| Title: | Circular Graphical Model Estimation |
| Version: | 0.1.0 |
| Description: | Provides methods for estimating circular graphical models using maximum likelihood estimation (MLE) and circular mean squared error (CMSE) approaches. The package includes tools for model fitting, network construction, network evaluation, and visualization. The CMSE-based methodology is related to Dar (2023) https://open.metu.edu.tr/handle/11511/102577. The package supports both simulated circular data and real-world applications, including gene-expression network analysis. |
| License: | MIT + file LICENSE |
| Encoding: | UTF-8 |
| Imports: | graphics, igraph, stats |
| Suggests: | knitr, rmarkdown |
| VignetteBuilder: | knitr |
| Depends: | R (≥ 4.1.0) |
| Config/roxygen2/version: | 8.0.0 |
| NeedsCompilation: | no |
| Packaged: | 2026-07-05 19:34:47 UTC; fatemeh |
| Author: | Fatemeh Sarasir 
[aut, cre],
Vilda Purutcuoglu
[aut],
Elif Dogan Dar
[aut] |
| Maintainer: | Fatemeh Sarasir <fsarasir@gmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2026-07-14 16:30:09 UTC |
circularNet: Circular Graphical Model Estimation
Description
Provides methods for estimating circular graphical models using maximum likelihood estimation (MLE) and circular mean squared error (CMSE) approaches. The package includes tools for model fitting, network construction, network evaluation, and visualization. The CMSE-based methodology is related to Dar (2023) https://open.metu.edu.tr/handle/11511/102577. The package supports both simulated circular data and real-world applications, including gene-expression network analysis.
Author(s)
Maintainer: Fatemeh Sarasir fsarasir@gmail.com (ORCID)
Authors:
Fatemeh Sarasir fsarasir@gmail.com (ORCID)
Vilda Purutcuoglu (ORCID)
Elif Dogan Dar (ORCID)
Circular Mean Squared Error
Description
Computes the circular mean squared error.
Usage
CMSE(par, data)
Arguments
par |
Parameter vector. |
data |
Data matrix (p x n). |
Value
Numeric CMSE value.
Examples
data <- matrix(
runif(20, -pi, pi),
nrow = 4
)
par <- rep(0, nrow(data))
CMSE(par, data)
Build Adjacency Matrix
Description
Constructs a binary network from estimated coefficients using thresholding.
Usage
build_network(beta_hat, threshold = 0.5)
Arguments
beta_hat |
Coefficient matrix. |
threshold |
Threshold value. |
Value
Binary adjacency matrix.
Examples
set.seed(1)
data <- matrix(
runif(100, -pi, pi),
ncol = 5
)
fit <- fit_circular_model(data)
build_network(
fit,
threshold = 0.2
)
Evaluate Estimated Network
Description
Computes performance metrics comparing an estimated network with a reference network.
Usage
evaluate_network(est, truth)
Arguments
est |
Estimated adjacency matrix. |
truth |
Ground-truth adjacency matrix. |
Value
List of evaluation metrics.
Examples
est <- matrix(
c(0,1,0,
1,0,1,
0,1,0),
nrow = 3,
byrow = TRUE
)
truth <- matrix(
c(0,1,0,
1,0,0,
0,0,0),
nrow = 3,
byrow = TRUE
)
evaluate_network(est, truth)
Fit Circular Graphical Model
Description
Estimates a circular graphical model using node-wise regression and likelihood-based optimization.
Usage
fit_circular_model(data)
Arguments
data |
Matrix of circular observations (rows = observations, columns = variables). |
Value
A coefficient matrix.
Examples
set.seed(1)
data <- matrix(
runif(100, -pi, pi),
ncol = 5
)
fit <- fit_circular_model(data)
round(fit, 2)
Log-Likelihood for Circular Model
Description
Computes the negative log-likelihood used for parameter estimation in the likelihood-based circular graphical model.
Usage
log_likelihood(params, theta, phi, mu_theta, mu_phi)
Arguments
params |
Parameter vector containing alpha, beta coefficients, and kappa. |
theta |
Response variable. |
phi |
Predictor matrix. |
mu_theta |
Mean of theta. |
mu_phi |
Mean of phi. |
Value
Negative log-likelihood value.
Examples
theta <- runif(20, -pi, pi)
phi <- matrix(runif(40, -pi, pi), ncol = 2)
params <- c(0.1, 0.1, 0.1, 1)
log_likelihood(
params,
theta,
phi,
mean(theta),
colMeans(phi)
)
Wrap Angles to (-pi, pi)
Description
Transforms numeric values to the interval (-pi, pi).
Usage
mod_twopi(x)
Arguments
x |
Numeric vector of angles. |
Value
Numeric vector of wrapped angles.
Examples
mod_twopi(c(-4 * pi, -3, 0, 3, 4 * pi))
Fit Circular Model Using CMSE
Description
Fits a circular regression model using CMSE optimization.
Usage
model_fit_cmse(data)
Arguments
data |
Data matrix (p x n), where rows correspond to variables and columns correspond to samples. |
Value
A list containing:
-
beta: coefficient matrix -
code: optimization convergence codes
Examples
set.seed(1)
data <- matrix(
runif(40, -pi, pi),
nrow = 4
)
model_fit_cmse(data)
Plot Estimated Network
Description
Visualizes an adjacency matrix as a network graph.
Usage
plot_network(adj_matrix)
Arguments
adj_matrix |
Adjacency matrix. |
Value
A network plot.
Examples
adj <- matrix(
c(0,1,1,
1,0,0,
1,0,0),
nrow = 3,
byrow = TRUE
)
plot_network(adj)
Compute Residuals
Description
Computes predicted and observed circular values.
Usage
residual_fnc(par, data)
Arguments
par |
Parameter vector. |
data |
Data matrix (p x n). |
Value
List with predicted and observed values.
Examples
data <- matrix(
runif(20, -pi, pi),
nrow = 4
)
par <- rep(0, nrow(data))
residual_fnc(par, data)