Pre-1.0.0 numbering scheme: 0.x will indicate releases, while 0.0.x will indicate PR's.
epidatasets
package. The datasets can no longer be loaded with
data(<dataset name>)
, but can be accessed with
data(<dataset name>, package = "epidatasets")
, epidatasets::<dataset name>
or, after loading the package, the name of the dataset alone (#382).growth_rate()
).
step_growth_rate()
has lost its additional_gr_args_list
argument and now
has an na_rm
argument.epiprocess
out of depends (#440). No internals have changed, but downstream
users may need to add library(epiprocess)
to existing code.step_adjust_latency
, which give several methods to adjust the forecast if the forecast_date
is after the last day of data.layer_population_scaling
default by
with other_keys
.epiprocess
.quantile_reg()
producing error when asked to output just median-level predictions.step_epi_ahead
until we have step_epi_shift
reference_date
as an argument to epi_recipe()
step_epi_ahead
and step_epi_lag
grf
were sometimes out of order.NA
values without causing unrelated errorslayer_residual_quantiles()
to always include 0.5
.recipes:::check_training_set()
to recipes:::validate_training_data()
, as it changed in recipes 1.1.0.layer_residual_quantiles()
to avoid timesuck in utils::methods()
dist_quantiles()
to be more descriptive, breaking changepivot_quantiles()
(now *_wider()
, breaking change)pivot_quantiles_wider()
for easier plottingpivot_quantiles_longer()
cdc_baseline_forecaster()
and flusight_hub_formatter()
smooth_quantile_reg()
flatline_forecaster()
min_train_window
argument removed from canned forecasterscheckmate
for input validationtarget_date
+ forecast_date
handling to match the time_type of the
epi_df. allows for annual and weekly datacheck_enough_train_data()
that will error if training data is too smallcheck_enough_train_data()
to arx_forecaster()
layer_residual_quantiles()
will now error if any of the residual quantiles
are NA*_args_list()
functions now warn if forecast_date + ahead != target_date
predictor
argument in arx_forecaster()
now defaults to the value of
the outcome
argumentarx_fcast_epi_workflow()
and arx_class_epi_workflow()
now default to
trainer = parsnip::logistic_reg()
to match their more canned versions.forecast()
method simplify generating forecastsbake.epi_recipe()
and remove epi_juice()
.compat-purrr
to use the r-lang standalone-*
version (via
{usethis}
)epi_recipe()
will now warn when given non-epi_df
datalayer_predict()
and predict.epi_workflow()
will now appropriately forward
...
args intended for predict.model_fit()
bake.epi_recipe()
will now re-infer the geo and time type in case baking the
steps has changed the appropriate valuesdist_quantiles()
dist_quantiles()
step_epi_slide
to produce generic sliding computations over an epi_df
{grf}
) as a parsnip engineepi_keys()
with epiprocess::key_colnames()
, #352arg_is_*()
, #287fit()
drops the epi_workflow
class (also error if
non-epi_df
data is given to epi_recipe()
), #363epi_df
class during baking to the extent possible, #376Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.