Man pages for pauwels2014
Bayesian Experimental Design for Systems Biology.

active_design	Simulates the active design process.
add_infinitesimal	Finite difference function
add_noise	Noise generative process for the simulations
armijo	Performs armijo line searcc
BFGS_special	An implementation of BFGS method for posterior maximization.
compute_gradient	Finite difference function
compute_gradient_coordinate	Finite difference function
compute_mean_risks	Compute an average risk as a function of credit spent
dmvnorm	Gaussian multivariate density
dream6_design	Simulates the active design process using the comparison...
estimate_risk_dream6	Expected risk estimation (comparison with litterature).
estimate_risk_out_all	Expected risk estimation.
eval_kn_log_like	Evaluates the likelihood of a parameter value
eval_log_like_knobj	Posterior function.
experiment_list1	Molecular perturbations.
exps	List of possible experiments
generate_our_knowledge	Initialize a knowledge list.
generate_sample	An implementation of the Metropolis Hasting algorithm
knobjs	Knowledge lists
log_likelihood	User defined likelihood function.
log_normalize	Normalize in log space
log_prior	User defined log prior
observables	Observable quantities of the model
pauwels2014-package	Reproduce numerical experiments
proj_grad	Least square on the positive orthant
random_design	Simulates a randim design process.
read_knobjs	Summarizes pre-computed results.
reverse_params	Transform log space parameters back to the original space
risk_theta_fun	Risk function
risk_theta_vect	Expected risk based on a posterior sample
sample_function	Generates posterior samples
sample_function_multi_mod_weight	Sample function visiting multiple modes of the posterior
sample_function_single_mod	Sample function visiting a single mode of the posterior.
simulate_experiment	Simulates the dynamics of a molecular perturbation
simulate_experiment_no_transform	Link to the ode solver.
transform_params	User defined parameter transformation function.