Depends: emmeans (>= 1.3), methods, R (>= 3.2)
Description: Obtain least-squares means for linear, generalized linear,
NOTE: lsmeans now relies primarily on code in the 'emmeans' package.
'lsmeans' will be archived in the near future.

Title: Nonparametric Estimation and Inference Procedures using
Author: Matias D. Cattaneo, Max H. Farrell, Yingjie Feng
Description: 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.
Imports: ggplot2, pracma, mgcv, combinat, matrixStats, MASS
Packaged: 2018-04-18 12:29:54 UTC; Administrator

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

Description: 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.

Description: 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.
Packaged: 2019-08-08 20:21:51 UTC; Administrator