Package 'robusttseq'

Huber M-estimator for location and scale

Description

Computes robust estimates of mean (mu) and scale (sigma) using Huber's M-estimator.

Usage

huber_estimation(x, c = 1.345, tol = 1e-06, max_iter = 100)

Arguments

x	Numeric vector of observations.
c	Tuning constant (default 1.345). Larger values make the estimator closer to the mean, smaller values make it more robust.
tol	Convergence tolerance (default 1e-6).
max_iter	Maximum number of iterations (default 100).

Value

A list with two elements:

mu

Estimated robust mean

sigma

Estimated robust scale (standard deviation)

Examples

set.seed(123)
x <- c(rnorm(100), 10)  # outlier at 10
huber_estimation(x)

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Robust Huber-Moderated t-Test for Gene Expression

Description

Performs a moderated two-sample t-test on count data using Huber M-estimators and empirical Bayes variance shrinkage (via limma::squeezeVar).

Usage

robust_huber_moderated(counts, group, c_huber = 1.345, robust_prior = TRUE)

Arguments

counts	Numeric matrix of counts (genes in rows, samples in columns).
group	Factor indicating group membership for each column/sample.
c_huber	Tuning constant for Huber estimator (default 1.345).
robust_prior	Logical; if TRUE, uses a robust empirical Bayes prior (default TRUE).

Value

Numeric vector of p-values, one per gene.

Examples

library(limma)
counts <- matrix(rpois(200, lambda = 10), nrow = 20)
group <- factor(rep(c("A", "B"), each = 5))
pvals <- robust_huber_moderated(counts, group)

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Two-Sample Robust t-Test (Huber)

Description

Performs a robust two-sample t-test using Huber M-estimators for location and scale.

Usage

robust_t_test(x, y, c = 1.345)

Arguments

x	Numeric vector of sample 1.
y	Numeric vector of sample 2.
c	Tuning constant for Huber estimator (default 1.345).

Value

An object of class "htest" similar to t.test.

Examples

set.seed(123)
x <- rnorm(30, mean = 5)
y <- rnorm(35, mean = 6)
robust_t_test(x, y)

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