Comments (1)
The case of covlmc
is too complex to be handled automatically. For a programming point of view, this only marginally more complex than for VLMC, but on a mathematical point of view, this is far from obvious. Indeed COVLMC are not stationary as they are driven by the external covariates. This aiming to the stationary distribution is at best misguided.
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Related Issues (20)
- Support initial="extended" in loglikelihood.covlmc HOT 1
- Improve test coverage
- Handle the empty context consistently
- Report the random seed as an attribute in simulate.*
- Switch to extended likelihood as default likelihood function HOT 1
- Release mixvlmc 0.2.0
- Implements predict.vlmc for the C++ back end
- Add options to control tune_* functions
- Use ellipsis to test varargs proper usage
- Improve draw.* usability
- Refactor SuffixTree.cpp to declare the module elsewhere
- Create a global option for the C++ back end
- Improve automatic C++ representation rebuilding
- Allow COVLMC models to be estimated on multiple time series
- Implement a C++ back end for the multiple time series estimation
- Include more likelihood function variants for model estimated on multiple time series
- Write a new vignette for multiple series support
- Create a discrete time series type as well as a type of collections of those
- Implement integration with knitr (draw.*) HOT 1
- Follow rOpenSci best practices
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