ResearchTrend.AI
  • Communities
  • Connect sessions
  • AI calendar
  • Organizations
  • Join Slack
  • Contact Sales
Papers
Communities
Social Events
Terms and Conditions
Pricing
Contact Sales
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2026 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2001.09111
  4. Cited By
spNNGP R package for Nearest Neighbor Gaussian Process models
v1v2 (latest)

spNNGP R package for Nearest Neighbor Gaussian Process models

Journal of Statistical Software (JSS), 2020
24 January 2020
Andrew O. Finley
A. Datta
S. Banerjee
ArXiv (abs)PDFHTML

Papers citing "spNNGP R package for Nearest Neighbor Gaussian Process models"

9 / 9 papers shown
Bayesian Spatial Predictive Synthesis
Bayesian Spatial Predictive Synthesis
D. Cabel
S. Sugasawa
Masahiro Kato
K. Takanashi
K. McAlinn
401
4
0
28 Jan 2025
The inverse Kalman filter
The inverse Kalman filter
X. Fang
Mengyang Gu
272
1
0
14 Jul 2024
Minibatch Markov chain Monte Carlo Algorithms for Fitting Gaussian
  Processes
Minibatch Markov chain Monte Carlo Algorithms for Fitting Gaussian ProcessesBayesian Analysis (Bayes. Anal.), 2023
Matthew J. Heaton
Jacob A. Johnson
268
1
0
26 Oct 2023
Exploring the Efficacy of Statistical and Deep Learning Methods for
  Large Spatial Datasets: A Case Study
Exploring the Efficacy of Statistical and Deep Learning Methods for Large Spatial Datasets: A Case StudyJournal of Agricultural Biological and Environmental Statistics (JABES), 2023
A. Hazra
Pratik Nag
Rishikesh Yadav
Ying Sun
248
8
0
10 Aug 2023
Random forests for binary geospatial data
Random forests for binary geospatial data
Arkajyoti Saha
A. Datta
AI4CE
338
3
0
27 Feb 2023
Vecchia-approximated Deep Gaussian Processes for Computer Experiments
Vecchia-approximated Deep Gaussian Processes for Computer ExperimentsJournal of Computational And Graphical Statistics (JCGS), 2022
Annie Sauer
A. Cooper
R. Gramacy
359
50
0
06 Apr 2022
Modelling Big, Heterogeneous, Non-Gaussian Spatial and Spatio-Temporal
  Data using FRK
Modelling Big, Heterogeneous, Non-Gaussian Spatial and Spatio-Temporal Data using FRK
Matthew Sainsbury-Dale
A. Zammit‐Mangion
Noel Cressie
160
6
0
06 Oct 2021
Random Forests for dependent data
Random Forests for dependent data
Arkajyoti Saha
Sumanta Basu
A. Datta
AI4CE
311
9
0
30 Jul 2020
Highly Scalable Bayesian Geostatistical Modeling via Meshed Gaussian
  Processes on Partitioned Domains
Highly Scalable Bayesian Geostatistical Modeling via Meshed Gaussian Processes on Partitioned Domains
M. Peruzzi
Sudipto Banerjee
Andrew O. Finley
288
61
0
25 Mar 2020
1
Page 1 of 1