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ctsem News
12/8/2024
3.10.1
- Fix bug introduced in 3.10.0 where certain combinations of Gaussian
and binary variables cause convergence difficulties and invalid
results.
- Revert unconstrained correlation change introduced in 3.10.0, it was
more difficult to fit in some cases.
- Detect duplicated T0MEANS parameters and propose alternative
approach.
- Add ctPredictTIP function for examining and plotting differences due
to time independent predictors.
10/05/2024
3.10.0
- Fix bug when computing Jacobian of certain nonlinear models.
- Modify unconstrained correlation approach for better optimization /
uncertainty quantification and clearer interpretation.
- Add ctACF function for plotting approximate continuous time auto and
cross correlations.
- Bug fixes to ctKalman plots, were occasionally confused re subject
ID.
- Include experimental / imperfect approach to ordinal data.
30/10/2023
3.9.1
- Fix bug in ctKalman - was dropping certain subjects resulting in no
plots / output.
- Fix fatal (i.e, erroring out) bug in certain nonlinear parameter
specifications.
- Allow direct references to time dependent predictors in nonlinear
specifications - now measurement model can easily depend on time varying
covariates.
- Update array syntax internally to rstan 2.26+ approach – completely
this time…
14/9/2023
3.9.0
- Add progress reports for stochastic optimizer and Hessian.
- Add small noise to improve sampling performance when
inits='optimize'
.
- Include ctACF and ctACFresiduafunction for approximate continuous
time auto-correlations.
- Update array syntax internally to rstan 2.26+ approach.
- Improved stochastic optimizer.
20/8/2023
3.8.1
- Correct bug in nonlinear formulations when the same state is
referenced for multiple nonlinearities.
- Correct unnecessary memory usage when computing Hessian with
multiple cores.
- Improve stochastic subsampling first pass optimizer.
- Simplify discrete time model computations internally.
- Performance gains and reduced memory usage via usage of matrix
exponential subsets and automatic computation of dynamic error
indices.
20/6/2023
3.7.6
- Deprecate
nopriors
argument to ctStanFit
,
allow priors
argument
- Allow
inits='optimize'
argument to
ctStanFit
, to speed up sampling approach
- Allow integer values for
removeObs
argument to
ctKalman
, for N step ahead predictions
- Allow
sameInitialTimes
argument to
ctStanFit
, to generate empty observations at earliest
observation time, ensuring comparability of times at T0MEANS
24/3/2023
3.7.6
- Fix compile error for some higher dimensional non-linear models
- Fix NaN gradient error for certain non-linear measurement error
models
- Use Rstantools for compile specification to ensure future
compatibility
- Allow standardized error output from
ctKalman
1/7/2022
3.7.0
- Fix some plotting features, speed up random effects a little
9/3/2022
3.6.0
- Fix: Random effects standard deviations were mis-estimated for
time-dependent predictor effects and diffusion parameters when fitting
with optimization
6/12/2021
3.5.5
- Some edge case optimizer problems resolved
- Bug fix in discrete time plots when
observational=TRUE
.
Correlations were unnecessarily squared previously
22/7/2021
3.5.4
- Improved automatic imputation of time-independent predictors, fixed
bug where too many imputed values were set to zero. Care still
recommended if relying on automatic imputation though!
16/6/2021
3.5.3
- Fixed bug in output of
$rawpopcorr
introduced in 3.4.3.
Correlations displayed incorrectly, other parameters unaffected
- Modified correlation approach to ensure monotonicity in high
dims
31/5/2021
3.5.0
- Added
ctFitMultiModel
function to simplify processing
of multiple models
- Added
ctChisqTest
function for simplified likelihood
ratio tests between models
- Added subsampling optimization for first pass, faster for larger
models/data
21/4/2021
3.4.3
- Changed optimization scheme, first BFGS, then stochastic gradient
descent
- Fixed
ctStanTIpredEffects
function, much faster
- Altered correlation matrix approach - better optimization/sampling
behavior, priors can differ by index though
10/2/2021
3.4.2
ctStanDiscretePars
temporal dependence plots work for
discrete time also
- Fix: In certain circumstances with covariate effects on duplicated
parameters, the effects may not have been completely applied in the last
few releases
- Other small bug fixes and efficiency improvements
4/12/2020
3.4
ctLOO
function for leave one out/k-fold
cross-validation
ctCheckFit
function dramatically improved for visual
model diagnostics
- Fixes to a range of edge cases when specifying more complex
nonlinearities
ctStanDiscretePars
has improved plotting options
ctStanFitUpdate
function can be used to attempt to
update a saved ctStanFit
object to the current version of
ctsem
ctSaturatedCov
function for estimating a form of
saturated model as a reference. Still a bit developmental.
- General robustness / efficiency improvements.
10/7/2020
3.3.8
- Stationarity,subject specific parameter output, and linear system
estimation removed (uses nonlinear in all cases, a bit slower) to try
satisfy CRAN compile time checks.
20/6/2020
3.3.2
- ctLOO function to compute leave k out entropy estimates for model
comparison / validation.
- Optimizer parallelisation for single subject models
- ctStanFitUpdate function to use saved fit objects created in earlier
versions of ctsem.
