brokenstick 2.3.0

• Replaces what argument in get_knots() to whatknots
• Adds support for whatknots in get_omega()
• Extends capabilities of plot_trajectory() with shape and linetype options
• Adds an example to plot.brokenstick() on how to create a decent black and white figure of trajectories
• Replaces knots = 0:3 by knots = 0:2 in examples
• Updates the perfectmodel vignette

brokenstick 2.2.0

Major changes:

1. Changes the default number of knots in brokenstick() to 5. The former default produced a solution without internal knots. The new default produces a generally more informative starting model when the user does not specify knots (using knots = c(..., ...)) or the number of knots (using k = ...).
2. Replaces the strip_data argument in predict() by the a more intuitive include_data argument. By default, observed data are now included into the predictions, similar to predict.lm().
3. Turns error Argument 'newdata' is required for a light brokenstick object. of brokenstick() into a warning and returns NULL.
4. Updates the vignette Broken Stick Model for Irregular Longitudinal Data to sync with revision for JSS.

Minor changes:

• Expression predict(fit_200_light, x = "knots") now produces warning message instead of crashing
• Updates objects fit_200 and fit_200_light to use automatic boundary (2.68y) instead of 3 yrs
• Automatically sorts any user-specified values for knots in increasing order to evade problems with predict()

brokenstick 2.1.0

Incorporate changes and updates required by JSS

• Reorganises the vignettes
• Renames brokenstick-article.Rmd to manual/manual.Rmd, include high-res version on the site and take out of the package to save space
• Updates vignettes/bibliography.bib to title case
• Removes superfluous navigation from vigettes
• Defines an model.frame.brokenstick() function that adheres to conventions
• Changes return values by fitted() and residuals() to vectors
• Defines a less verbose print.brokenstick() helper
• Make all calls to library() to character argument
• Removes library(lme4) from code to evade changing the search path

brokenstick 2.0.2

• Shrinks the size of brokenstick object by removing the formula list element

brokenstick 2.0.1

• Shrinks the size of light objects by removing the sigma2j vector from the light brokenstick class

brokenstick 2.0.0

Main changes

1. Function brokenstick() in version 2.0.0 sets the Kasim-Raudenbush sampler as the default method. The former method lme4::lmer() remains available by setting method = "lmer" argument.

2. Version 2.0.0 adopts the variable names of the coda package (e.g., start, end, thin, niter, and so on) and stores the results of the Kasim-Raudenbush sampler as objects of class mcmc.

3. For method = "kr" one may now inspect the solution of the sampler by standard functions from the coda package. For method = "lmer" we can apply functions from the lme4 package for merMod objects.

4. Version 2.0.0 redefines the brokenstick class. New entries include call, formula, internal, sample, light, data, imp and mod. Removed entries are knots (renamed to internal) and draws (renamed to imp). We may omit the newdata argument for the training data. Setting light = TRUE creates a small version of the brokenstick object. Objects of class brokenstick are not backwards compatible, so one should regenerate objects of class brokenstick in order use newer features in 2.0.0.

5. Version 2.0.0 conforms to classic model fitting interface in R. Renames the new_data argument to newdata to conform to predict.lm(). Methods plot() and predict() no longer require a newdata argument. All special cases of predict() updated and explained in documentation and examples.

6. Version 2.0.0 adds methods coef(), fitted(), model.frame(), model.matrix(), print() and summary for the brokenstick object.

7. Simplifies algorithmic control. Renames control_brokenstick() to set_control() and removes a layer in the control list.

Minor changes

• Stabilises the rgamma() calls in KR-algorithm for edge cases.
• predict_brokenstick() can now work with the both (internal) training and (external) test data.
• Removes the superfluous type argument from predict.brokenstick()
• Adds a function get_omega() to extract the variance-covariance matrix of the broken stick estimates
• Adds choice "dropfirst" to get_knots()
• Improves error messages of edge cases in test-brokenstick_edge.R
• Perform stricter tests on arguments of brokenstick()
• Introduces argument warn_splines in make_basis() to suppress uninteresting warns from splines::bs()
• Removes superfluous knotnames argument in make_basis()
• Argument x in make_basis() is now a vector instead of a column vector
• Introduces new xname argument in make_basis() to set the xname

brokenstick 1.1.1

• Handles an edge case that crashed predict()

brokenstick 1.1.0

This version adds a couple of minor alterations.

