Nothing
weight_nodes = TRUE
, using the new nudge
argument to
plot.nma_data()
(#15).as_tibble()
or as.data.frame()
on an nma_summary
object (such as relative effects or predictions) now
includes columns for the corresponding treatment (.trt
) or contrast (.trta
and .trtb
), and a .category
column may be included for multinomial models.
Previously these details were only present as part of the parameter
columnlog_student_t()
, which can be used
for positive-valued parameters (e.g. heterogeneity variance).set_agd_contrast()
now produces an informative error message
when the covariance matrix implied by the se
column is not positive definite.
Previously this was only checked by Stan after calling the nma()
function..trtclass
special in regression
formulas, now main effects of .trtclass
are always removed since these are
collinear with .trt
. This allows expansion of interactions with *
to work
properly, e.g. ~variable*.trtclass
, whereas previously this resulted in an
over-parametrised model.get_nodesplits()
when studies have multiple arms of the same
treatment.print.nma_data()
now prints the repeated arms when studies have
multiple arms of the same treatment.NA
in
multi()
) (PR #11)consistency = "nodesplit"
in nma()
. Comparisons to split can be chosen
using the nodesplit
argument, by default all possibly inconsistent comparisons
are chosen using get_nodesplits()
. Node-splitting results can be summarised
with summary.nma_nodesplit()
and plotted with plot.nodesplit_summary()
.add_integration()
for ML-NMR models is now adjusted to the underlying Gaussian
copula, so that the output correlations of the integration points better match
the requested input correlations. A new argument cor_adjust
controls this
behaviour, with options "spearman"
, "pearson"
, or "none"
. Although these
correlations typically have little impact on the results, for strict
reproducibility the old behaviour from version 0.3.0 and below is available with
cor_adjust = "legacy"
.relative_effects()
and predict.stan_nma()
respectively, using the new
argument predictive_distribution = TRUE
.posterior_ranks()
or posterior_rank_probs()
, when
argument sucra = TRUE
.trt
, study
, or trt_class
are factors, previously the order of levels was reset into natural sort order.options("contrasts")
.plot.nma_data()
no longer gives a ggplot deprecation warning (PR #6).predict.stan_nma()
with a single covariate when newdata
is a
data.frame
(PR #7).predict.stan_nma()
on a regression model with only
contrast data and no newdata
or baseline
specified now throws a descriptive
error message.baseline_type
and baseline_level
arguments to
predict.stan_nma()
, which allow baseline distributions to be specified on the
response or linear predictor scale, and at the individual or aggregate level.baseline
argument to predict.stan_nma()
can now accept a
(named) list of baseline distributions if newdata
contains multiple studies.newdata
arguments to functions like
relative_effects()
and predict.stan_nma()
now give more informative error
messages.--run-donttest
run correctly.relative_effects()
with all_contrasts = TRUE
no longer gives an error for
regression models.cor
in add_integration()
is not required when only one covariate is present.likelihood
and link
arguments in
nma()
).set_*()
functions now accept dplyr::mutate()
style semantics,
allowing inline variable transformations.multi()
for
specifying the outcomes. Accompanied by a new data set hta_psoriasis
and
vignette.flat()
.as.array.stan_nma()
is now much more efficient, meaning that
many post-estimation functions are also now much more efficient.plot.nma_dic()
is now more efficient, particularly with large
numbers of data points.plot.nma_dic()
with multiple data types has been reversed for improved clarity
(now AgD over the top of IPD).predict()
from ML-NMR / IPD
regression models are now calculated in a much more memory-efficient manner.weight_edges = TRUE
no longer produce
legends with non-integer values for the number of studies.plot.nma_dic()
no longer gives an error when attempting to specify
.width
argument when producing "dev-dev" plots.\donttest{}
instead of \dontrun{}
plot()
method for nma_data
objects.as.igraph()
, as_tbl_graph()
methods for nma_data
objects.relative_effects()
,
posterior ranks with posterior_ranks()
, and posterior rank probabilities with
posterior_rank_probs()
. These will be study-specific when a regression model
is given.predict()
method for
stan_nma
objects.plot.nma_summary()
.sample_size
argument for set_agd_*()
that:center = TRUE
) in nma()
when
a regression model is given, replacing the agd_sample_size
argument of nma()
plot()
method for nma_dic
objects produced by dic()
.link = "cloglog"
for
binomial likelihoods.prior_het_type
.plot_prior_posterior()
.pairs()
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