- Multiple core memory usage reductions.
26/4/2020
###3.2.1 - Minor updates to suit rstan 2.2.3 - Higher dim Hessian
really really fixed…
18/4/2020
3.2.0
- Change to defaults of ctStanFit – optimization without priors is new
default.
- Optimization with binary variables much improved – specify using:
mymodel$manifesttype <- c(1,0,1) for 3 manifest variables, 1st and
last binary and second continuous.
- Improved latex output for models and fits using ctModelLatex.
- Hessian estimation in higher dimensions fixed, again.
- Experimental automatic covariate (tipred) detection – set{
mymodel$TIpredAuto <- 1L } to try it.
- Minor plotting / other fixes
10/2/2020
3.1.1
- Reverted to non sequential measurement update introduced in
3.1.0
- Further hessian estimation fixes
- Stochastic optimizer further improved
21/01/2020
3.1.0
- Fixed Hessian estimation sensitivity introduced last release.
- ctStanKalman has option to return subject specific estimates.
- Various performance improvements – stochastic optimizer very
effective with many parameters.
- Fix rounding, off by one issue when using timestep argument for
nonlinear dynamics.
10/12/2019
3.0.9
- ctStanGenerate function – generate from a ctstanmodel and prior
distribution.
- Parallel optimization improvements – memory usage halved, more cores
nearly always useful.
- Missing time independent predictors single imputed when
optimizing.
- ctModelHigherOrder function to easily add higher order structure to
specified model. E.g. slow changing trends / oscillations.
- various minor output / efficiency / estimation robustness
improvements.
30/10/2019
3.0.8
- ctStanFit bug fix: MANIFESTVAR wrongly reported as the sqrt
- ctFit bug fix: Std errors of covariances were too wide when
transformedParams=TRUE
- TI predictor effects can be specified as the 5th element of
parameter string in ctModel – “drift11 | -exp(param) | FALSE | 1 | age,
gender”
- Nonlinear models: Parameter names in the additional PARS matrix, and
latent variables, can be referenced directly in parameters –
“-exp(cognition * drift11)” instead of “-exp(state[2] * PARS[1,1]”. PARS
matrix must still be specified.
- Switched plotting to ggplot2, many changes / improvements.
11/9/2019
3.0.4
- Removed some spurious warnings generated by last release.
- Improved ctKalman function for ctStanFit results (timestep now
works).
- Parallelise optimization over subjects with ctStanFit
(cores=xx).
- Bug fix: time varying diffusion generated errors.
- Use stochastic optimizer to check for improvements by default
- Simplify ctModel specification – vectors interpreted as rowwise
matrices, automatic dimension detection.
20/8/2019
3.0.1
- fixed a few minor / error generating bugs introduced in previous
release related to random effects optimization.
28/7/2019
3.0.0
- parameter transformations can be specified naturally without
recompilation
- analytic Jacobian’s used for extended Kalman filter where
possible
- improved optimizer performance
- generally improved performance, particularly for nonlinear
systems.
14/5/2019
2.9.5
- corrected optimization of random effects using ctStanFit
- ctModelLatex function to display within subject model equation
- custom calculations allowed in ctModel for linear and nonlinear
approaches
- Nonlinearity possible for discrete time now also
- Use stochastic optimizer by default for ctStanFit – more robust
12/4/2019
2.9.0
- Improved optimizer using stochastic gradient descent.
- fixed bug introduced re finding start values when binary variables
are used.
- custom calculations can be specified and estimated using both linear
and nonlinear dynamics approach.
- ctStanFit is no longer available for win32 systems.
- ctStanKalman function extracts system state over time.
6/11/2018
2.7.3
- Updated for rstan 2.18.1 compatibility
- Non-linear dynamics now handled using mixture of extended and
unscented filters for improved speed.
- Priors for hierarchical variance modified so prior for total
variance has consistent shape regardless of dimension.
- Optimization / importance sampling works well for many cases, see
arguments using ?ctStanFit.
- ctStanPostPredict produces a range of posterior predictive
plots.
- Various small non-critical bug fixes / updates. (see github for
details)
25/6/2018
2.6.5
- Fixed bug in ctStanFit introduced in previous release leading to
errors in handling of missing data.
- ctStanTIpredMarginal function for plotting marginal relationships
between predictors and parameters.
1/6/2018
2.6.0
- Removed need for compilation of standard models.
- Unscented Kalman filter for ctStanFit:
- Non-linear / time-varying / state dependent specifications now
possible.
- Optimization followed by importance sampling can be used instead of
sampling via Stan.
- Most plotting functions still not working correctly for such
models.
- ctCheckFit function for plotting covariance of data generated from
posterior against original.
- Time independent predictors can now be used independent of random
effects.
- Fix bug in summary preventing display when binary variables were
used in fit.
- Allow data sets to contain both binary and continuous
variables.
- More robust data import, character string id’s and jumbled order of
rows now manageable.
- stanWplot no longer requires shiny to be explicitly loaded.
- Fix bug in ctKalman plotting function preventing interpolation.
- Fix bug in additional summary matrices introduced in 2.5.0 – some
were transposed.
- Summary no longer returns errors when partial stationarity is
set.
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
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