README.md
In cirls: Constrained Iteratively Reweighted Least Squares

Constrained Iteratively Reweighted Least Squares

The package is currently under development. A working version is already available on CRAN but an up-to-date version is available here. The documentation is also not finished.

The package cirls provides routines to fit Generalized Linear Models (GLM) with coefficients subject to linear constraints, through a constrained iteratively reweighted least-squares algorithm.

The easiest way to install the cirls package is to install it from CRAN


install.packages("cirls")

The development version can be installed from GitHub using the devtools package as

devtools::install_github("PierreMasselot/cirls")

Please check the file NEWS.md for changes in the development version compared to the CRAN one.

The central function of the package is cirls.fit meant to be passed through the method argument of the glm function. The user is also expected to pass a either constraint matrix or a list of constraint matrices through the Cmat argument, and optionally lower and upper bound vectors lb and ub. Built-in constraints can also be passed through the constr formula interface.

The package also contains dedicated methods inference and model selection.

The example below show how to use the package to perform nonnegative regression. See ?cirls.fit for more comprehensive examples.

# Simulate predictors and response with some negative coefficients
set.seed(111)
n <- 100
p <- 10
betas <- rep_len(c(1, -1), p)
x <- matrix(rnorm(n * p), nrow = n)
y <- x %*% betas + rnorm(n)

# Define constraint matrix
Cmat <- diag(p)

# Fit GLM by CIRLS
res <- glm(y ~ x, method = cirls.fit, Cmat = list(x = Cmat))
coef(res)

# Obtain vcov and confidence intervals
vcov(res)
confint(res)

To come