Help for package rgml0

Type:

Package

Title:

Random Graphical Model Estimation under L0 Penalty

Version:

0.0.1

Description:

Provides functions for estimating sparse precision matrices using a random graphical model framework under an L0-style penalty. The method evaluates candidate theta values and returns both continuous and binary precision matrices representing inferred network structures.

License:

MIT + file LICENSE

Encoding:

UTF-8

RoxygenNote:

7.3.3

Imports:

MASS

Suggests:

testthat (≥ 3.0.0)

Config/testthat/edition:

NeedsCompilation:

Packaged:

2026-04-26 18:43:47 UTC; Matan

Author:

Mahdieh Farzin Asanjan [aut, cre], Vilda Purutcuoglu [aut], Ernst Wit [aut]

Maintainer:

Mahdieh Farzin Asanjan <mah.farzin2010@gmail.com>

Repository:

CRAN

Date/Publication:

2026-04-28 19:40:07 UTC

Random graphical model estimation under L0-penalty

Description

Estimates sparse precision matrices from a covariance matrix using an iterative sparse-step approximation under an L0-style penalty. The method evaluates a grid of theta values and returns both continuous and binary precision matrices representing the inferred network structure.

Usage

rgml0(
  S,
  theta.jump = 0.1,
  n.iter = 5,
  gamma0 = 1000,
  gammastop = 1e-04,
  IMsteps = 2,
  gammastep = 1.1,
  force.zero = TRUE,
  threshold = 1e-07,
  use_diag = FALSE
)

Arguments

S

A numeric covariance matrix.

theta.jump

Step size for candidate theta values.

n.iter

Number of inner iterations.

gamma0

Initial gamma value.

gammastop

Stopping value for gamma.

IMsteps

Currently unused.

gammastep

Factor used to decrease gamma.

force.zero

Logical flag (currently unused).

threshold

Threshold for binarizing the precision matrix.

use_diag

Logical flag (currently unused).

Value

A list where each element contains:

precision_matrix: Estimated precision matrix.
binary_precision_matrix: Thresholded binary matrix.
theta: Theta value used.
sample_index: Index of theta.

Examples

S <- diag(3)
out <- rgml0(S, theta.jump = 0.4, n.iter = 1,
             gamma0 = 1, gammastop = 0.1, gammastep = 2)
length(out)