curves 0.4.0
New features
- Added
interactions(), which ranks numeric predictor
pairs by the count-weighted magnitude of their centred second-order ALE
surfaces.
- Added
top_n to bivariate() for
method = "ale" so the plotting interface can rank all
eligible pairs with interactions() and display only the
strongest interaction surfaces.
curves 0.3.1
Fixes
bivariate(method = "ale") now masks grid cells that
contain no observations as NA in the returned surface and
renders them in the fill scale’s na.value (default light
grey) rather than colouring them with a value extrapolated from
neighbouring cells. This prevents the interaction surface from
displaying confident estimates over regions of feature space that the
data does not support, which previously misled readers when predictors
were correlated.
bivariate(method = "ale") now defaults to
n = 10 (the second-order surface uses an n x n
cell grid; n = 40 left most cells empty for typical data)
and to rug = TRUE so the data density is always
visible.
ale_surface_limits() is robust to surfaces that are
entirely NA.
- Updated docs/
Rd to describe the half-cell centring
convention used for the 2D ALE accumulation and the empty-cell masking
behaviour.
- Enabled categorical ALE in
multimodel() and
mapcurve(), matching the univariate ALE support and using a
shared level order across ensemble members when
multimodel() averages unordered factor effects.
- Added
extrapolate = FALSE to bivariate()
so unsupported ALE grid cells remain masked by default but can be shown
on request using the interpolated values already used internally for
accumulation.
curves 0.3.0
New features
- Extended
univariate() with
method = "profile", "pdp", "ice",
and "ice+pdp" so single-profile, partial dependence, and
ICE plots share one entry point.
- Added
method = "ale" to univariate() for
accumulated local effects curves on numeric predictors.
- Changed
univariate(method = "ale") to warn and skip
factor predictors instead of failing when numeric predictors are also
available.
- Split
univariate() and mapcurve() sampling
controls so n sets numeric grid resolution while
background_n sets the number of randomly sampled background
rows used for PDP/ICE.
- Added
interval to univariate() and
mapcurve() so method = "pdp" can draw central
quantile ribbons for numeric predictors.
- Added adaptive raster sampling for
univariate() so
PDP/ICE methods can draw more background predictor combinations when
predict_data comes from a SpatRaster.
- Added
bivariate() for bivariate response surfaces with
static heatmap and contour views.
- Extended
bivariate() with method = "pdp"
and method = "ale", plus optional marginal rugs for numeric
predictor pairs in static plots.
- Added optional interactive 3D response surfaces for numeric
predictor pairs when
plotly is installed.
- Added support for selecting predictor pairs by column name or column
index.
- Added support for list-valued
fun in
multimodel() so mixed model types can use model-specific
prediction wrappers before averaging curves.
Changes
- Renamed the bivariate plotting mode argument from
type
to plot_type so model-specific type arguments
can still be passed through ... to
predict().
- Changed
bivariate(plot_type = "heatmap") to use a
viridis fill scale by default and to stop drawing contour overlays on
heatmaps.
- Updated the random forest species distribution vignette to use a
smaller predictor set and to include
univariate() examples
for profile, PDP, and ICE + PDP plots.
- Stopped drawing connecting lines for unordered factor predictors in
univariate(), so categorical panels no longer imply numeric
intervals.