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4 changes: 2 additions & 2 deletions DESCRIPTION
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@@ -1,6 +1,6 @@
Package: orbital
Title: Predict with 'tidymodels' Workflows in Databases
Version: 0.4.0.9000
Version: 0.4.1.9000
Authors@R: c(
person("Emil", "Hvitfeldt", , "emil.hvitfeldt@posit.co", role = c("aut", "cre")),
person("Posit Software, PBC", role = c("cph", "fnd"),
Expand Down Expand Up @@ -48,7 +48,7 @@ Suggests:
testthat (>= 3.0.0),
themis,
tibble,
tidypredict (>= 0.5.1),
tidypredict (>= 1.0.1),
workflows
VignetteBuilder:
knitr
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2 changes: 2 additions & 0 deletions NEWS.md
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@@ -1,5 +1,7 @@
# orbital (development version)

# orbital 0.4.1

* Make work with new versions of xgboost. (#119)

# orbital 0.4.0
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3 changes: 0 additions & 3 deletions README.md
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Expand Up @@ -39,8 +39,6 @@ Given a fitted workflow

``` r
library(tidymodels)
#> Warning: package 'ggplot2' was built under R version 4.5.2
#> Warning: package 'parsnip' was built under R version 4.5.2

rec_spec <- recipe(mpg ~ ., data = mtcars) |>
step_normalize(all_numeric_predictors())
Expand Down Expand Up @@ -120,7 +118,6 @@ you can also predict in most SQL databases
``` r
library(DBI)
library(RSQLite)
#> Warning: package 'RSQLite' was built under R version 4.5.2

con <- dbConnect(SQLite(), path = ":memory:")
db_mtcars <- copy_to(con, mtcars)
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