README

The bnns package provides tools to fit Bayesian Neural Networks (BNNs) for regression and classification problems. It is designed to be flexible, supporting various network architectures, activation functions, and output types, making it suitable for both simple and complex data analysis tasks.

Features

Installation (stable CRAN version)

install.packages("bnns")

Installation (development version)

# Install devtools if not already installed
if (!requireNamespace("devtools", quietly = TRUE)) {
  install.packages("devtools")
}

# Install bnns
devtools::install_github("swarnendu-stat/bnns")

Getting Started

1. Iris Data

head(iris)
#>   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> 1          5.1         3.5          1.4         0.2  setosa
#> 2          4.9         3.0          1.4         0.2  setosa
#> 3          4.7         3.2          1.3         0.2  setosa
#> 4          4.6         3.1          1.5         0.2  setosa
#> 5          5.0         3.6          1.4         0.2  setosa
#> 6          5.4         3.9          1.7         0.4  setosa

2. Fit a BNN Model

library(bnns)

iris_bnn <- bnns(Sepal.Length ~ -1 + ., data = iris, L = 1, act_fn = 3, nodes = 4, out_act_fn = 1, chains = 1)

3. Model Summary

summary(iris_bnn)
#> Call:
#> bnns.default(formula = Sepal.Length ~ -1 + ., data = iris, L = 1, 
#>     nodes = 4, act_fn = 3, out_act_fn = 1, chains = 1)
#> 
#> Data Summary:
#> Number of observations: 150 
#> Number of features: 6 
#> 
#> Network Architecture:
#> Number of hidden layers: 1 
#> Nodes per layer: 4 
#> Activation functions: 3 
#> Output activation function: 1 
#> 
#> Posterior Summary (Key Parameters):
#>                mean      se_mean         sd       2.5%         25%        50%
#> w_out[1]  0.8345667 0.0728930420 0.65030179 -0.4150176  0.38054488  0.7769148
#> w_out[2] -0.3719132 0.4067431773 0.96605220 -1.7062097 -1.03225732 -0.7365945
#> w_out[3]  0.4783495 0.1965466796 0.86504113 -1.2350476  0.02944919  0.5634587
#> w_out[4]  0.4537029 0.3334670001 0.89069977 -1.3791675  0.09313077  0.5518418
#> b_out     2.2082591 0.0614548175 1.18859472 -0.1036760  1.38416657  2.2072194
#> sigma     0.3015085 0.0004831093 0.01804107  0.2693205  0.28895030  0.3013415
#>                75%     97.5%       n_eff      Rhat
#> w_out[1] 1.2478028 2.1730066   79.589862 1.0254227
#> w_out[2] 0.4680286 1.7548944    5.641059 1.3136052
#> w_out[3] 1.0454306 2.0448172   19.370556 1.1335888
#> w_out[4] 1.0281249 2.0733860    7.134392 1.1997484
#> b_out    3.1573563 4.2214829  374.072451 1.0016806
#> sigma    0.3128869 0.3386066 1394.549362 0.9988699
#> 
#> Model Fit Information:
#> Iterations: 1000 
#> Warmup: 200 
#> Thinning: 1 
#> Chains: 1 
#> 
#> Predictive Performance:
#> RMSE (training): 0.2821305 
#> MAE (training): 0.2234606 
#> 
#> Notes:
#> Check convergence diagnostics for parameters with high R-hat values.

4. Predictions

pred <- predict(iris_bnn)

5. Visualization

plot(iris$Sepal.Length, rowMeans(pred), main = "True vs Predicted", xlab = "True Values", ylab = "Predicted Values")
abline(0, 1, col = "red")

Applications

Regression Example (with custom priors)

Use bnns for regression analysis to model continuous outcomes, such as predicting patient biomarkers in clinical trials.

model <- bnns(Sepal.Length ~ -1 + .,
  data = iris, L = 1, act_fn = 3, nodes = 4,
  out_act_fn = 1, chains = 1,
  prior_weights = list(dist = "uniform", params = list(alpha = -1, beta = 1)),
  prior_bias = list(dist = "cauchy", params = list(mu = 0, sigma = 2.5)),
  prior_sigma = list(dist = "inv_gamma", params = list(alpha = 1, beta = 1))
)

Classification Example

For binary or multiclass classification, set the out_act_fn to 2 (binary) or 3 (multiclass). For example:

# Simulate binary classification data
df <- data.frame(
  x1 = runif(10), x2 = runif(10),
  y = sample(0:1, 10, replace = TRUE)
)

# Fit a binary classification BNN
model <- bnns(y ~ -1 + x1 + x2,
  data = df, L = 2, nodes = c(16, 8),
  act_fn = c(3, 2), out_act_fn = 2, iter = 1e2,
  warmup = 5e1, chains = 1
)

Clinical Trial Applications

Explore posterior probabilities to estimate treatment effects or success probabilities in clinical trials. For example, calculate the posterior probability of achieving a clinically meaningful outcome in a given population.

Documentation

Contributing

Contributions are welcome! Please raise issues or submit pull requests on GitHub.

bnns