plot_bs() builds the x-axis correctly for a basis built
with values outside of the boundary knots.Rcpp::stop() for consistent
error signaling. (Thanks to @Enchufa2, re #63)cpr()’s progress argument has been
extended to control if a progress bar is used for just the cpr steps, or
if a more detailed progress form of the influence weight calculations is
reported.
influence_of_iknots() gains parallel execution via
pbapply() (#17)
plot.cpr_cp() gains the argument
comparative which, when set to FALSE and only
one cpr_cp is passed in for plotting, the graphic will
appear more like the plot.cpr_bs() results. When
comparative = TRUE or more than one cpr_cp is
present, the behavior from v0.3.0 is retained.
cp.formula() gains the methods.args
argument to pass arguments to the regression method instead of relying
on ....
d_order_statistic() and
p_order_statistic() were added. These functions allow you
to get the density or 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)
loglikelihood() is not exported in the namespacesummary.cpr_cp() now calculates the “wiggle” of the
function by default, that is, changes the default from
wiggle = FALSE to wiggle = TRUEcp() and cn() both have the default
keep_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 a str(x).print.cpr_cn() returns the object invisiblyprint.cpr_cnr() returns the object invisiblyprint.cpr_cpr() returns the object invisiblyRefactor of c++ defining basis functions, derivatives of basis functions, b-splines structures
cp.formula() checks the formula and
requires that bsplines() is used once and is the first term
on the right hand side of the formula.
help(cpr-defunct)
for details.cpr() has examplesImports to Suggests (re
#36)ggplot2::aes_string()First public release.
plot.cpr_cn() supports rgl and
plot3D graphicsget_spline() is an S3 method for getting a
data.frame of interpolated values of a spline given a
cpr_cp object. Later development will add methods for
cpr_cn objects.predict.cpr_cp() and predict.cpr_cn()
methods addedmatrix_rank() addedupdate_bsplines() and update_btensor()
methods added (#27)Documentation improvements.
influence_of() and plot.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 to plot.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 of is.cpr_cp(),
is.cpr_bs(), … functions added.spdg has been added to the package.build_tensor() definitionThis 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
Continued development should be focused on bug fixes and minor enhancements.
cnr() (#8)cnr() (#10)show_xi to cpr:::plot.cp() and using
ggplot2::geom_rug() to show the location of the knots for
each of the control polygons plotted.summary() for cpr_cn and
cpr_cnr objects added.plot() method for cpr_cnr objects.margin option in cnr() allows the user to
specify which marginals CNR will be applied to.sec.axis option from ggplot2_2.2.0
for the plotting of the knot sequence and numeric values in
plot.cpr_bs() (#18)from and to arguments for
plot.cpr_cpr() fixed (#14)iknots argument in
btensor()keep is correctly handled in the cnr()
call.show_xi correctly handled in the
plot.cpr_cp() call.knot_expr() created to help with
plotting the knot locations in cpr:::plot.cpr_bs().plot.cpr_cp() allows the user to suppress the plotting
of the control polygon. When plotting multiple control polygons and
splines, this option will make it easier to view the spline
functions.First version of univariable cpr methods ready for deployment
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.
bsplineD()bsplines()generate_cp_data()deboor.cpp file so that the
bsplines() are accessible. The prior design only allowed
access to the basis, the current design allows access to the generic
B-splines.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.
Corrected the attributes calls within cpr() after
adjusting the attributes being set on a cpr_cp.
plot.cpr_bs() correctly displays the indices for the
knot sequence.
boehem.cpp are
cleaner.plot.cpr_cpr() allows user to select either control
polygons or sums of squared residuals to be plotted.greville_sites() removed.tensor() for building tensor
products of cpr::bsplines().influence_weights() to get the
influence weights for each internal knot on each marginal of a tensor
product.is.cpr_bs() added.cp()trimmed_quantile() handles the use_unique
option correctlycp() and
cpr()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.
This version was based on the idea that using an angle to reduce the control polygon was a good idea. Further literature review and simulations showed otherwise. This version is marked for posterity and the cpp functions are going to be useful in the following versions as well.