c(person(c("Boris", "P"), "Hejblum", role = c("aut", "cre"),
email = "boris.hejblum@u-bordeaux.fr"),
person("Chariff", "Alkhassim", role = c("aut"),
email = "chariff.alkhassim@u-bordeaux.fr"),
person("Francois", "Caron", role = c("aut"),
email = "francois.caron@stats.ox.ac.uk")
)
graphics, stats, grDevices, ellipse, fastcluster, ggplot2,
pheatmap, reshape2
Dirichlet process mixture of multivariate normal, skew normal or skew t-distributions
modeling oriented towards flow-cytometry data preprocessing applications. Method is
detailed in: Hejblum, Alkhassimn, Gottardo, Caron & Thiebaut (2019) <doi: 10.1214/18-AOAS1209>.
Boris P Hejblum [aut, cre],
Chariff Alkhassim [aut],
Francois Caron [aut]

Dirichlet process mixture of multivariate normal, skew-normal or skew t-distributions
modeling oriented towards flow-cytometry data preprocessing applications.

Nonparametric Smoothing for contaminated data using
Simulation-Extrapolation
This package contains a collection of functions to to perform nonparametric deconvolution using simulation extrapolation (SIMEX). We propose an estimator that adopts the SIMEX idea but bypasses the simulation step in the original SIMEX algorithm. There is no bandwidth parameter and the estimate is determined by appropriately selecting "design points". See details in: Wang, X.F., Sun, J. and Fan, Z. (2011). Deconvolution density estimation with heteroscedastic errors using SIMEX.

Data sets from "Nonlinear Regression Analysis and Its Applications"
R port by Douglas Bates <bates@stat.wisc.edu>
Datasets from Bates and Watts (1988) "Nonlinear Regression
Analysis and Its Applications" with sample code.

Data sets from "Nonlinear Regression Analysis and Its
Applications"

Metrics for Multiple Testing with Correlated Outcomes
Implements methods in Mathur and VanderWeele (in preparation) to characterize global evidence strength across W correlated ordinary least squares (OLS) hypothesis tests. Specifically, uses resampling to estimate a null interval for the total number of rejections in, for example, 95% of samples generated with no associations (the global null), the excess hits (the difference between the observed number of rejections and the upper limit of the null interval), and a test of the global null based on the number of rejections.
stats, doParallel, matrixcalc, StepwiseTest, foreach, mvtnorm