Package: DGP4LCF 1.0.0.1

Jiachen Cai

DGP4LCF: Dependent Gaussian Processes for Longitudinal Correlated Factors

Functionalities for analyzing high-dimensional and longitudinal biomarker data to facilitate precision medicine, using a joint model of Bayesian sparse factor analysis and dependent Gaussian processes. This paper illustrates the method in detail: J Cai, RJB Goudie, C Starr, BDM Tom (2023) <doi:10.48550/arXiv.2307.02781>.

Authors:Jiachen Cai [aut, cre]

DGP4LCF_1.0.0.1.tar.gz
DGP4LCF_1.0.0.1.zip(r-4.7-x86_64)DGP4LCF_1.0.0.1.zip(r-4.7-arm64)DGP4LCF_1.0.0.1.zip(r-4.6-x86_64)DGP4LCF_1.0.0.1.zip(r-4.6-arm64)
DGP4LCF_1.0.0.1.tar.gz(r-4.7-arm64)DGP4LCF_1.0.0.1.tar.gz(r-4.7-x86_64)DGP4LCF_1.0.0.1.tar.gz(r-4.6-arm64)DGP4LCF_1.0.0.1.tar.gz(r-4.6-x86_64)
DGP4LCF_1.0.0.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
DGP4LCF/json (API)

# Install 'DGP4LCF' in R:
install.packages('DGP4LCF', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))
Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
Datasets:

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

openblascpp

2.30 score 3 scripts 524 downloads 13 exports 77 dependencies

Last updated from:33cff03546. Checks:10 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-devel-arm64OK150
linux-devel-x86_64OK186
source / vignettesOK254
linux-release-arm64OK149
linux-release-x86_64OK169
windows-devel-arm64OK231
windows-devel-x86_64OK210
windows-release-arm64OK260
windows-release-x86_64OK173
wasm-releaseOK139

Exports:factor_loading_heatmapfactor_score_trajectorygibbs_after_mcem_algorithmgibbs_after_mcem_combine_chainsgibbs_after_mcem_diff_initialsgibbs_after_mcem_load_chainsmcem_algorithmmcem_cov_plotmcem_parameter_setupnumerics_summary_do_not_need_alignmentnumerics_summary_need_alignmentsubject_specific_objectstable_generator

Dependencies:ashbitopscliclustercodacodetoolscolorspacecombinatcorrplotcpp11deldirdeSolvedoParalleldotCall64evaluatefactor.switchingfarverfdafda.uscfdsfieldsFNNforeachggplot2glueGPFDAgtableHDIntervalhdrcdehighrinterpisobanditeratorskernlabKernSmoothknitrkskSampleslabelinglatticelifecyclelocfitlpSolvemapsMASSMatrixMatrixModelsmclustmcmcMCMCpackmgcvmulticoolmvtnormnlmepcaPPpheatmappracmaquantregR6rainbowRColorBrewerRcppRcppArmadilloRcppEigenRCurlrlangS7scalesspamSparseMSuppDistssurvivalvctrsviridisLitewithrxfunyaml

An Example of Irregular Data Analysis
Rendered frombsfadgp_irregular_data_example.Rmdusingknitr::rmarkdown

Last update: 2024-05-29
Started: 2024-05-29

An Example of Regular Data Analysis
Rendered frombsfadgp_regular_data_example.Rmdusingknitr::rmarkdown

Last update: 2024-05-29
Started: 2024-05-29

Readme and manuals

Help Manual

Help pageTopics
Displaying significant factor loadings in the heatmap.factor_loading_heatmap
Plotting figures for factor score trajectory.factor_score_trajectory
Generating posterior samples for parameters (other than DGP parameters) in the model and predicted gene expression for one chain.gibbs_after_mcem_algorithm
Combining from all chains the posterior samples for parameters in the model and predicted gene expressions.gibbs_after_mcem_combine_chains
Generating different initials for multiple chains.gibbs_after_mcem_diff_initials
Loading the saved posterior samples for parameters in the model and predicted gene expressions.gibbs_after_mcem_load_chains
Monte Carlo Expectation Maximization (MCEM) algorithm to return the Maximum Likelihood Estimate (MLE) of DGP Parameters.mcem_algorithm
Visualizing cross-correlations among factors.mcem_cov_plot
Parameters' setup and initial value assignment for the Monte Carlo Expectation Maximization (MCEM) algorithm.mcem_parameter_setup
Numerical summary for important continuous variables that do not need alignment.numerics_summary_do_not_need_alignment
Numerical summary for factor loadings and factor scores, which need alignment.numerics_summary_need_alignment
Initials values.sim_fcs_init
Results when people have irregularly observed time points (some 6 while others 8).sim_fcs_results_irregular_6_8
Results when people are observed at common 8 time points.sim_fcs_results_regular_8
Truth of simulated data.sim_fcs_truth
Constructing subject-specific objects required for Gibbs sampler (for subjects with incomplete observations only).subject_specific_objects
Generating a table listing all possible combinations of the binary variables for one gene.table_generator