Tools for statistical analysis using partitioning-based least squares regression as described in Cattaneo, Farrell and Feng (2019a, <arXiv:1804.04916>) and Cattaneo, Farrell and Feng (2019b, <arXiv:1906.00202>): lsprobust() for nonparametric point estimation of regression functions and their derivatives and for robust bias-corrected (pointwise and uniform) inference; lspkselect() for data-driven selection of the IMSE-optimal number of knots; lsprobust.plot() for regression plots with robust confidence intervals and confidence bands; lsplincom() for estimation and inference for linear combinations of regression functions from different groups.

Linear Splines with Convenient Parametrisations
Linear splines with convenient parametrisations such that
(1) coefficients are slopes of consecutive segments or (2) coefficients are
slope changes at consecutive knots. Knots can be set manually or at break points
of equal-frequency or equal-width intervals covering the range of 'x'.
The implementation follows Greene (2003), chapter 7.2.5.
testthat, knitr, rmarkdown, scales, ggplot2, broom
c(person("Michal", "Bojanowski", role=c("aut", "cre"), email="michal2992@gmail.com"))

Implements the LS-PLS (least squares - partial least squares)
method described in for instance Jørgensen, K., Segtnan, V. H., Thyholt, K.,
Næs, T. (2004)  A Comparison of Methods for Analysing Regression Models with
Both Spectral and Designed Variables.
Journal of Chemometrics, 18(10), 451--464.

Implements the LS-PLS (least squares - partial least
squares) method described in for instance Jørgensen, K.,
Segtnan, V. H., Thyholt, K., Næs, T. (2004) A Comparison of
Methods for Analysing Regression Models with Both Spectral and
Designed Variables.  Journal of Chemometrics, 18(10), 451--464.

c(person("Bjørn-Helge", "Mevik", role = c("aut", "cre"),
email = "b-h@mevik.net"))
Implements the LS-PLS (least squares - partial least squares)
method described in for instance Jørgensen, K., Segtnan, V. H., Thyholt, K.,
Næs, T. (2004)  "A Comparison of Methods for Analysing Regression Models with
Both Spectral and Designed Variables"
Journal of Chemometrics, 18(10), 451--464, <doi:10.1002/cem.890>.