Obtain least-squares means for linear, generalized linear,
and mixed models. Compute contrasts or linear functions of
least-squares means, and comparisons of slopes.
Plots and compact letter displays. Least-squares means were proposed in
Harvey, W (1960) "Least-squares analysis of data with unequal subclass numbers",
Tech Report ARS-20-8, USDA National Agricultural Library, and discussed
further in Searle, Speed, and Milliken (1980) "Population marginal means
in the linear model: An alternative to least squares means",
The American Statistician 34(4), 216-221 <doi:10.1080/00031305.1980.10483031>.
NOTE: lsmeans now relies primarily on code in the 'emmeans' package.
'lsmeans' will be archived in the near future.

Nonparametric Estimation and Inference Procedures using
Partitioning-Based Least Squares Regression
Matias D. Cattaneo, Max H. Farrell, Yingjie Feng
Tools for statistical analysis using partitioning-based least squares regression as described in Cattaneo, Farrell and Feng (2018) <arXiv:1804.04916>. lsprobust() for nonparametric point estimation of regression functions and derivatives thereof, and for robust bias-corrected (pointwise and uniform) inference procedures. lspkselect() for data-driven procedure for selecting 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.
ggplot2, pracma, mgcv, combinat, matrixStats, MASS

ggplot2, pracma, mgcv, combinat, matrixStats, MASS, dplyr

Tools for statistical analysis using partitioning-based least squares regression as described in Cattaneo, Farrell and Feng (2019, <arXiv:1804.04916>): 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.