Matrix kindly informed me
that a new release of that package would cause an error in
interpolate_marginal_posterior, due to an upstream bug in
splines::interpSpline and its predict method.
They also kindly suggested a simple fix, which I implemented in this
update.added an S3 interface for parameter transformations,
see make_transformation,
validate_transformation,
default_transformation, and any aghq package
function with a transformation argument. Providing
transformation to aghq and related functions
does not change the computation of the marginal likelihood/normalizing
constant, but it does mean all downstream summary methods will return
results for the transformed parameters.
added an S3 interface for computing moments of
positive functions, see make_moment_function,
validate_moment, and the updated
compute_moment.
added algorithms for moments and marginal posteriors that are now
proved to recover the same rate of convergence of the marginal
likelihood/normalizing constant. See the updated documentation and
options for the default_control() family of
functions.
sample_marginal.marginallaplace was using numeric
indexing to pull “theta” parameters, and this was not being done
correctly. Switched to using named parameters, because this feature was
previously added to the summary methods so it was easy to add
here.
Added a call to make.unique in
marginal_laplace_tmb. TMB uses non-unique
names for its par vectors when parameters are supplied to
the template as vectors rather than scalars. This was causing errors in
the named indexing.
added SIMPLIFY = FALSE to mapply in
sample_marginal to fix a problem when the output was only
length 1.
interpolation argument in
compute_quantiles to auto, from
polynomial. I guess this may be considered a bug fix.Added support for doing the multiple required Cholesky
decompositions in parallel in
sample_marginal.marginallaplace.
Switched from using chol to using
Matrix::Cholesky with perm = TRUE inside
sample_marginal.marginallaplace.
Added spline-based interpolation to
interpolate_marginal_posterior and all downstream
functions. Now the calculation of marginals doesn’t get less stable as
more quadrature points are added. Added package splines to
Imports to support this; since polynomial interpolation
almost always gives unstable answers when the number of quadrature
points is even moderate, I consider this a necessary
Import.
Added the option interpolation to
default_control() (see documentation). Default option of
auto designed to always give stable marginal posterior
interpolation regardless of the number of quadrature points.
Added an internal validate_control check to all
functions which use a control argument, which makes sure
the user inputs a control list with the correct names and value types.
This is supported by the existing control functions, and helps prevent
further cryptic downstream errors.
Added a onlynormconst option to aghq
and related functions. Simply returns the numeric value of the log
integral, avoiding all the extra stuff, at greater speed.
Added a new summary method for objects of class
marginallaplace, that includes information on the random
effects.
Preserve variable names in all summary output.
Added a requireNamespace condition to all functions
from packages listed in Suggests.
aghq::laplace_approximation() had a typo and was
returning the wrong value. This has been fixed and tests added for its
accuracy based on an example with a known answer.
Fixed an issue in the optimization where the
trustOptim package was not being checked for, and this was
throwing a cryptic error.
Changed default options for optimization in all functions to
optim(...method = 'BFGS'), in case users do not have the
trust or trustOptim packages
installed.
Added expm1 to logdiffexp() to improve
numerical stability.
Fixed optimize_theta so that control
arguments are passed correctly.
Removed several unit tests that were failing on M1 Macs. These tests were actually testing that polynomial interpolation of marginal posteriors FAILs, so apparently this isn’t failing on these new Macs, but that’s better, not worse. Will re-test and potentially add back once I have local access to this hardware.
Re-added numDeriv as an Import, since it is used in
core functionality.
Switched default optimization control arguments to use
base::optim, to facilitate removal of
trustOptim and trust as Import
dependencies.
Switched default method for numerically differentiated Hessians
to 'Richardson', for more accurate results.
Added new function marginal_laplace_tmb() which
improves compatibility with the TMB package.
Turned all the main functions for computing posterior summaries
into S3 methods with methods for objects of class
aghq.
default_control_tmb() which was missing the
ndConstruction = 'product' default argument which caused
the function to throw a cryptic error.Removed external dependencies:
matrixStats: the only function I had used was
logSumExp, which I replaced with a slower but completely
base R version from
https://stats.stackexchange.com/questions/381936/vectorised-computation-of-logsumexp
(accessed on 2021/03/27) in order to remove this dependency. This does
not have a meaningful effect on the performance of
aghq.
All ‘tidyverse’ subsidiary packages.
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