small fixes in docs

README.md

@@ -21,7 +21,7 @@ The model has several advantages:
 * It is rapidly fitted as a Gaussian Markov random field (GMRF) in a Generalized Linear Mixed Model (GLMM), with speed and asymptotics associated with each
 * It allows granular control over the number of parameters (and restrictions on parameters) used to structure the covariance among variables and over time,
 
-_phylosem_ is specifically intended as a minimal implementation, and uses standard packages to simplify input/output formatting:
+_dsem_ is specifically intended as a minimal implementation, and uses standard packages to simplify input/output formatting:
 
 * Input: time-series defined using class _ts_, with `NA` for missing values
 * Input: structural trade-offs specified using syntax defined by package _sem_

vignettes/dynamic_factor_analysis.Rmd

@@ -140,7 +140,7 @@ These estimated states follow the data more closely and have smaller estimated c
 
 ## Reduced-rank factor model with measurement errors
 
-Next, we can specify three factors factors while eliminating additional process error and estimating measurement errors.  This requires us to switch to `gmrf_parameterization = "projection"`, so that we can fit a rank-deficient Gaussian Markov random field:
+Next, we can specify two factors factors while eliminating additional process error and estimating measurement errors.  This requires us to switch to `gmrf_parameterization = "projection"`, so that we can fit a rank-deficient Gaussian Markov random field:
 
 ```{r, echo=TRUE, message=FALSE, fig.width=7, fig.height=7}
 # Add factors to data