Updating svg files in the vignette for wider compatibility.
hbamr 2.4.5
Minor revisions
Fixing typos and minor bugs.
hbamr 2.4.4
Revisions to existing
functions
The hbam() function now stores the rationalization
parameter gamma by default if the model being fit is
HBAM_R_MINI.
The hbam_cv() function now has new default sampler
settings to facilitate faster sampling.
hbamr 2.4.2
Revisions to existing
functions
The hbam() function now has new default sampler
settings to facilitate faster sampling.
Revisions to existing models
The HBAM_R_MINI model now allows analyzing data with an even number
of answering categories.
hbamr 2.4.1
Revisions to existing
models
All models except HBAM_R_MINI now allow analyzing data with an even
number of answering categories.
hbamr 2.4.0
Refactored code
All models have been collected in a single Stan file to reduce the
footprint of the package. It now requires significantly less disk
space.
Discontinued function
The show_code() function has been removed, as there is
no longer easily readable code to show for any particular model.
hbamr 2.3.2
Revisions to existing
models
The following models now generate posterior predictions if the user
runs hbam() with the argument
extra_pars = "Y_pred": HBAM, HBAM_MULTI, HBAM_NF,
HBAM_MULTI_NF, BAM.
hbamr 2.3.1
Improvements of existing
functions
The prep_data() function now prints a summary of the
input and output data to the console. It also throws an error if the
selection criteria for inclusion in the analysis are too strict to
retain any respondents.
The appearance of the plot from plot_stimuli() has been
slightly improved.
Revisions to existing
models
The priors on theta and rho have been made
narrower to make the models more robust to situations with extremely
scarce data.
hbamr 2.3.0
Revisions to existing
models
All FBAM-type models have been revised to allow for heteroskedastic
errors.
The FBAM_MINI model has been renamed
FBAM to reflect this change.
hbamr 2.2.1
Revisions to existing
models
The prior on rho (which appears in all heteroskedastic
models) has been made narrower to reduce the risk of divergent
transitions when there are few observations per stimuli (which could be
the case for e.g. expert surveys).
The default priors on mu_beta and mu_alpha
in MULTI-type models and the priors on sigma_beta and
sigma_alpha in HBAM-type models have also been made
narrower to reduce the risk of sampling issues.
hbamr 2.2.0
Improvements of existing
functions
The get_est() function now takes the logical argument
format_orig, which if TRUE makes the function
return posterior summaries for individual-level parameters in a format
that matches the rows in the original dataset.
The hbam() and fbam() functions now store
the input data within the returned objects, which allows simplifying the
interfaces for other functions like get_est() and
plot_over_self(). As a result, the
plot_over_self() function no longer requires a data
argument.
The prep_data(), hbam(), and
fbam() functions now allow users to not supply
self-placements. In this case, no meaningful respondent positions will
be estimated, but all other parameters are unaffected.
The prep_data() function now allows the
group_id argument to take various forms, such as factor or
character. It also allows missing values in the group_id
vector and will drop respondents who do not have a valid
group_id. Missing values would previously generate an
uninformative error message.
The models now accept even numbers of answering categories, but
prep_data() will throw a warning.
The prep_data() function now identifies the left and
right poles for the BAM model as the stimuli with the most non-NA
observations on each side of the center. This can be advantageous when
analyzing datasets where some stimuli have a much higher number of valid
observations than others.
hbamr 2.1.2
Revisions to existing
models
The behavior of FBAM models now depends on whether they are fit via
MCMC or optimization. When fit via MCMC, the models simulate errors in
respondents’ self-placements and generate draws for the latent discrete
flipping parameters, kappa, using a Bernoulli function – just like other
models. When fit via optimization, there is no simulation of
self-placement errors and lambda is rounded to yield a MAP estimate of
kappa.
hbamr 2.1.1
Improved cross-validation
function
The hbam_cv() function no longer uses
parallel::mclapply() for parallel computation as the latter
relies on forking, which is not available on Windows.
hbam_cv() has been revised to work with the
future package, where the user decides the computational
strategy and options are available for parallel computation on all
systems.
