Christian Fong <christianfong@stanford.edu>, Marc Ratkovic <ratkovic@princeton.edu>, Kosuke Imai <kimai@princeton.edu>
CBPS is an R package that implements the covariate balancing propensity score proposed by Imai and Ratkovic (2014; JRSSB). The propensity score is estimated such that it maximizes the resulting covariate balance as well as the prediction of treatment assignment. The method, therefore, avoids an iteration between model fitting and balance checking.  The package also implements several extensions of the CBPS beyond the cross-sectional, binary treatment setting.  The current version implements the CBPS for longitudinal settings so that it can be used in conjunction with marginal structural models (Imai and Ratkovic, 2013a), treatments with three- and four-valued treatment variables, continuous-valued treatments (Fong, Imai, and Ratkovic), and the situation with multiple distinct binary treatments administered simultaneously. In the future it will be extended to other settings including the generalization of experimental and instrumental variable estimates.

Fits Cubic Bezier Spline Functions to Intertemporal and Risky
Choice Data
person("Sangil", "Lee", email = "sangillee3rd@gmail.com", role = c("aut", "cre"))
While Cubic Bezier Splines (CBS) are heavily used in the graphics software industry, it can also be used as a flexible function
approximation tool given the right constraints. 'CBSr' provides a method to calculate the y value from a x value given
an appropriately constrained CBS curve. It then uses this method to approximate latent utility functions in intertemporal choice
and risky choice data. Lee, Glaze, Bradlow, Kable, (2019) <doi:10.31234/osf.io/2ugwr>.

Uses monotonically constrained Cubic Bezier Splines (CBS) to approximate latent utility functions in intertemporal choice and risky choice data.
PsyArXiv preprint: Lee, Glaze, Bradlow, Kable, (2019) <doi:10.31234/osf.io/2ugwr>.

Uses monotonically constrained Cubic Bezier Splines (CBS) to approximate latent utility functions in intertemporal choice and risky choice data. For more information, see Lee, Glaze, Bradlow, and Kable <doi:10.1007/s11336-020-09723-4>.