R: Linear Latent Non-Gaussian Models with Flexible Distributions

# Linear Latent Non-Gaussian Models with Flexible Distributions

## Documentation for package ‘ngme2’ version 0.9.7

- DESCRIPTION file.
- User guides, package vignettes and other documentation.
- Package NEWS.

## Help Pages

A B C D F G I M N O P Q R S T V

## -- A --

adagrad | AdaGrad SGD optimization
adam | Adam SGD optimization
adamW | AdamW SGD optimization
adaptive_gd | Adaptive gradient descent
ar | ngme AR(p) model specification
ar1 | ngme AR(1) model specification
argo_float | Argo float dataset
arma | ngme ARMA(p, q) model specification
arma11 | Convenience wrapper for ARMA(1,1)

## -- B --

batch_decay | Batch/checkpoint decay helper
batch_means_ci | Pooled Batch-Means Confidence Intervals from Multiple Chains
batch_means_estimator | Batch-Means Covariance Estimator for SGD Trajectories
bfgs | BFGS optimization
bv | Ngme bivariate model specification
bv_matern | Ngme bivariate model with Matern sub_models

## -- C --

calibrate_inv_exp_lambda | Calibrate Inverse-Exponential Prior for NIG Driven Noise
calibrate_inv_exp_lambda_driven_nig | Calibrate Inverse-Exponential Prior for NIG Driven Noise
cienaga | The swamp of Cienaga Grande in Santa Marta, Colombia
cienaga.border | The x y location of the border of the swamp of Cienaga Grande in Santa Marta, Colombia
compare_noise_kld | Compare noise objects using Kullback-Leibler divergence
compute_index_corr_from_map | Helper function to compute the index_corr vector
compute_log_like | Compute Gaussian log-likelihood
compute_ngme_CI | Refit an Existing ngme Object with SGD and Compute Batch-Means CI
compute_ngme_ci | Refit an Existing ngme Object with SGD and Compute Batch-Means CI
compute_ngme_sgld_samples | Refit an Existing ngme Object with SGLD and Extract Samples
compute_score_given_pred | Compute the scores given the prediction
control_ngme | Generate control specifications for the ngme model
control_opt | Generate control specifications for 'ngme()' function.
control_opt_batch_ci | Generate CI-focused control settings for batch-means inference
create_paired_cv_splits | Create paired indices for bivariate cross-validation Ensures that paired observations (e.g., u_wind and v_wind at same location) are kept together in the same fold
cross_validation | Compute the cross-validation for the ngme model Perform cross-validation for ngme model first into sub_groups (a list of target, and train data)

## -- D --

dgal | The Generalized Asymmetric Laplace (GAL) Distribution
dgig | The Generalised Inverse-Gaussian (GIG) Distribution
dig | The Inverse-Gaussian (IG) Distribution
digam | The Inverse-Gamma (IGam) Distribution
dnig | The Normal Inverse-Gaussian (NIG) Distribution

## -- F --

f | Specifying a latent process model (wrapper function for each model)

## -- G --

gal | The Generalized Asymmetric Laplace (GAL) Distribution
generic | Generic precision matrix operator
generic_ns | Non-stationary precision matrix operator with custom matrix combinations
get_data_from_formula | Extracts design matrix from a formula and data.
get_parameter_distance | Calculate parameter distance from true values
get_trace_trajectories | Get trace trajectories from ngme fitting
get_trajectories | get the trajectories of parameters of the model
gig | The Generalised Inverse-Gaussian (GIG) Distribution

## -- I --

ig | The Inverse-Gaussian (IG) Distribution
igam | The Inverse-Gamma (IGam) Distribution
iid | ngme iid model specification

## -- M --

make_time_series_cv_index | Create Time Series Cross-Validation Indices
matern | ngme Matern SPDE model specification
mean_list | taking mean over a list of nested lists
merge_noise | Merge 2 noise into 1 noise
merge_replicates | Merge model of replicates into model of 1 replicate given train_idx and test_idx, the merged model contains all the information of train_idx from different replicates.
momentum | Momentum SGD optimization