• Updates cran-comments
• Adds a link to the JSS manuscript in the description field
• Removes unnecessary \dontrun{} directives
• Exports parse_formula() to remove ::: from examples
• Sanitises chunk names by removing : and _ characters
• Corrects some “first-order” mindo’s to “second-order”
• Repairs plotting glitch in oldfriends.Rmd
• Limits number of printed rows in predict.brokenstick() example

brokenstick 1.0.0

• Ready for CRAN –> Move up to version 1.0.0
• The package is now hosted on https://github.com/growthcharts/brokenstick/

brokenstick 0.78.0

• Prepare package for first CRAN submission

brokenstick 0.77.0

• Add documentation for the brokenstick class object
• Add JSS manuscript as a vignette
• Remove the prediction vignette and its dependencies
• Extend plot.brokenstick() with the ability to plot imputed trajectories
• Add the weightloss data
• Fail early when user specifies degree > 1

brokenstick 0.76.0

• Adds argument what to plot.brokenstick()
• Solves a bug that always yielded zero rows for case 3 prediction
• Solves a data combination problem in predict() when the group variable is a factor
• Add a better explanation of the boundary parameter
• Evades that model.matrix() removes rows with NA if degree = 0

brokenstick 0.75.0

• This version trims down the package in various ways
• Removes dependencies of hardhat and recipes
• Makes the brokenstick object smaller since no blueprints are stored
• Removes the recipe interface to the brokenstick() function
• Moves ggplot2 to suggests
• Copies over the install.on.demand() function from mice

brokenstick 0.72.1

• Imports recipes::recipe() to inform R package installation process

brokenstick 0.72.0

• Adds badges, resources and references to README
• Updates license
• Updates CITATION

brokenstick 0.71.0

• Removes the dependency on growthstandards
• Updates and corrects plot examples
• Tries to evade ggplot2 out-of-range/missing messages through better filtering

brokenstick 0.70.1

• Adds support for brokenstick model with degree = 0

brokenstick 0.70.0

• This version jump illustrates big and breaking changes:

1. brokenstick adopted the tidymodels philosophy, and now includes a dependency on hardhat. It is now possible to fit a model using five different interfaces. There is no need anymore the hardcode variable names in the source data.

2. This version introduces a new estimation method, the Kasim-Raudenbush sampler. The new method is more flexible and faster than lme4::lmer() when the number of knots is large.

3. This version introduces two simple correlation models that may be used to smooth out the variance-covariance matrix of the random effects.

4. The definition of the brokenstick class has changed. Objects of class brokenstick do no longer store the training data.

5. The brokenstick_export class is retired.

6. The predict() function is fully rewritten as has now a new interface. Since the brokenstick class does not store the training data anymore, the predict() function now obtains a new_data argument. Syntax that worked for brokenstick package before 0.70.0 does not work anymore and should be updated. The shape argument replaces the output argument.

7. The plot() function is rewritten, and now requires a new_data specification.

8. Retired functions: brokenstick() replaces fit_brokenstick(), predict.brokenstick() replaces predict.brokenstick_export(), get_r2() replaces get_pev()

9. Removed functions: get_data(), get_X(), export()

brokenstick 0.62.0

• This version simplifies the plotting functions
• Renders ggplot objects sharper in vignettes by svglite
• Drops the pkg argument in plot.brokenstick()
• Lessens the dependency on rbokeh
• Replaces hbgd (which is no longer developed) by growthstandards package
• Replaces smocc_50/fit_50 by smocc_200/fit_200
• Added a NEWS.md file to track changes to the package

brokenstick 0.61.0

• Added smocc_50 and fit_50 demo data
• Removed smocc.hgtwgt, smocc_hgtwgt and fit_206 datasets
• Adapt code and vignettes to reflect replacement of demo data

brokenstick 0.60.0

• Adapted source to R 4.0.0.

brokenstick 0.55

• Added new utility function get_pev() for proportion explained variance
• get_knots() gets a what argument
• Now using smarter defaults for plot()
• Simplified arguments to plot()
• Simplified vignette “Overview of main functions”

brokenstick 0.54

• Added Support for ggplot2
• Made ggplot2 plot default
• Changed default show_references flag to FALSE

brokenstick 0.53

• This is the version announced during my invited lecture at the 7th Channel Network Conference, Hasselt, Belgium.

Here is the abstract of the lecture:

Broken stick model for individual growth curves

Stef van Buuren

1. Netherlands Organization for Applied Scientific Research TNO
2. Utrecht University

The broken stick model describes a set of individual curves by a linear mixed model using second-order linear B-splines. The model can be used

• to smooth growth curves by a series of connected straight lines;
• to align irregularly observed curves to a common age grid;
• to create synthetic curves at a user-specified set of break ages;
• to estimate the time-to-time correlation matrix;
• to predict future observations.

The user specifies a set of break ages at which the straight lines connect. Each individual obtains an estimate at each break age, so the set of estimates of the individual form a smoothed version of the observed trajectory.

The main assumptions of the broken stick model are that the development between the break ages follows a straight line, and that the broken stick estimates follow a common multivariate normal distribution. In order to conform to the assumption of multivariate normality, the user may fit the broken stick model on suitably transformed data that yield the standard normal (Z-score) scale.

This lecture outlines the model and introduces the brokenstick R package.