Title: Nonparametric Rotations for Sphere-Sphere Regression
person(c("Charles","C."), "Taylor", email = "charles@maths.leeds.ac.uk",
person("Giovanni", "Lafratta", email = "giovanni.lafratta@unich.it",
person("Stefania", "Fensore", email = "stefania.fensore@unich.it",
Description: Fits sphere-sphere regression models by estimating locally weighted rotations.
Simulation of sphere-sphere data according to non-rigid rotation models.
Provides methods for bias reduction applying iterative
procedures within a Newton-Raphson learning scheme.
Cross-validation is exploited to select smoothing parameters.
See Marco Di Marzio, Agnese Panzera & Charles C. Taylor (2018)

Description: Fits sphere-sphere regression models by estimating locally weighted
rotations. Simulation of sphere-sphere data according to non-rigid rotation
models. Provides methods for bias reduction applying iterative procedures
within a Newton-Raphson learning scheme. Cross-validation is exploited to select
smoothing parameters. See Marco Di Marzio, Agnese Panzera & Charles C. Taylor

Depends: R (>= 1.7), SparseM, tripack, akima
Description: Nonparametric and sparse quantile regression methods

Depends: R (>= 1.7), quantreg, SparseM, tripack, akima

Packaged: Wed Mar  3 08:17:29 2004; roger

Depends: R (>= 1.7), quantreg, SparseM, tripack, akima, rgl
Packaged: Fri May 14 08:24:01 2004; roger

Suggests:  quantreg, SparseM, tripack, akima, rgl
Packaged: Fri Aug 13 11:18:43 2004; roger

Title: Nonparametric Quantile Regression -- Defunct
Packaged: Sun Oct  3 14:52:19 2004; roger

Title: Nonparametric and Stochastic Efficiency and Productivity
Description: Provides a variety of tools for nonparametric and parametric efficiency measurement.

Description: Nonparametric efficiency measurement and statistical inference via DEA type of estimators (see e.g., Kneip, Simar, and Wilson (2008) <doi:10.1017/S0266466608080651> and Badunenko and Mozharovskyi (2020) <doi:10.1080/01605682.2019.1599778>) as well as Stochastic Frontier estimators for both cross-sectional data and 1st, 2nd, and 4th generation models for panel data (see e.g., Kumbhakar and Lovell (2003), Badunenko and Kumbhakar (2016) <doi:10.1016/j.ejor.2016.04.049>). The stochastic frontier estimators can handle both half-normal and truncated normal models with conditional mean and heteroskedasticity. The marginal effects of determinants can be obtained.