powRICLPM 0.1.1
Minor improvements
- Now includes the
check_Phi() function, which users can
use to check if they have specified their Phi matrix of
lagged-effects as intended.
plot() now allows other performance measures, such as
bias, MSE, coverage, to be plotted through its y
argument.
Bug fixes
- Mistakes in the model syntax of the estimation model when imposing
stationarity constraints (using
constraints = "stationarity") have now been corrected.
powRICLPM 0.1.0
New features
powRICLPM can now save the generated data sets by
specifying a path with the save_dat argument
Minor improvements and fixes
- The
est_ME argument in powRICLPM has been
renamed estimate_ME.
- Internal model fitting using
lavaan now skips certain
checks to speed up the process.
- The
wSigma argument in powRICLPM has been
replaced with the within_cor argument. Now, only a
double denoting the correlation between the
within-components needs to specified rather than a correlation
matrix.
- By default,
powRICLPM now discards results from Monte
Carlo replications with inadmissible parameter results, unless bounded
estimation is used (bounds = TRUE).
powRICLPM 0.0.0.9004
New features
powRICLPM() can now set the reliability of the observed
variables for the generated data through the reliability
argument (i.e., include measurement error).powRICLPM() can estimate measurement errors by setting
est_ME = TRUE.powRICLPM() quantifies the uncertainty around the
simulated power through non-parametric bootstrapping.powRICLPM() now allows for various estimators
implemented in lavaan.give(from = ..., what = "...") is implemented to
extract various bits of information from a powRICLPM
object.
Minor improvements and
fixes
check_N() now takes imposed constraints into account to
create more informative error messages (@dbaranger, #1).summary.powRICLPM() now tabulates the output.
powRICLPM 0.0.0.9003
- Original GitHub release of the Beta-version of
powRICLPM on May 17th, 2022.