Reference

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 on operator (theta=0)

bv_matern_normal()

Ngme bivariate model specification (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

precond_sgd()

Preconditioner 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

bfgs()

BFGS 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