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exclude option to subset_draws(),
which can be used to exclude the matched selection.are_log_weights option to
pareto_smooth(), which is necessary for correct Pareto
smoothing computation if the input vector consists of log weights.pareto_smooth option to
weight_draws(), to Pareto smooth weights before adding to a
draws object.pareto_khat(), pareto_khat_threshold(),
pareto_min_ss(),
pareto_convergence_rate())thin_draws() now automatically thins draws based on ESS
by default, and non-integer thinning is possible.rvars can now be done with the
base matrix multiplication operator (%*%) instead of
%**% in R >= 4.3.variables(), variables<-(),
set_variables(), and nvariables() now support
a with_indices argument, which determines whether variable
names are retrieved/set with ("x[1]", "x[2]"
…) or without ("x") indices (#208).extract_variable_array() function to extract
variables with indices into arrays of iterations x chains x any
remaining dimensions (#340).factor variables
(draws_df, draws_list, and
draws_rvars), extract_variable() and
extract_variable_matrix() can now return
factors.rhat_nested
(#256)rvars using
rvars (#282):
x[i] or x[i] <- y where i
is a scalar logical rvar slices (or updates) x
by its draws. Thus, if y <- x[i], then y is
the same shape as x but with sum(i)
draws.x[[i]] or x[[i]] <- y where
i is a scalar numeric rvar slices (or updates)
x by selecting the ith element within each
corresponding draw. Thus, if y <- x[[i]], then
y is an rvar of length 1.rvar_ifelse(), which is a variant of
ifelse() that accepts (and returns) rvars
(#282).rvars has been made faster.rfun() works with primitive functions (#290) and
dots arguments (#291).vctrs::vec_proxy_equal(),
vctrs::vec_proxy_compare(), and
vctrs::vec_proxy_order().cbind(<rvar>),
rbind(<rvar>), and chol(<rvar>)
for R 4.4 (#304).bind_draws(<draws_rvars>) regenerates
draw ids when binding along chains or draws; this also fixes a bug in
split_chains(<draws_rvars>) (#300).tibble::num() formatting to output from
summarise_draws() until print() is called so
that summary output can be easily converted to a vanilla data frame
(#275).rvar_factor() and rvar_ordered()
subtypes of rvar() that work analogously to
factor() and ordered() (#149). See the new
section on rvar_factors in
vignette("rvar").draws_df(), draws_list(), and
draws_rvars() formats now support discrete variables stored
as factors / ordereds (or
rvar_factors / rvar_ordereds). If converted to
formats that do not support discrete variables with named levels
(draws_matrix() and draws_array()),
factor-like variables are converted to numerics.match() and %in% generic and added
support for rvars to both functions.modal_category(), entropy(), and
dissent() functions for summarizing discrete draws.bind_draws() (#253).summarise_draws output via
tibble::num.print.rvar() and format.rvar() now default
to a smaller number of significant digits in more cases, including when
printing in data frames. This is controlled by the new
"posterior.digits" option (see
help("posterior-package")).vec_proxy.rvar() and
vec_restore.rvar(), improving performance of
rvars in tibbles (and elsewhere
vctrs is used).as_draws_rvars() preserves dimensions of
length-1 arrays (#265).rvar,
vctrs, dplyr, and ggplot2 (#267,
#269).for_each_draw(x, expr), which executes
expr once for each draw of x, exposing
variables in x as arrays of the shape implied by the
indices in their names (#224).subset_draws(), thin_draws(),
and resample_draws() for rvars (#225).weights to be optional in
resample_draws() (#225).drop() for
rvars.draws_list objects. (#229,
#250)diag() for
rvars (#246).as_draws_rvars(),
including nested use of [, like x[y[1],2]
(#243).rvars with ndraws() > 1
(#242).rvars can be cast to draws
formats (#242).rvars with more than 1 dimension
as scalars when casting to other formats (#248).mcse_sd function to not make a normality
assumption. (#232)draws_list objects.NULL in
mutate_variables. (#222)rvar and
distributional::dist_sample (#109)bind_draws.draws_df when
binding more than two objects thanks to Jouni Helske (#204)pillar::glimpse() when used on a data
frame containing rvars (#210)"draws" and "draws_df" classes from
draws_df objects if meta data columns are removed by a
dplyr operation (#202)print.draws_df() on objects with
unrepaired draws (#217)variance() works properly with
summarise_draws() (#219)matrixStats to speed up convergence functions
(#190) and rvar summaries (#200)as_draws_rvars() works on lists of lists
(#192)rvar_rng
(#195)subset_draws() respects input variable
order, thanks to Karl Dunkle Werner and Alexey Stukalov (#188)ess_tail. (#198)rvars being
unnecessarily slow (#179)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.
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