The return value of hbam_cv() has been changed to
comply with the standards of the loo package. The function
now returns a list with classes kfold and loo.
This allows the user to compare estimated ELPDs and obtain standard
errors for their differences via loo::loo_compare().
hbamr 2.1.0
Revisions to existing
models
All models in the package now simulate errors in respondents’
self-placements. In the original HBAM model, respondents’ latent
positions were treated as parameters, which had several consequences: It
yielded a realistic level of uncertainty in the estimated positions, but
also led to slower sampling. Furthermore, the hierarchical prior on the
latent respondent positions shrunk them toward zero, which was
undesirable in most applications as it affected the distances between
respondents and stimuli. Finally, the original approach led to a more
nuanced posterior distribution where combinations of individual-level
parameter values that would lead to implausible values for the
respondent positions were weighted down. However, the model’s log-normal
prior on the stretch parameters is by itself sufficient to yield
meaningful respondent positions estimates, so there is no significant
cost to not treating the latent respondent positions as parameters. The
current versions of the model instead simulate errors in the
self-placements to yield the same level of uncertainty as the original
model, while sampling considerably faster.
Discontinued model
HBAM_MAX has been removed as it offered little
extra after the other models were revised to simulate errors in
respondents’ self-placements.
hbamr 2.0.1
New function
show_code() shows Stan code for any model in the
package.
New models
HBAM_MULTI is a version that models differences
between groups defined by the user. It requires a vector identifying the
groups to be supplied as the argument group_id. The model
gives each group separate hyperparameters for the locations of the prior
distributions for the shift and stretch parameters. Rather than
shrinking the estimates toward the mode for the whole dataset, this
model shrinks the estimates toward the mode for the group. The vectors
of hyperparameters are called mu_alpha and
mu_beta and are constructed to have means of 0. The scales
of the priors on these hyperparameters can be set by the user via the
arguments sigma_mu_alpha and sigma_mu_beta.
One potential use for this model is to supply self-placements as
group_id, and thus give each self-placement group its own
prior distribution for the shift and stretch parameters.
HBAM_MULTI_NF is a version of the HBAM_MULTI model
that does not allow for scale flipping.
FBAM_MULTI is a version of the FBAM_MINI model that
shares the group-modeling features of the HBAM_MULTI model. It allows
the user to set the scales of the priors for the shift and stretch
parameters via the arguments sigma_alpha and
sigma_beta, and set the scales of the priors on
mu_alpha and mu_beta via the arguments
sigma_mu_alpha and sigma_mu_beta.
FBAM_MULTI_NF is a version of the FBAM_MULTI model
that does not allow for scale flipping.
Renamed models
The original HBAM model has been renamed
HBAM_MAX.
The HBAM_NE model has been renamed HBAM as it makes
a better default model.
HBAM_0 has been renamed HBAM_NF. To be consistent
with the other models and offer faster sampling, the model no longer
models the latent respondent positions as parameters.
Revisions to existing
models
FBAM_MINI (as well as the new FBAM models) now
allows for user-defined prior distributions.
All models (except HBAM_MAX) have been revised to not treat the
respondent positions as parameters. This results in considerably faster
sampling.
All HBAM models that allow for scale flipping have been given a
logit-normal prior on the mixing proportions, lambda (i.e. the
expectations of the latent discrete flipping parameters, kappa). This
replaces the original beta prior, which could trigger divergent
transitions.
Discontinued models
HBAM_2 has been replaced by the more general
HBAM_MULTI model.
HBAM_HM has been removed as it offered little extra
and the number of models should be kept somewhat limited.
HBAM_R has been removed as users would typically be
better off with the simpler HBAM_R_MINI.