Function reference • ngme2

ngme2 pacakge description
`ngme2`	ngme2
Fit the linear mixed effects model
`ngme()`	Fit an additive linear mixed effect model over replicates
`summary(<ngme>)`	Summary of ngme fit result
`ngme_result()`	ngme fit result
`ngme_cov_matrix()`	variance of the data or the latent field
`ngme_post_samples()`	posterior samples of different latent models
Latent models
`ngme_model_types()`	Show ngme model types
`f()`	Specifying a latent process model (wrapper function for each model)
`ar1()`	ngme AR(1) model specification
`matern()`	ngme Matern SPDE model specification
`rw1()`	ngme random walk model of order 1
`rw2()`	ngme random walk model of order 2
`ou()`	ngme Ornstein–Uhlenbeck process specification
`iid()`	ngme iid model specification
`tp()`	ngme tensor-product model specification
`bv()`	Ngme bivariate model specification
`bv_normal()`	Ngme bivariate model specification 2 (theta=0)
`re()`	ngme random effect model
ngme2 noises
`ngme_noise()` `noise_normal()` `noise_nig()` `noise_gal()` `noise_normal_nig()`	ngme noise specification
`ngme_noise_types()`	Show ngme noise types
ngme2 optimization methods
`vanilla()`	Vanilla SGD optimization
`momentum()`	Momentum SGD optimization
`adagrad()`	AdaGrad SGD optimization
`rmsprop()`	Root Mean Square Propagation (RMSProp) SGD optimization
`adam()`	Adam SGD optimization
`adamW()`	AdamW SGD optimization
Prediction and Cross validation
`predict(<ngme>)`	Predict function of ngme2 predict using ngme after estimation
`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)
Data visualization
`plot(<ngme_noise>)`	plot the density of noise (for stationary)
`traceplot()`	Trace plot of ngme fitting
Simulation functions
`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
Auxiliary functions
`precision_matrix_multivariate()`	Compute the precision matrix for multivariate model
`precision_matrix_multivariate_spde()`	Compute the precision matrix for multivariate spde Matern model
`compute_index_corr_from_map()`	Helper function to compute the index_corr vector
`mean_list()`	taking mean over a list of nested lists
`ngme_as_sparse()`	Convert sparse matrix into sparse dgCMatrix
`control_opt()`	Generate control specifications for `ngme()` function.
`control_ngme()`	Generate control specifications for the ngme general model
`control_f()`	Generate control specifications for `f` function
`ngme_parse_formula()`	Parse the formula for ngme function
`ngme_ts_make_A()`	Make observation matrix for time series
`print(<ngme>)`	Print an ngme model
`print(<ngme_model>)`	Print ngme model
`print(<ngme_operator>)`	Print ngme operator
`print(<ngme_replicate>)`	Print ngme object
`print(<ngme_noise>)`	Print ngme noise
`ngme_prior()`	ngme prior specification
`ngme_prior_types()`	Show ngme priors
`test_ngme()`	Test ngme function
`ngme_make_mesh_repls()`	ngme make mesh for different replicates
Distributions
`dig()` `rig()` `pig()` `qig()`	The Inverse-Gaussian (IG) Distribution
`dnig()` `rnig()` `pnig()` `qnig()`	The Normal Inverse-Gaussian (NIG) Distribution
`digam()` `rigam()` `pigam()` `qigam()`	The Inverse-Gamma (IGam) Distribution
`dgig()` `rgig()` `pgig()` `qgig()`	The Generalised Inverse-Gaussian (GIG) Distribution
`dgal()` `rgal()` `pgal()` `qgal()`	The Generalized Asymmetric Laplace (GAL) Distribution
Datasets
`argo_float`	Argo float dataset
`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

Reference

ngme2 pacakge description

Fit the linear mixed effects model

Latent models

ngme2 noises

ngme2 optimization methods

Prediction and Cross validation

Data visualization

Simulation functions

Auxiliary functions

Distributions

Datasets