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causaloptim 1.0.0
Major release with lots of changes:
- Balke’s legacy C++ code is no longer supported. Vertex enumeration
is now always done with rcdd
- New interface to specify a causal model without an effect. Also
derivation of observable constraints and checking of linearity
- Various bug fixes and improvements
causaloptim 0.10.0
- experimental functionality for automated linearity checking
causaloptim 0.9.8
- complete refactor of shiny app (@gjo11)
- add tests for shiny ui and server components (@gjo11)
- update documentation and clean up exports (@gjo11)
causaloptim 0.9.7
- More fixes to snprintf and LEN_LABEL
- Fixed warnings from Balke’s old cpp code
- update bibentry
causaloptim 0.9.3
- Minor bug fixes in Balke’s old code (sprintf -> snprintf)
- removed methods vignette and linked to published article instead (it
is open access, please email me if you can’t access it)
- bugfix to ensure parameters get removed if constraints make them
impossible
causaloptim 0.9.2
bug fixes
- Fixed parsing issue in latex_bounds
causaloptim 0.9.2
bug fixes
- Fixed c++ compiler warning about length of buffer in
Tableau.cpp
causaloptim 0.9.1
More big new features!
- categorical variables are now handled
- Update shiny UI to enable specification of categorical
variables
- Update draft of methods paper in vignette
bug fixes
- Fixed propagation of intervention sets downstream when there are
crossed effects
causaloptim 0.8.2
Big new features!
- Major update to the vertex enumeration algorithm (thanks to @gjo11). There is a new
function called
optimize_effect_2
which uses the
rcdd
package to enumerate the vertices. This is
significantly faster than Balke’s C++ code. This function is used by
default in the shiny app. There are some minor differences in how the
bounds are displayed, and note that the display of the vertices within
MIN and MAX may differ from optimize_effect
. The bounds are
equivalent (just in a different order) in all of the test cases that we
have looked at.
- New vignette explaining how the optimization was implemented (also
thanks to @gjo11)
Small features
- interpret_bounds returns function that will now give a warning if
bounds are invalid.
- Pull steps of analyze_graph out into separate modules.
Bugfixes
- Make sure that qs are unique in large problems
causaloptim 0.7.1
Bugfixes
- Propagation of intervention set when not all paths are defined
- Checking for violations of condition 6 in effect
New features
- Additional information returned from analyze_graph
- Better printing for analyze_graph objects
causaloptim 0.6.5
Bugfixes
- Fixed mismatched new/delete/delete[] in C++ code
causaloptim 0.6.4
Bugfixes
- Parse effect fixed for joint outcome probabilities.
- Added examples
- Update description
causaloptim 0.6.3
Bugfixes
- Refactor algorithm 1 so that it works correctly for unobserved
variables
- Fix probability printing in shiny app for unobserved variables
causaloptim 0.6.2
Bugfixes
- Fixed repeated qs bug that affects graphs with multiple variables on
left side
- Added citation file
causaloptim 0.6.1
Minor updates
- Allow user interrupt in long-running c++ loops
- Increase maximum number of vertices
- Added progress indicator to shiny app
- Update dependencies
causaloptim 0.6.0
New features
- Allow for observed variables in the causal effect. E.g., for the
treatment effect among the treated
p{Y(X = 1) = 1; X = 1} - p{Y(X = 0) = 1; X = 1}
causaloptim 0.5.3
Bugfixes
- Fixed warnings on CRAN check
- Added travis ci
- Lowercase min and max in latex
- Additional documentation on shiny app
causaloptim 0.5.2
Bugfixes
- Better reset functionality in shiny app
causaloptim 0.5.1
New features
- button to print latex code in shiny app
- Checking for intervention set children to be on rightside
- Other error checks and bugfixes
causaloptim 0.5.0
New features
causaloptim 0.4.1
Bug fixes
- Bugfixes in parsing bounds
- CRAN checks
New features
- Error checking in parse effect and parse constraints in shiny
app
causaloptim 0.3.0
Rebuilt interface, text based now.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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