Package: bliss 1.1.1

Paul-Marie Grollemund

bliss: Bayesian Functional Linear Regression with Sparse Step Functions

A method for the Bayesian functional linear regression model (scalar-on-function), including two estimators of the coefficient function and an estimator of its support. A representation of the posterior distribution is also available. Grollemund P-M., Abraham C., Baragatti M., Pudlo P. (2019) <doi:10.1214/18-BA1095>.

Authors:Paul-Marie Grollemund [aut, cre], Isabelle Sanchez [ctr], Meili Baragatti [ctr]

bliss_1.1.1.tar.gz


bliss_1.1.1.tar.gz(r-4.4-noble)
bliss_1.1.1.tgz(r-4.4-emscripten)bliss_1.1.1.tgz(r-4.3-emscripten)
bliss.pdf |bliss.html
bliss/json (API)
NEWS

# Install 'bliss' in R:
install.packages('bliss', repos = c('https://pmgrollemund.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/pmgrollemund/bliss/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
Datasets:

On CRAN:

5.16 score 2 stars 36 scripts 317 downloads 31 exports 31 dependencies

Last updated 4 months agofrom:3ea5ce3cec. Checks:OK: 1. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 09 2024

Exports:%between%BIC_model_choiceBliss_Gibbs_SamplerBliss_Simulated_Annealingbuild_Fourier_basischange_gridchoose_betacompute_beta_posterior_densitycompute_beta_samplecompute_random_walkcompute_starting_point_sanncorr_matrixdetermine_intervalsdo_need_to_reducedposteriorfit_Blissimage_Blissintegrate_trapezeinterpretation_plotlines_blisspdexppost_treatment_blisspredict_blisspredict_bliss_distributionprintblissreduce_xsigmoidsigmoid_sharpsimsim_xsupport_estimation

Dependencies:clicolorspacefansifarverggplot2gluegtableisobandlabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepillarpkgconfigR6RColorBrewerRcppRcppArmadilloRcppProgressrlangscalestibbleutf8vctrsviridisLitewithr

Introduction to BLiSS method

Rendered fromBlissIntro.Rmdusingknitr::rmarkdownon Oct 09 2024.

Last update: 2024-05-09
Started: 2018-03-07

Readme and manuals

Help Manual

Help pageTopics
between%between%
BIC_model_choiceBIC_model_choice
bliss: Bayesian functional Linear regression with Sparse Step functionsbliss-package bliss
Bliss_Gibbs_SamplerBliss_Gibbs_Sampler
Bliss_Simulated_AnnealingBliss_Simulated_Annealing
build_Fourier_basisbuild_Fourier_basis
change_gridchange_grid
choose_betachoose_beta
compute_beta_posterior_densitycompute_beta_posterior_density
compute_beta_samplecompute_beta_sample
compute_chains_infocompute_chains_info
compute_random_walkcompute_random_walk
compute_starting_point_sanncompute_starting_point_sann
corr_matrixcorr_matrix
a list of datadata1
determine_intervalsdetermine_intervals
do_need_to_reducedo_need_to_reduce
dposteriordposterior
fit_Blissfit_Bliss
image_Blissimage_Bliss
integrate_trapezeintegrate_trapeze
interpretation_plotinterpretation_plot
lines_blisslines_bliss
A list of param for bliss modelparam1
pdexppdexp
post_treatment_blisspost_treatment_bliss
predict_blisspredict_bliss
predict_bliss_distributionpredict_bliss_distribution
Print a bliss Objectprintbliss
reduce_xreduce_x
A result of the BliSS methodres_bliss1
sigmoidsigmoid
sigmoid_sharpsigmoid_sharp
simsim
sim_xsim_x
support_estimationsupport_estimation