Changelog
Source:NEWS.md
Version 0.4.0
CRAN release: 2024-02-15
New Features
cpr
’sprogress
argument has been extended to control if a progress bar is used for just the cpr steps, or if a more detailed progress for of the influence weight calculations is reported.influence_of_iknots
gains parallel execution viapbapply
(#17)plot.cpr_cp
gains the argumentcomparitive
which, when set toFALSE
and only onecpr_cp
is passed in for plotting, the graphic will appear more like theplot.cpr_bs
results. Whencomparitive = TRUE
or more than onecpr_cp
is present, the behavior from v0.3.0 is retained.cp.formula
gains themethods.args
argument to pass arguments to the regression method instead of relying on...
.d_order_statistic
andp_order_statistic
were added. These functions allow you to get the density of distribution function for the jth order statistic from a sample of size n from a distribution with defined density and distribution functions within R.sign_changes
will count the number of sign changes of the first or second derivative of a spline function.get_spline
returns standard errors and derivatives (#60)
User Visible Changes
-
loglikelihood
is not exported in the namespace -
summary.cpr_cp
now calculates the “wiggle” of the function by default, that is, changes the default fromwiggle = FALSE
towiggle = TRUE
-
cp
andcn
both have the defaultkeep_fit
argument set to TRUE. This change was made to simplify the prediction methods. -
print.cpr_bt
returns the object invisibly, it used to return astr(x)
. -
print.cpr_cn
returns the object invisibly -
print.cpr_cnr
returns the object invisibly -
print.cpr_cpr
returns the object invisibly
Version 0.3.0
CRAN release: 2023-11-29
Other Changes
- Depends on Rcpp >= 0.12.11 (actually moved to >= 1.0.11) to handle registering native routines.
- Moves rgl from
Imports
toSuggests
(re #36) - Refactoring base code to eliminate the use of dplyr, tidyr, tibble, etc. Focus on base R methods to reduce install dependencies and improve long term stability of the package.
- Require R > 3.5.0
- Stop using testthat for testing
- Remove use of the tidyr, dplyr
- Improve documentation
- Minor bug fixes
- Replace use of now deprecated
ggplot2::aes_string
Version 0.2.2
New Features
-
get_spline
is an S3 method for getting adata.frame
of interpolated values of a spline given acpr_cp
object. Later development will add methods forcpr_cn
objects. -
predict.cpr_cp
andpredict.cpr_cn
methods added -
matrix_rank
added -
update_bsplines
andupdate_btensor
methods added (#27)
Version 0.2.1
Documentation improvements.
New Features
-
influence_of
andplot.cpr_influence_of
provide a clean interface for users to explore the influence of a set of knots on a spline function. (#19) -
color
(TRUE
/FALSE
) option added toplot.cpr_bs
. -
plot.cpr_cn
lets the user plot 2D surfaces for tensor product surfaces. The plots are for the whole surface if the input is a 2D tensor product, and is a 2D slice evaluated at a given value for other margins for 3+ dimensional tensor products. -
is.
a collection ofis.cpr_cp
,is.cpr_bs
, … functions added. - The dataset
spdg
has been added to the package.
Version 0.2.0
This version has a fairly polished set of tools for b-splines, cpr, and cnr. This version seems to be in a good place for use in the three major papers
- Methods 1: uni-variable functions,
- Methods 2: multi-variable functions, and
- Software paper.
Continued development should be focused on bug fixes and minor enhancements.
New Features
- Option to save fits in
cnr
(#8) - Option to define the number of polynomial coefficients to use in
cnr
(#10) - x-axis tick label options for plotting b-splines (#12)
- added
show_xi
tocpr:::plot.cp
and usingggplot2::geom_rug
to show the location of the knots for each of the control polygons plotted. -
summary
forcpr_cn
andcpr_cnr
objects added. -
plot
method forcpr_cnr
objects. -
margin
option incnr
allows the user to specify which marginals CNR will be applied to. - Using
sec.axis
option fromggplot2_2.2.0
for the plotting of the knot sequence and numeric values inplot.cpr_bs
(#18)
Bug Fixes
-
from
andto
arguments forplot.cpr_cpr
fixed (#14) - correct construction of missing
iknots
argument inbtensor
-
keep
is correctly handled in thecnr
call. -
show_xi
correctly handled in theplot.cpr_cp
call.
Version 0.1.1
Version 0.1.0
First version of univariable cpr methods ready for deployment
Big picture
cpr::cp
and cpr::cpr
have been used for the simulations which are aimed to be part of the first manuscript. Modifications might be needed, but hopefully the univariable methods are stable.
A lot of changes in the implementation and API have occurred from the 0.0.x series. The aim for version 0.2.0 will be to have a very similar API for cpr::cn
and cpr::cnr
as provided for the cpr::cp
and cpr::cpr
calls.
version 0.0.3
Version 0.0.3 is the version of the package used to run the analysis and simulations presented in the paper submitted to the 28th International Biometrics Conference, Western North American Region (WNAR) of the Internal Biometric Society, Student paper competition. The conference will be held 10 - 16 July 2016 in Victoria, British Columbia, Canada.
Bug Fixes
Corrected the attributes calls within
cpr
after adjusting the attributes being set on acpr_cp
.plot.cpr_bs
correctly displays the indices for the knot sequence.
End User Visible changes:
- The knot insertion matrix W is accessible to the end user in a new way. Names of functions in
boehem.cpp
are cleaner. -
plot.cpr_cpr
allows user to select either control polygons or sums of squared residuals to be plotted.
version 0.0.2
new features
- Added the function
tensor
for building tensor products ofcpr::bsplines
. - Added the function
influence_weights
to get the influence weights for each internal knot on each marginal of a tensor product. -
is.cpr_bs
added. - S3 methods for
cp
version 0.0.1.9003
First usable version with the method based on the ‘importance weight’ of internal knots based on reversing the methods presented by Boehm (1980). Development of metrics and methods for parsing out the preferable models.
Version 0.0.1.9003 was the first stable version for fitting the exact data model.