Error(s) in re-building vignettes:
--- re-building ‘hierarchicalmanual.rnw’ using knitr
System dimensions inferred from LAMBDA
n.TDpred inferred  inferred from TDpredNames
n.TIpred inferred  inferred from TIpredNames
Custom par cint2 set as: param = cint2; transform = 10 * param; indvarying = TRUE; sdscale = 1; cint2 tipred effects from: TI1
System dimensions inferred from manifestNames and latentNames
n.TDpred inferred  inferred from TDpredNames
n.TIpred inferred  inferred from TIpredNames
LAMBDA vector spec input rowwise into 2 * 2 matrix:
MANIFESTMEANS specified via single value -- filling 2 * 1 matrix:
CINT vector spec input rowwise into 2 * 1 matrix:
Custom par cint2 set as: param = cint2; transform = 10 * param; indvarying = TRUE; sdscale = 1; cint2 tipred effects from: TI1
Free T0VAR parameters as well as indvarying T0MEANS -- fixing T0VAR pars to diag matrix of 1e-3
Free T0VAR parameters as well as indvarying T0MEANS -- fixing T0VAR pars to diag matrix of 1e-3
Maximum a posteriori estimation requested
NA's in TIpreds - imputing 1  NA's to allow optimization -- TIpred effect estimates may be overly confident.
NA's in TDpreds! Replaced by zeroes, consider appropriateness...
Using 2/8 logical CPU cores
starting worker pid=87474 on localhost:11156 at 08:13:20.119
starting worker pid=87488 on localhost:11156 at 08:13:20.432
Doing 1st pass with priors on reduced data set
Loading required package: Rcpp
Loading required package: Rcpp
ctsem also changes in time, for manual run ctDocs(), for blog see https://cdriver.netlify.app/, for citation info run citation('ctsem'), for original OpenMx functionality install.packages('ctsemOMX')
ctsem also changes in time, for manual run ctDocs(), for blog see https://cdriver.netlify.app/, for citation info run citation('ctsem'), for original OpenMx functionality install.packages('ctsemOMX')
Optimizing...
Including TI predictors...
Estimating Hessian
Computing posterior with 1000 samples
Computing temporal regression coefficients for 100 samples, may take a moment...
Fitting model to empty dataset...
Maximum a posteriori estimation requested
Using 1/8 logical CPU cores
Optimizing...
Estimating Hessian
Computing posterior with 10000 samples
Computing state estimates..
Computing quantities for 1 samples...
Getting 100 samples by 20 subjects for 2000 total samples
Calculating time independent predictor effects...
Free T0VAR parameters as well as indvarying T0MEANS -- fixing T0VAR pars to diag matrix of 1e-3
Free T0VAR parameters as well as indvarying T0MEANS -- fixing T0VAR pars to diag matrix of 1e-3
Free T0VAR parameters as well as indvarying T0MEANS -- fixing T0VAR pars to diag matrix of 1e-3
Free T0VAR parameters as well as indvarying T0MEANS -- fixing T0VAR pars to diag matrix of 1e-3
Custom par ma1 set as: param = ma1; transform = log(1+(exp(param))); indvarying = FALSE; sdscale = 1; 
Custom par a21 set as: param = a21; transform = -log(1+exp(param)); indvarying = FALSE; sdscale = 1; 
Custom par m1 set as: param = m1; transform = param*10+44; indvarying = TRUE; sdscale = 1; 
Bayesian estimation via Stan's NUTS sampler requested
Individual variation not possible as only 1 subject! indvarying set to FALSE on all parameters
Free T0VAR parameters fixed to diagonal matrix of 1 as only 1 subject - consider appropriateness!
Setting DRIFT diagonals of 0 to approximate 0 of -0.000001
Sampling...

Execution halted