Title: Identification of Differentially Expressed Genes
Author: José María Martínez Otzeta, Concepción Arenas, Basilio Sierra, Itziar Irigoien
Maintainer: José María Martínez Otzeta <josemaria.martinezo@ehu.eus>
Description: Automated discovery of differentially expressed genes. The method (called ORdensity) is composed of two phases: discovering potential differentially expressed genes and recognizing differentially expressed genes. It makes use of a permutation resampling procedure to build outlying and density indexes. References: a) Irigoien, I. and Arenas, C. (2018). "Identification of differentially expressed genes by means of outlier detection". <doi:10.1186/s12859-018-2318-8>. b) Martínez-Otzeta, J. M., Irigoien, I., Sierra, B., and Arenas, C. (2020). "ORdensity: user-friendly R package to identify differentially expressed genes". <doi:10.1186/s12859-020-3463-4>.
Imports: cluster, distances, Rfast, plyr, methods, foreach, doRNG,

Title: Ordered Subset Analysis for Case-Control Studies
Author: Xuejun Qin, Elizabeth R. Hauser, Silke Schmidt,(Center for
Description: Genetic heterogeneity can reduce the power for complex
disease gene mapping since only a fraction of the cases in the
collected dataset may carry a specific disease susceptibility
allele. The Ordered Subset Analysis for Case-Control data
(OSACC) program was designed to evaluate evidence for
association in the presence of genetic heterogeneity. For a
more detailed description of the method and results of an
extensive simulation study based on different models of genetic
heterogeneity. Please see Qin et. al.(2010), Ordered Subset
Analysis for Case-Control Studies, Genetic Epidemiology 34(5),

Description: Functions for implementing different versions of the OSCV method in the kernel regression and density estimation frameworks. The package mainly supports the following articles: (1) Savchuk, O.Y., Hart, J.D. (2017). Fully robust one-sided cross-validation for regression functions. Computational Statistics, <doi:10.1007/s00180-017-0713-7> and (2) Savchuk, O.Y. (2017). One-sided cross-validation for nonsmooth density functions, <arXiv:1703.05157>.