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update to v2.4.0
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DESCRIPTION

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Package: dgpsi
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Type: Package
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Title: Interface to 'dgpsi' for Deep and Linked Gaussian Process Emulations
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Version: 2.3.0-9000
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Version: 2.4.0
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Authors@R: c(
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person('Deyu', 'Ming', role = c('aut', 'cre', 'cph'),
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email = 'deyu.ming.16@ucl.ac.uk'),

LICENSE

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YEAR: 2023
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YEAR: 2024
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COPYRIGHT HOLDER: Deyu Ming

NEWS.md

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# dgpsi 2.3.0-9000 (development version)
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# dgpsi 2.4.0
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- One can now use `design()` to implement sequential designs using `f` and a fixed candidate set passed to `x_cand` with `y_cand = NULL`.
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- The sizes of `.pkl` files written by `write()` are significantly reduced.
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- One can now set different kernel functions to nodes in different layers in a DGP emulator by passing a vector of kernel function names to `name` argument of `dgp()`.

R/initi_py.R

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if ( is.null(py_ver) ) py_ver <- '3.9.13'
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if ( is.null(dgpsi_ver) ) {
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##For devel version
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dgpsi_ver <- c('cython>=0.29.30', 'dill>=0.3.2, <=0.3.5.1', 'jupyter>=1.0.0', 'matplotlib-base>=3.2.1', 'numba >=0.51.2',
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'numpy >=1.18.2', 'pathos ==0.2.9', 'multiprocess ==0.70.13', 'psutil >=5.8.0', 'pybind11 >=2.10.0', 'pythran >=0.11.0',
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'scikit-build >=0.15.0', 'scikit-learn >=0.22.0', 'scipy >=1.4.1', 'tqdm >=4.50.2', 'tabulate >=0.8.7')
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env_name <- 'dgp_si_R_2_3_0_9000'
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#dgpsi_ver <- c('cython>=0.29.30', 'dill>=0.3.2, <=0.3.5.1', 'jupyter>=1.0.0', 'matplotlib-base>=3.2.1', 'numba >=0.51.2',
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# 'numpy >=1.18.2', 'pathos ==0.2.9', 'multiprocess ==0.70.13', 'psutil >=5.8.0', 'pybind11 >=2.10.0', 'pythran >=0.11.0',
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# 'scikit-build >=0.15.0', 'scikit-learn >=0.22.0', 'scipy >=1.4.1', 'tqdm >=4.50.2', 'tabulate >=0.8.7')
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#env_name <- 'dgp_si_R_2_3_0_9000'
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##For release version
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#dgpsi_ver <- 'dgpsi==2.3.0'
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#env_name <- 'dgp_si_R_2_3_0'
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dgpsi_ver <- 'dgpsi==2.4.0'
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env_name <- 'dgp_si_R_2_4_0'
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} else {
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env_name <- paste('dgp_si_R_', gsub(".", "_", dgpsi_ver,fixed=TRUE), sep = "")
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dgpsi_ver <- paste('dgpsi==', dgpsi_ver, sep = "")

README.md

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[![REF](https://img.shields.io/badge/REF-Linked%20GP-informational)](https://doi.org/10.1137/20M1323771)
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[![REF](https://img.shields.io/badge/REF-Deep%20GP-informational)](https://doi.org/10.1080/00401706.2022.2124311)
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[![REF](https://img.shields.io/badge/REF-Linked%20DGP-informational)](https://arxiv.org/abs/2306.01212)
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[![python](https://img.shields.io/badge/Python-dgpsi%20v2.3.0-informational)](https://github.com/mingdeyu/DGP)
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[![python](https://img.shields.io/badge/Python-dgpsi%20v2.4.0-informational)](https://github.com/mingdeyu/DGP)
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![CRAN_License](https://img.shields.io/cran/l/dgpsi?color=green)
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The R package `dgpsi` provides R interface to Python package [`dgpsi`](https://github.com/mingdeyu/DGP) for deep and linked Gaussian process emulations using stochastic imputation (SI).
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> ```
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## Research Notice
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This package is part of an ongoing research initiative. For detailed information about the research aspects and guidelines for use, please refer to our [Research Notice](./RESEARCH-NOTICE.md).
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This package is part of an ongoing research initiative. For detailed information about the research aspects and guidelines for use, please refer to our [Research Notice](https://github.com/mingdeyu/dgpsi-R/blob/master/RESEARCH-NOTICE.md).
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## References
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> [Ming, D. and Williamson, D. (2023) Linked deep Gaussian process emulation for model networks. arXiv:2306.01212](https://arxiv.org/abs/2306.01212)

cran-comments.md

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This is the submission of 'dgpsi' 2.3.0.
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This is the submission of 'dgpsi' 2.4.0.
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Various updates have been made in this version. A detailed list of changes that have been made since the last version 2.2.0 is contained in NEWS.md.
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Various updates have been made in this version. A detailed list of changes that have been made since the last version 2.3.0 is contained in NEWS.md.
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## Test environments
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vignettes/dgpsi.Rmd

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)
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```
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`dgpsi` provides a flexible toolbox for Gaussian process (GP), deep Gaussian process (DGP) and linked (D)GP emulations. In this guide, we show how to use the package to emulate a step function with a three-layered DGP structure. There are other examples showing the functionality of the package in [`Articles`](https://mingdeyu.github.io/dgpsi-R/dev/articles/index.html) on the package website, and how to customize a DGP structure. A comprehensive reference of available functions is documented in [`Reference`](https://mingdeyu.github.io/dgpsi-R/dev/reference/index.html) section of the package website.
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`dgpsi` provides a flexible toolbox for Gaussian process (GP), deep Gaussian process (DGP) and linked (D)GP emulations. In this guide, we show how to use the package to emulate a step function with a three-layered DGP structure. There are other examples showing the functionality of the package in [`Articles`](https://mingdeyu.github.io/dgpsi-R/articles/index.html) on the package website, and how to customize a DGP structure. A comprehensive reference of available functions is documented in [`Reference`](https://mingdeyu.github.io/dgpsi-R/reference/index.html) section of the package website.
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## Load the package
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vignettes/seq_design.Rmd

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### See also
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See [`Sequential Design II`](https://mingdeyu.github.io/dgpsi-R/dev/articles/seq_design_2.html) for the sequential design of a bundle of DGP emulators with automatic terminations.
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See [`Sequential Design II`](https://mingdeyu.github.io/dgpsi-R/articles/seq_design_2.html) for the sequential design of a bundle of DGP emulators with automatic terminations.
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### References
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vignettes/seq_design_2.Rmd

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### See also
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See [`Sequential Design I`](https://mingdeyu.github.io/dgpsi-R/dev/articles/seq_design.html) for the sequential design and automatic structure simplification of a DGP emulator on a 2D simulator.
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See [`Sequential Design I`](https://mingdeyu.github.io/dgpsi-R/articles/seq_design.html) for the sequential design and automatic structure simplification of a DGP emulator on a 2D simulator.

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