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enrichwith 0.3.2
Bug fixes
- fixed a typo (
dmu.deta -> d2mu.deta)
for betareg enrichment, which would cause failures with
get_bias_function() and other methods.
Other improvements,
updated, and additions
Documentation fixes.
Added MASS in Suggests to allow for using
ggplot2 in vignettes.
Deprecated enriched_glm() as it might lead to
scoping issues. Users are recommended to use the call
enrich(glm(...)) or
glm(...) |> enrich().
enrichwith 0.3.1
Bug fixes
- Added conditional use of
SuppDists.
Other improvements,
updates and additions
- Fixed typos and spelling mistakes in documentation.
- Added new tests for d/r/p/qmodel.
enrichwith 0.3
New functionality
- Added support for
wedderburn family from the
gnm R package.
Other improvements,
updates and additions
- Improved documentation.
- Improved tests and added some new.
enrichwith 0.2
Bug fixes
- Fixed bug in the computation of beta regression score contributions
from
betareg objects.
- Fixed bug with the attributes of information matrices for
generalized linear models with fixed dispersion.
- Fixed the row names in the information matrices for
glm
and betareg objects.
New functionality
get_information_function for betareg
objects returns a function that can compute observed information
matrices (by setting type = "observed").get_information_function now returns a function that
can compute the Cholesky decomposition of the information matrix (by
setting CHOL = TRUE).simulate methods (coming form
get_simulate_function) now check for correct length of
coefficient vector if this is specified.
Other improvements,
updates and additions
enrichwith 0.1
Bug fixes
New functionality
enrich.family can return 4th derivatives of the a
function and other characteristics of the exponential family.enrich method for betareg objects
(enrichment options with scores, information, bias and simulate).
Other improvements,
updates and additions
- Documentation updates.
- Added vignette for
enrich.family including a
description of the exponential family.
enrichwith 0.05
Bug fixes
- Fixed bug when passing a vector of options in the
with
argument of enrich methods.
- Fixed a bug that would return wrong results if the
bias
function was passed a named vectors.
New functionality
Other improvements,
updates and additions.
- Added d,p,q and enriched_glm in the vignette.
- Documentation updates.
enrichwith 0.04
Bug fixes
- p,q,d model: if data is missing then the model frame is used.
- Fixed error message in qmodel.
New functionality
- Added
get_dmodel_function,
get_pmodel_function, get_qmodel_function
enriched_glm can now be used to fit GLMs and get
objects that are enriched with auxiliary functions and other components
when compared to their glm counterparts.
Other improvements,
updates and additions
- Fixed typos in
?enrich.family.
- Added documentation for
get_dmodel_function.glm,
get_pmodel_function.glm,
get_qmodel_function.glm.
enrichwith 0.03
Bug fixes
- Took care of aliasing in bias calculations.
- Names of score and bias components do not get a prefix anymore.
- Fixed a bug on argument of information function (part of the
“auxiliary functions” enrichment option).
- Fixed a bug when testing the simulation methods.
New functionality
- Provide enrich capabilities for
lm objects.
- Score functions (?get_score_function) now accept
contributions = TRUE for getting score contributions.
- Provide the get_*_function convenience methods
(e.g. get_score_function, get_information_function,
get_bias_function).
- Provide the dmodel, pmodel, qmodel auxiliary functions for glm
objects, to compute d, p, q based on a supplied data frame.
Other improvements,
updates and additions
- Various codebase improvements.
- Various documentation improvements.
- New vignette for enriching
glm objects.
- Updated README file.
enrichwith 0.02
- Fixed an issue with the example in ?enrich.glm.
- Minor codebase improvements.
- Harmonised the output of the functions in the auxiliary_functions
component of enriched
glm objects.
- More detailed descriptions for the enrichment options for
glm objects.
- Included a vignette on how bias-reduction for GLMs can be
implemented using enriched
glm objects.
enrichwith 0.01
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.