.install_pkg            Installs Julia packages if needed
.julia_project_status   Obtain the status of the current Julia project
.set_seed               Set a seed both in Julia and R
.using                  Loads Julia packages
BNN                     Create a Bayesian Neural Network
BNN.totparams           Obtain the total parameters of the BNN
BayesFluxR_setup        Set up of the Julia environment needed for
                        BayesFlux
Chain                   Chain various layers together to form a network
Dense                   Create a Dense layer with 'in_size' inputs and
                        'out_size' outputs using 'act' activation
                        function
Gamma                   Create a Gamma Prior
InverseGamma            Create an Inverse-Gamma Prior
LSTM                    Create an LSTM layer with 'in_size' input size,
                        and 'out_size' hidden state size
Normal                  Create a Normal Prior
RNN                     Create a RNN layer with 'in_size' input,
                        'out_size' hidden state and 'act' activation
                        function
Truncated               Truncates a Distribution
bayes_by_backprop       Use Bayes By Backprop to find Variational
                        Approximation to BNN.
find_mode               Find the MAP of a BNN using SGD
get_random_symbol       Creates a random string that is used as
                        variable in julia
initialise.allsame      Initialises all parameters of the network, all
                        hyper parameters of the prior and all
                        additional parameters of the likelihood by
                        drawing random values from 'dist'.
likelihood.feedforward_normal
                        Use a Normal likelihood for a Feedforward
                        network
likelihood.feedforward_tdist
                        Use a t-Distribution likelihood for a
                        Feedforward network
likelihood.seqtoone_normal
                        Use a Normal likelihood for a seq-to-one
                        recurrent network
likelihood.seqtoone_tdist
                        Use a T-likelihood for a seq-to-one recurrent
                        network.
madapter.DiagCov        Use the diagonal of sample covariance matrix as
                        inverse mass matrix.
madapter.FixedMassMatrix
                        Use a fixed mass matrix
madapter.FullCov        Use the full covariance matrix as inverse mass
                        matrix
madapter.RMSProp        Use RMSProp to adapt the inverse mass matrix.
mcmc                    Sample from a BNN using MCMC
opt.ADAM                ADAM optimiser
opt.Descent             Standard gradient descent
opt.RMSProp             RMSProp optimiser
posterior_predictive    Draw from the posterior predictive distribution
prior.gaussian          Use an isotropic Gaussian prior
prior.mixturescale      Scale Mixture of Gaussian Prior
prior_predictive        Sample from the prior predictive of a Bayesian
                        Neural Network
sadapter.Const          Use a constant stepsize in mcmc
sadapter.DualAverage    Use Dual Averaging like in STAN to tune
                        stepsize
sampler.AdaptiveMH      Adaptive Metropolis Hastings as introduced in
sampler.GGMC            Gradient Guided Monte Carlo
sampler.HMC             Standard Hamiltonian Monte Carlo (Hybrid Monte
                        Carlo).
sampler.SGLD            Stochastic Gradient Langevin Dynamics as
                        proposed in Welling, M., & Teh, Y. W. (n.d.).
                        Bayesian Learning via Stochastic Gradient
                        Langevin Dynamics. 8.
sampler.SGNHTS          Stochastic Gradient Nose-Hoover Thermostat as
                        proposed in
summary.BNN             Print a summary of a BNN
tensor_embed_mat        Embed a matrix of timeseries into a tensor
to_bayesplot            Convert draws array to conform with 'bayesplot'
vi.get_samples          Draw samples form a variational family.
