates of (standardized) path coefficients, and evaluation of individual model fits (e.g., through R-squared values). Compared with traditional variance-covariance based SEM, piecewise SEM allows for fitting of models to different distributions through GLM and/or hierarchical/nested random structures through (G)LMER. Supported model classes include: lm, glm, gls, pgls, sarlm, lme, glmmPQL, lmerMod, merModLmerTest, glmerMod.


Depends:R (>= 3.5.0), nlme,
MASS, lme4License:

The primary functions in the package are psem which unites structural equations in a single model. summary.psem can be used on an object of class psem to provide various summary statistics for evaluation and interpretation.


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Shipley, Bill. Cause and correlation in biology: a user's guide to path analysis, structural equations and causal inference. Cambridge University Press, 2002.

Shipley, Bill. "Confirmatory path analysis in a generalized multilevel context." Ecology 90.2 (2009): 363-368.

Shipley, Bill. "The AIC model selection method applied to path analytic models compared using a d-separation test." Ecology 94.3 (2013): 560-564.

Grace, J.B., Johnson, D.A., Lefcheck, J.S., and Byrnes, J.E. "Standardized Coefficients in Regression and Structural Models with Binary Outcomes." Ecosphere 9(6): e02283.

Nakagawa, Shinichi, Paul CD Johnson, and Holger Schielzeth. "The coefficient of determination R2 and intra-class correlation coefficient from generalized linear mixed-effects models revisited and expanded." Journal of the Royal Society Interface 14.134 (2017): 20170213.