Package {rinet}

rinet: Clinical Reference Interval Estimation with Reference Interval Network (RINet)

Description

Predicts statistics of a reference distribution from a mixture of raw clinical measurements (healthy and pathological). Uses pretrained CNN models to estimate the mean, standard deviation, and reference fraction from 1D or 2D sample data. Methods are described in LeBien, Velev, and Roche-Lima (2026) "RINet: synthetic data training for indirect estimation of clinical reference distributions" doi:10.1016/j.jbi.2026.104980.

Setup

The models run on the torch backend, which is downloaded separately from the package. Run torch::install_torch() once after installing rinet.

Model versions

Two 1D models are included. "v2.1" (the default) was retrained on revised simulations with pathological shifts and widths drawn relative to the reference component, and "v2.0" is the model described in the published paper. Select one with the model_version argument of predict_rinet or predict_rinet_1d. The 2D model is v2.0. See news(package = "rinet") for evaluation details. The simulation and training code behind v2.1 is available at https://github.com/jackgle/RINet.

Author(s)

Maintainer: Jack LeBien jlebien@abartyshealth.com

Authors:

• Jack LeBien jlebien@abartyshealth.com

See Also

Useful links:

• https://github.com/jackgle/rinet_r_package

• Report bugs at https://github.com/jackgle/rinet_r_package/issues

Convert correlation to covariance matrix

Description

Convert correlation to covariance matrix

Usage

.correlation_to_covariance(corr_matrix, std_vector)

Extract histogram features from standardized data

Description

Extract histogram features from standardized data

Usage

.extract_features(
  data,
  ndim,
  feature_grid_range = c(-4, 4),
  feature_grid_nbins = 100
)

Internal function to invert the training-time output scaling

Description

Internal function to invert the training-time output scaling

Usage

.inverse_transform(predictions, ndim, version)

Internal function to load the TorchScript model

Description

Internal function to load the TorchScript model

Usage

.load_model(ndim, version)

Predict statistics of the underlying reference distribution from mixture distributions using RINet

Description

Automatically detects whether input data is 1D or 2D and calls the appropriate prediction function. This is the main user-facing function. It estimates the statistics of a "healthy" reference population from a mixture of healthy and pathological measurements.

Usage

predict_rinet(
  data,
  feature_grid_range = c(-4, 4),
  feature_grid_nbins = 100,
  verbose = 0,
  log_scale = TRUE,
  percentiles = c(0.025, 0.975),
  n_bootstrap = 0,
  confidence_level = 0.95,
  model_version = c("v2.1", "v2.0")
)

Arguments

Value

A list of predictions. Each element contains:

Setup

The models run on the torch backend, which is downloaded separately from the package. Run torch::install_torch() once after installing rinet.

Examples

## Not run: 
  # 1D sample (using positive data for log-scale)
  sample_1d <- exp(rnorm(1000, mean = 2, sd = 0.5))
  result <- predict_rinet(sample_1d)

  # 2D sample (using positive data for log-scale)
  sample_2d <- exp(matrix(rnorm(2000, mean = 2, sd = 0.5), ncol = 2))
  result <- predict_rinet(sample_2d)

  # Multiple samples (automatically detected)
  samples <- list(exp(rnorm(1000, mean = 2, sd = 0.5)),
                  exp(rnorm(1000, mean = 2, sd = 0.5)))
  results <- predict_rinet(samples)

  # Reproduce the published paper's 1D model
  result <- predict_rinet(sample_1d, model_version = "v2.0")

## End(Not run)

Predict statistics of the underlying reference distribution from 1D mixture distributions using RINet

Description

Takes one or more 1D samples and predicts the underlying reference population statistics (mean, std, reference fraction) from a mixture of healthy and pathological measurements.

Usage

predict_rinet_1d(
  data,
  feature_grid_range = c(-4, 4),
  feature_grid_nbins = 100,
  verbose = 0,
  log_scale = TRUE,
  percentiles = c(0.025, 0.975),
  n_bootstrap = 0,
  confidence_level = 0.95,
  model_version = c("v2.1", "v2.0")
)

Arguments

Value

A list of predictions. Each element contains:

Examples

## Not run: 
  # Single sample (using positive data for log-scale)
  sample1 <- exp(rnorm(1000, mean = 2, sd = 0.3))
  result <- predict_rinet_1d(sample1)
  print(result[[1]]$mean)

  # Multiple samples
  samples <- list(exp(rnorm(1000, 2, 0.3)), exp(rnorm(1000, 1.5, 0.4)))
  results <- predict_rinet_1d(samples)

## End(Not run)

Predict statistics of the underlying reference distribution from 2D mixture distributions using RINet

Description

Takes one or more 2D samples and predicts the underlying reference population statistics (means, stds, correlation, reference fraction) from a mixture of healthy and pathological measurements.

Usage

predict_rinet_2d(
  data,
  feature_grid_range = c(-4, 4),
  feature_grid_nbins = 100,
  verbose = 0,
  log_scale = TRUE,
  percentiles = c(0.025, 0.975),
  n_bootstrap = 0,
  confidence_level = 0.95
)

Arguments

Value

A list of predictions. Each element contains:

Examples

## Not run: 
  # Single 2D sample (using positive data for log-scale)
  sample1 <- exp(matrix(rnorm(2000, mean = 2, sd = 0.3), ncol = 2))
  result <- predict_rinet_2d(sample1)
  print(result[[1]]$mean)
  print(result[[1]]$covariance)

  # Multiple samples
  samples <- list(exp(matrix(rnorm(2000, mean = 2, sd = 0.3), ncol = 2)),
                  exp(matrix(rnorm(2000, mean = 2, sd = 0.3), ncol = 2)))
  results <- predict_rinet_2d(samples)

## End(Not run)