ctsem news:

Known bugs / limitations:

27/9/2017
#### 2.5.0 ####
Fixes: 
- stanWplot function for trace plots while sampling with stan was not working on non windows platforms.
- ctStan summary reports population standard deviations more accurately -- improved delta approach.
- various minor plotting improvements

Additions / Changes:
- ctStanFit now handles correlation matrices differently -- little substantive impact.
- ctKalman can now be used to plot individual trajectories from ctFit objects and ctStanFit objects (ctStanKalman function no longer exists).
- ctStanFit now handles missing data on covariate effects -- time dependent predictors are set to zero, time independent predictors are imputed with a normal(0,10) prior (can adjust via the $tipredsimputedprior subobject of the ctStanModel).
- ctStanFit default population standard deviation prior now changed to a regularised independence Jeffreys -- previous truncated normal approach still possible because...
- ctStanFit now accepts custom specifications for the population standard deviation -- see the $rawhypersd , $rawhypersdlowerbound, and $hypersdtransform subobjects of the ctStanModel object. 
- ctStan: Plotting covariate effects via ctStanTIpredeffects function now easier to use and more versatile -- can plot effects on discrete time matrices, for instance.
- ctFit and ctMultigroupFit data argument changed to 'dat' instead of 'datawide', and now dataform="long" argument can be used to use long format data (as per ctStan) directly. 
- additional parameter matrices shown for summary of ctStanFit objects.
- ctStanParMatrices function to compute continuous time matrices for a given model and vector of free population means.


16/5/2017
#### 2.4.0 ####
Fixes:
- Time dependent predictors generated errors with the frequentist Kalman filter form since 2.2.0
- With stationary set to NULL (not the default but offered in help file) for ctFit, 
t0 matrices were mistakenly set to stationary.
- Duplicated parameter names now allowed in a ctStanFit model.

Features:
- ctGenerateFromFit generates data based on a model fitted with ctFit.
- ctPostPredict generates distributions from data based on a model fitted with ctFit
and plots this against the original data.


6/4/2017
#### 2.3.1 ####
Fixes:
- summary: Standard errors were not reported in some cases
- ctStanFit: 2.3.0 hierarchical correlation changes were applied too broadly
- ctFit: discreteTime switch no longer gives errors when traits included
- ctFit: transformedParams=FALSE argument no longer throwing errors.
- ctStanKalman: correct handling of missing data for plotting.

3/3/2017
#### 2.3.0 ####
Fixes:
- TRAITVAR in frequentist ctsem was incorrectly accounting for differing time 
  intervals since v2.0.0. TRAITVAR is now (again) reported as total between subjects
  variance.
- Default quantiles on ctStanDiscretePars adjusted to 95%.
- Hierarchical correlation probabilities adjusted in ctStanFit for more consistent
  behaviour with high dimensional processes.

Changes:
- Default to unstandardised cross effects plots.

1/2/2017
#### 2.2.0 ####
Changes:
- Time dependent predictors now have instantaneous effect in both frequentist and 
  Bayesian approaches, and the documentation is updated to reflect this.
  Previously, no TDpreds affecting first time point in frequentist.
  Accordingly, wide data structure is changed, with an extra column 
  per predictor and predictors now sorted by time point as for indicators. 
  See vignette for example. 
- Default to 0 covariance between time dependent predictors and initial (T0) 
  latents / traits / time independent predictors. Specify matrix as 'free' 
  in ctModel to estimate instead.
- Default carefulFit = TRUE for multiple groups frequentist models (ctMultigroupFit)
- Improve optimization approach for ctStanFit - but still not reliable for random effects.

Fixes:
- Multiple time dependent predictors with multiple processes resulted in inaccurate
  estimates for TDPREDEFFECT in frequentist approach of previous versions.
- Prevent ctGenerate from auto-filling matrices to 0 variance.
- Correct oscillating example for change in tolerance in OpenMx.


6/1/2017
#### 2.1.1 ####
Improvements:
- improved fitting of frequentist models with ctFit and ctRefineTo, due to
  changes to carefulFit penalisation and refining approach.

Changes:
- Removed package 'PSM' from suggests field and vignette as requested by CRAN

Fixes:
- rstan 2.14 caused problems with data import for ctStanFit
- eliminated spurious warnings for ctStanFit


20/12/2016
#### 2.1.0 ####
Features:
- Empirical Bayes, experimental but can now optimize with hierarchical model 
  (when using the Kalman filter, as per defaults)
- Easy extraction and plotting of time independent predictor (covariate) effects,
  see ctStanTIpredEffects for example.
- Added stationary argument to ctStanFit - much more efficient than setting 
  priors on stationarity.

Bugs fixed:
- incorrect number of cores spawned for parallel sessions.
- optimize and variational bayes switches for ctStanFit did not work.
- ctKalman would break if only 1 row of data passed in.


18/11/2016
#### 2.0.0 ####
Features:
- Hierarchical Bayesian modeling using Stan, see ctStanFit function and 
  the vignette at https://cran.r-project.org/package=ctsem/vignettes/hierarchical.pdf

Changes:
- Defaults change: Fix CINT to 0 and free MANIFESTMEANS
- Reintroduce variable effect of TRAITVAR at T0 (more flexible but more 
    fitting problems - try MANIFESTTRAITVAR instead if problematic, or 
    use step-wise fitting approach, automated with ctRefineTo)


#### ctsem 1.1.6 ####
Features added:
- now with a change log!
- ctCompareExpectation plots expected means and covariances against model implied.
- remove log transform of drift matrix diagonal, positive drift diagonals again possible.
- ctRefineTo allows easy step wise fitting from simple to complex - faster and more robust fitting in many cases.
- ctPlot is a new function that allows more customization of plots.
- ctModel now allows time varying means to be specified.

Bugs fixed:
- corrected handling of Cholesky inputs for ctGenerate
