Title: Inference for the Stress-Strength Model R = P(Y<X)
Description: Confidence intervals and point estimation for R under various parametric model assumptions; likelihood inference based on classical first-order approximations and higher-order asymptotic procedures.

Description: This tool can develop hybridization probes for target sequences based on melting temperature value calculated by R package 'TmCalculator' <https://CRAN.R-project.org/package=TmCalculator> and methods extended from Beliveau, B. J.,(2018) <doi:10.1073/pnas.1714530115>, and those hybridization probes can be used to capture specific target regions in fluorescence in situ hybridization and next generation sequence experiments.
Imports: TmCalculator (>= 1.0.0),Biostrings(>= 2.12.0)

Description: Hybridization probes for target sequences can be made based on melting temperature value calculated by R package 'TmCalculator' <https://CRAN.R-project.org/package=TmCalculator> and methods extended from Beliveau, B. J.,(2018) <doi:10.1073/pnas.1714530115>, and those hybridization probes can be used to capture specific target regions in fluorescence in situ hybridization and next generation sequence experiments.
Imports: TmCalculator (>= 1.0.2),Biostrings(>= 2.12.0)

Title:  Reliability for gene expression from Affymetrix chip
Description: Most statistical methods for finding interesting genes are focusing on the summary value with large fold change or large variations. Very few methods are considered the probe level data. This package is to calculate the reliability of the gene expression data from Affymetrix chip using the probe-level data.
Packaged: Mon Jul  3 14:54:16 2006; Kyung

Packaged: Sun Mar  1 12:30:01 2009; hornik

Packaged: Mon Sep 18 21:07:27 2006; hornik

Title: Probit with Spatial Dependence, SAR and SEM Models
Author: Davide Martinetti and Ghislain Geniaux
Description: Binomial Spatial Probit models for big data.
Depends: Matrix, methods, Rcpp, speedglm
Packaged: 2016-03-08 08:42:33 UTC; dmartinetti

Title: Probit with Spatial Dependence, SAR, SEM and SARAR Models
Description: Fast estimation of binomial spatial probit regression models with spatial autocorrelation for big datasets.
Imports: numDeriv, RANN, Rcpp (>= 1.0.4.6), methods
Packaged: 2021-06-28 15:02:14 UTC; dmartinetti
Author: Davide Martinetti [aut, cre] (<https://orcid.org/0000-0003-2047-1793>),