The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
dynamite
which can be used to tweak some aspects of the model (no checks on the compatibility with the post processing are made).cmdstanr
backend to O0
, as the O1
is not necessarily stable in all cases.full_diagnostics
to the print()
method which can be used to control the computation of the ESS and Rhat values. By default, these are now computed only for the time- and group-invariant parameters (which are also printed).print()
method now also warns about possible divergences, treedepth saturation, and low E-BMFI.predict()
code generation.dynamite()
will now retain the original column order of data
in all circumstances.mcmc_diagnostics
function so that HMC diagnostics are checked also for models run with the cmdstanr
backend.get_data()
method for dynamitefit
objects now correctly uses the previously defined priors instead of the default ones.data.table
package to 1 in examples, tests, and vignettes for CRAN.lfo()
method now uses a single chain and core to avoid a compatibility issue with CRAN.plot_nus()
for categorical responses.predict()
and fitted()
methods when newdata
contained duplicate time points within group.lfo()
in case of missing data.formula.dynamitefit()
with models defined using lags()
with a vector k
argument with more than one value.lfo()
method which resulted wrong ELPD estimates in panel data setting.lfo()
method which in case of lagged responses caused the ELPD computations to skip last time points.dynamite()
data parsing that caused substantial memory usage in some instances.formula.dynamitefit()
with models that had multinomial channels.formula.dynamitefit()
when the df
argument of splines()
was NULL
.trials()
and offset()
terms are now properly parsed when using lags()
.dynamite()
now supports parallel computation via the reduce-sum functionality of Stan.predict()
that resulted in redundant NAs produced
warnings.formula.dynamitefit()
with models that had multivariate channels.update()
method used by lfo()
.update()
method for model fit objects without a group variable.update()
method in lfo()
."tau"
and "tau_alpha"
type parameters with the as_draws()
method for categorical responses.formula.dynamitefit()
when the model contained a splines
component."dynamitefit"
objects no longer contain the data used for Stan sampling by default. This data can still be retrieved via get_data()
.gaussian_simulation_fit
that includes the model fit of the dynamite_simulation
vignette for the example with time-varying effects.latent_factor_example
and latent_factor_example_fit
have been removed to accommodate CRAN package size requirements. The code to generate these data is still available in the data_raw
directory.formula.dynamitefit()
when the model formula contained a lags
component or a lfactor
component."student"
family in obs()
."multinomial"
family in obs()
. A trials()
term is now mandatory for multinomial channels.trials()
and offset()
is now properly checked in the data.trials()
and offset()
now function correctly in predict()
when they contain response variables of the model.nobs()
for models that have multivariate channels.predict()
with models that contained multivariate channels with random effects.rstan::sampling()
and the sample()
method of the cmdstanr
Stan model via ...
in the call to dynamite
are now checked and unrecognized arguments will be ignored.get_parameter_dims()
that returns the parameter dimensions of the Stan model for "dynamitefit"
and "dynamiteformula"
objects.noncentered_lambda
from lfactor()
as this did not work as intended."nocb"
.omega
parameters, they now include also the channel name.gregexec()
internally which made it dependent on R version 4.1.0 or higher."mvgaussian"
family in obs()
. See the documentation of the dynamiteformula()
function for details on how to define multivariate channels."dynamitefit"
class. You can use the functions get_priors()
and get_parameter_names()
to see the names that are available, as before.verbose_stan
is now ignored when backend = "cmdstanr"
.stanc_options
argument for defining compiler options when using cmdstanr
can now be controlled via dynamite()
."data.table"
objects in predict()
leading to faster computation.update()
method now checks if the backend
has changed from the original model fit.update()
method now properly recompiles the model (if necessary) in cases where update()
is used for already updated "dynamitefit"
object.-Inf
prior mean if all observations at the first time point were zero.plot_deltas()
and other plotting functions now throw an error if the user tries to plot parameters of an incorrect type with them.dynamite()
now supports general group-level random effects. New random()
works analogously with varying()
inside obs()
, and the new optional random_spec()
component can be used to define whether the random effects should be correlated or not and whether to use noncentered parameterization.bayesplot
package. Instead, ggplot2
and patchwork
packages are used for the plot
method.dynamite()
function has been changed: time
now precedes group
and backend
now precedes verbose
. This change is also reflected in the get_data()
, get_priors()
, and get_code()
functions.y ~ x
and x ~ z
simultaneously is valid, but adding z ~ y
to these would result in a cycle.mcmc_diagnostics()
is now clearer.summary
argument was changed to FALSE
in as.data.frame()
and as.data.table()
methods, whereas it is now hard-coded to TRUE
in the summary()
method. The column ordering of the output of these methods was also changed so that the estimate columns are placed before the extra columns such as time
.parameters
to as.data.frame()
and similar methods as well for the plotting functions.get_parameter_types()
and get_parameter_names()
for extracting model parameter types and names respectively.data.table
package.multichannel_example
and the corresponding fit was modified: The standard deviation parameter of the Gaussian channel used in the data generation was decreased in order to make the example in the vignette more interesting.random()
component in order to reduce the size of the model fit object.plot_deltas()
no longer unnecessarily warns about missing values.get_prior()
, get_code()
, and get_data()
now support case without group
argument, as per issue #48.predict()
.cmdstanr
via argument backend
in dynamite
.rstan
.dplyr
and tidyr
to ‘Suggests’.categorical_logit()
is now used instead of categorical_logit_glm()
on older rstan
and cmdstanr
versions.random()
now also support centered parametrization.formula.dynamitefit()
so that it is now compatible with the update()
method. Also added the required "call"
object to the "dynamitefit"
object.loo()
and lfo()
methods for the dynamite models which can be used for approximate leave-one-out and leave-future-out cross validation.env
argument of data.table()
is now used to avoid possible variable name conflicts.lambda
is now xi
in order to free lambda
for factor loadings parameter as is customary in factor analysis.get_code()
applied to fitted model now correctly returns only the model code and not the stanmodel
object..draw
column of the as.data.frame()
output.predict()
and fitted()
by separating the simulated values from the predictors that are independent of the posterior draws.funs
, this can further significantly reduce memory usage when individual level predictions are not of interest.dynamite
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.
Health stats visible at Monitor.