Title: Nonparametric test of equality between two copulas
Author: Bruno Remillard and Jean-Francois Plante
Description: This package implements the nonparametric test of equality
between two copulas proposed by Remillard and Scaillet in their

Description: Semiparametric Estimation Exploiting Covariate
Independence in Two-Phase Randomized Trials

Description: Estimation of gene-treatment interactions in randomized clinical trials exploiting gene-treatment independence.
Packaged: 2016-01-28 01:33:51 UTC; Administrator

Title: Estimate Gene-Treatment Interaction Exploiting Randomization
Packaged: 2016-03-17 19:13:59 UTC; Administrator

Description: Estimation of gene-treatment interactions in randomized clinical trials exploiting gene-treatment independence. Methods used in the package refer to J. Y. Dai, M. LeBlanc, and C. Kooperberg (2009) Biometrics <doi:10.1111/j.1541-0420.2008.01046.x>.

Title: Process Data from Wearable Research Devices Using Two-Regression
Description: Application of two-regression algorithms for wearable
research devices. It provides an easy way for users to read in
device data files and apply an appropriate two-regression
algorithm. More information is available from
Hibbing PR, LaMunion SR, Kaplan AS, & Crouter SE (2017)

Title: Develop and Apply Two-Regression Algorithms
Description: Facilitates development and application of two-regression
algorithms for research-grade wearable devices. It provides an easy
way for users to access previously-developed algorithms, and also to
develop their own. Initial motivation came from Hibbing PR, LaMunion SR,
However, other algorithms are now supported. Please see the associated
references in the package documentation for full details of the algorithms
RcppRoll, stats, rlang, lubridate, tidyr
Packaged: 2022-09-05 03:08:39 UTC; prhibbing

Title: A Two-Sample Test for the Equality of Distributions for
person("Jeffrey", "D. Hart", email = "hart@stat.tamu.edu", role = "aut"))
Description: For high-dimensional data whose main feature is a large number, p, of variables but a small sample size, the null hypothesis that the marginal distributions of p variables are the same for two groups is tested. We propose a test statistic motivated by the simple idea of comparing, for each of the p variables, the empirical characteristic functions computed from the two samples. If one rejects this global null hypothesis of no differences in distributions between the two groups, a set of permutation p-values is reported to identify which variables are not equally distributed in both groups.
Packaged: 2018-10-24 08:00:41 UTC; JoseCarlos