## -- N --

name2fun | Convert transformation name to function
ngme | Fit an additive linear mixed effect model over replicates
ngme_as_sparse | Convert sparse matrix into sparse dgCMatrix
ngme_batch_ci | Batch-Means Confidence Intervals from an ngme Fit
ngme_cov_matrix | variance of the data or the latent field
ngme_make_mesh_repls | ngme make mesh for different replicates
ngme_model_types | Show ngme model types
ngme_noise | ngme noise specification
ngme_noise_types | Show ngme noise types
ngme_optimizers | List supported optimizers
ngme_parse_formula | Parse the formula for ngme function
ngme_post_samples | posterior samples of different latent models
ngme_prior_types | Show ngme priors
ngme_result | Access the result of a ngme fitted model
ngme_sgld_ci | Quantile Confidence Intervals from SGLD Samples
ngme_sgld_samples | Extract Posterior-like Samples from Stored SGLD Trajectories
ngme_ts_make_A | Make observation matrix for time series
ngme_update | Check whether a newer stable version of ngme2 is available
nig | The Normal Inverse-Gaussian (NIG) Distribution
noise_gal | ngme noise specification
noise_nig | ngme noise specification
noise_normal | ngme noise specification
noise_normal_nig | ngme noise specification
noise_skew_t | ngme noise specification
noise_t | ngme noise specification

## -- O --

openmp_test | Test OpenMP availability and report the number of threads.
ou | Ornstein-Uhlenbeck Process Model

## -- P --

pgal | The Generalized Asymmetric Laplace (GAL) Distribution
pgig | The Generalised Inverse-Gaussian (GIG) Distribution
pig | The Inverse-Gaussian (IG) Distribution
pigam | The Inverse-Gamma (IGam) Distribution
plot.ngme_noise | Plot the density of one or more stationary noise objects
plot.ngme_sgld_ci | Plot Posterior Distributions from SGLD Samples
plot.parameter_distance | Plot method for parameter_distance
pnig | The Normal Inverse-Gaussian (NIG) Distribution
poly_decay | Polynomial schedule helper
posterior_plot | Plot Posterior Distributions from SGLD Samples
precision_matrix_multivariate | Compute the precision matrix for multivariate model
precision_matrix_multivariate_spde | Compute the precision matrix for multivariate spde Matern model
precond_sgd | Preconditioner SGD optimization
predict.ngme | Predict function of ngme2 predict using ngme after estimation
print.ngme | Print an ngme model
print.ngme_model | Print ngme model
print.ngme_noise | Print ngme noise
print.ngme_operator | Print ngme operator
print.ngme_replicate | Print ngme object
print.ngme_trajectories | Print method for ngme_trajectories
print.noise_kld_comparison | Print method for noise_kld_comparison
print.parameter_distance | Print method for parameter_distance
priors | Prior Container
prior_half_cauchy | Prior Half-Cauchy
prior_inv_exp | Prior Inverse-Exponential
prior_inv_exponential | Prior Inverse-Exponential
prior_none | Prior None
prior_normal | Prior Normal
prior_pc_sd | Prior PC-SD

## -- Q --

qgal | The Generalized Asymmetric Laplace (GAL) Distribution
qgig | The Generalised Inverse-Gaussian (GIG) Distribution
qig | The Inverse-Gaussian (IG) Distribution
qigam | The Inverse-Gamma (IGam) Distribution
qnig | The Normal Inverse-Gaussian (NIG) Distribution

## -- R --

re | ngme random effect model
rgal | The Generalized Asymmetric Laplace (GAL) Distribution
rgig | The Generalised Inverse-Gaussian (GIG) Distribution
rig | The Inverse-Gaussian (IG) Distribution
rigam | The Inverse-Gamma (IGam) Distribution
rmsprop | Root Mean Square Propagation (RMSProp) SGD optimization
rnig | The Normal Inverse-Gaussian (NIG) Distribution
rw1 | Random Walk Model of Order 1 (RW1)
rw2 | Random Walk Model of Order 2 (RW2)

## -- S --

sgd | Vanilla SGD optimization
sgld | Stochastic Gradient Langevin Dynamics (SGLD) optimization
simulate.ngme | Simulate from a ngme object (possibly with replicates)
simulate.ngme_model | Simulate latent process with noise
simulate.ngme_noise | Simulate ngme noise object
spacetime | Ngme space-time non-separable model specification
stepsize_control | Unified stepsize control
stepsize_decay | Stepsize decay schedule
stepsize_schedule | Stepsize schedule
summary.ngme | Summary of ngme fit result
summary.ngme_batch_ci | Summary for Batch-Means CI Results

## -- T --

tp | ngme tensor-product model specification
traceplot | Trace plot of ngme fitting

## -- V --

var1 | ngme VAR(1) bivariate model specification (Cayley re-parameterization)