This package implements nonparametric smoothing methods for circular data.

María Oliveira [aut, cre],
María Alonso-Pena [aut, cre],
Jose Ameijeiras-Alonso [aut],
Rosa M. Crujeiras [aut],
Alberto Rodríguez-Casal [aut]
Nonparametric smoothing methods for density and regression estimation involving circular data,
including methods described in Oliveira et al. (2014) and proposals in Alonso-Pena et al. (2021).

Maria Alonso-Pena [aut, cre],
Maria Oliveira [aut],
Jose Ameijeiras-Alonso [aut],
Rosa M. Crujeiras [aut],
Irene Gijbels [aut],
Alberto Rodriguez-Casal [aut],
Felicita Scapini [dtc]
Nonparametric smoothing methods for density and regression estimation involving circular data,
including the estimation of the mean regression function and other conditional characteristics.
Rcpp (>= 1.0.8.3), misc3d, movMF, plotrix, rgl, shape,
Bolstad2

Sample Size Calculation for the Proportional Hazards Cure Model
Chao Cai, Songfeng Wang, Wenbin Lu, Jiajia Zhang
An R-package for calculating sample size based on
Proportional Hazard Mixture Cure (PHMC) Model

Sample Size Calculation for the Proportional Hazards Mixture
Cure Model
An R-package for calculating sample size of a survival trial with or without cure fractions

An R-package for calculating sample size of a survival trial with or without cure fractions.

Provides functions and examples for histogram, kernel (classical, variable bandwidth and transformations based), discrete and semiparametric hazard rate estimators.

person("Tahani", "Coolen-Maturi", email = "tahani.maturi@durham.ac.uk", role = c("cre","aut"))
An implementation of the Nonparametric Predictive Inference approach in R. It provides tools for quantifying uncertainty via lower and upper probabilities. It includes useful functions for pairwise and multiple comparisons: comparing two groups with and without terminated tails, selecting the best group, selecting the subset of best groups, selecting the subset including the best group.

Non-Parametric Maximum Likelihood Estimate for Cohort Samplings
To compute the non-parametric maximum likelihood estimates (NPMLEs) and penalized NPMLEs with SCAD, HARD and LASSO penalties for nested case-control or case-cohort sampling design with time matching under Cox's regression model. It also proposes the standard error formula for estimator using observed profile likelihood. For details about (penalized) NPNLEs see the original paper "Penalized Full Likelihood Approach to Variable Selection for Cox's Regression Model under Nested Case-Control Sampling" by Wang et al. (2019) <doi:10.1007/s10985-019-09475-z>.
Jie-Huei Wang, Chun-Hao Pan, I-Shou Chang, and Chao A. Hsiung