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singleRcapture 0.2.1.2
- Bugfix for interaction terms in formula not being considered
- Small changes in summary for marginal count distributions
- Small fixes for standard errors in predicted means
- Code coverage raised to nearly 90%
- The
logLik.singleRStaticCountData
method now has a
type
argument that, if set to "function"
makes
the function return the minus lok-likelihood function (by default) or if
deriv
argument is set to 1
or 2
respectively either a gradient or hessian of lok-likelihood
function.
singleRcapture 0.2.1.1
- Bugfix for tests failing with
noLongDouble
singleRcapture 0.2.1
- Fixed bugs in
IRLS
fitting when providing
weights
argument when calling
estimatePopsize
- The
weightsAsCounts
option in controlModel
now works properly, dfbeta
and dfpopsize
decrease weight of selected row in a model matrix instead of deleting it
when this is set to TRUE
simulate
method now works for both family object (like
ztpoisson()
) and for objects returned by
estimatePopsize
- Introduced
singleRStaticCountData
sub class for
singleRclass
and made estimatePopsize
a method
so that a new package singleRcaptureExtra
(under
development) can make all necessary calculations for pop size estimation
when providing object fitted by countreg::zerotrunc
or
VGAM::vglm
/VGAM::vgam
- Some bugfixes for multicore bootstrap
- Code was re-factored to make further development/maintenance for the
package much easier
- Update will be uploaded to
CRAN
semiparametric
bootstrap now has a much faster sampling
algorithm (that does the same job)
Unit tests: * Reduced computational burden of unit tests * Multicore
tests will only be performed after
TEST_SINGLERCAPTURE_MULTICORE_DEVELOPER
is set to
"true"
via Sys.setenv
and
_R_CHECK_LIMIT_CORES_
to false
singleRcapture 0.2.0.1
- Added
offset
argument to
estimatePopsize
- Added options for parallel computing in
bootstrap
and
in dfbeta
- Added deviance for all negative binomial based models. (NOTE: They
are very slow for now and I believe it may change after I verify one
theoretical results that will lead to significant speed increase for
these computations)
- Overhaul of starting points (new methods and added linear predictors
start in
IRLS
)
- Code for weights in
IRLS
fitting was speed up
- Minor bugfixes
singleRcapture 0.2.0
The package is now at CRAN
- features and improvements:
- Added final
Hurdleztnegbin
model
- Vastly improved
redoPopSize
which now handles bootstrap
on a fitted model non standard covariance matrixes newdata
argument user supplied coef
etc.
- Added
predict.singleR
method which offers standard
error for both link
, response
as well as
mean
predictions
- No unexpected warnings should occur now in main function when using
the package correctly
- All control arguments are now verified before being passed
- Fitting is now more reliable
- Added information about
stats::optim
error codes
- Added warnings for functions computing deviance
- bugfixes:
- fixed bugs occurring when using mathematical functions as part of
formulas i.e. when setting formula to something like:
y ~ log(x) + I(x ^ t) + I(t ^ 2)
singleRcapture 0.1.4
- features
- Added
ztoinegbin
, oiztnegbin
and
ztHurdlenegbin
models
- Added an optional arguments to all family-functions to specify a
link function for distribution parameters
- Updated and standardized documentation
- Added more warnings
- Added some more methods for
singleR
class in some
commonly used glm
functions, in particular
texreg::screenreg
should work well now
- changes
- Changed some default arguments
- Added option to save logs from
IRLS
fitting
- bugfixes
- Fixed some issues with intercept only models
- Fixed some slight miscalculations in information matrixes for one
inflated models making fitting them much more reliable
- github repository
- More and better
Rcmd
tests
singleRcapture 0.1.3.2 – NTTS
- features:
- Added function that implements population size estimates for
stratas
- More warnings in fitting
- More options in control functions
- Corrected/implemented deviance residuals for more models
- changes:
- Now the whole package uses
cammelCase
- Performance upgrades
- Corrected some miss calculated moments
- Change exported data so that factors are actually factors not just
characters
- Removed unused dependency
- github repository
- Added automated
R-cmd
check
singleRcapture 0.1.3.1
- features:
- Basically all of documentation was redone and now features most of
important theory on SSCR methods and some information on (v)glms
- Added checks on positivity of working weights matrixes to stabilise
"IRLS"
algorithm
- Added most of sandwich capabilities to the package, in particular:
- S3 method for
vcovHC
was implemented
vcovCL
should work on singleR
class
objects should work with "HC0"
and "HC1"
type
argument values
- Basic version of function
redoPopEstimation
for
updating the population size estimation after post-hoc procedures was
implemented
popSizeEst
function for extracting population size
estimation results was implemented
- Minor improvements to memory usage were made and computation was
speed up a little
- Changed names of mle and robust fitting methods to optim and IRLS
respectively
- Some bugfixes
- More warnings messages in
estimate_popsize.fit
singleRcapture 0.1.3
- features:
- Multiple new models
IRLS
generalised for distributions with multiple
parameters
- bugfixes
- QOL improvements
- extended bootstrap and most other methods for new models
singleRcapture 0.1.2
- features:
- control parameters for model
- control parameters for regression in bootstrap sampling
- leave one out diagnostics for popsize and regression parameters
(
dfbetas
were corrected)
- fixes for Goodness of fit tests in zero one truncated models
- computational improvements in
IRLS
- other small bugfixes
singleRcapture 0.1.1
- bug fixes and some of the promised features for 0.2.0 in particular
- More tiny tests
- Some fixes for marginal frequencies
- Deviance implemented
- dfbetas and levarage matrix
- Parametric bootstraps work correctly for the most part there is just
some polishing left to do
singleRcapture 0.1.0
- first version of the package released
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.