Package: jlmerclusterperm 1.1.4

jlmerclusterperm: Cluster-Based Permutation Analysis for Densely Sampled Time Data

An implementation of fast cluster-based permutation analysis (CPA) for densely-sampled time data developed in Maris & Oostenveld, 2007 <doi:10.1016/j.jneumeth.2007.03.024>. Supports (generalized, mixed-effects) regression models for the calculation of timewise statistics. Provides both a wholesale and a piecemeal interface to the CPA procedure with an emphasis on interpretability and diagnostics. Integrates 'Julia' libraries 'MixedModels.jl' and 'GLM.jl' for performance improvements, with additional functionalities for interfacing with 'Julia' from 'R' powered by the 'JuliaConnectoR' package.

Authors:June Choe [aut, cre, cph]

jlmerclusterperm_1.1.4.tar.gz
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jlmerclusterperm_1.1.4.tgz(r-4.6-any)jlmerclusterperm_1.1.4.tgz(r-4.5-any)
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jlmerclusterperm_1.1.4.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
jlmerclusterperm/json (API)

# Install 'jlmerclusterperm' in R:

install.packages('jlmerclusterperm', repos = c('https://yjunechoe.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/yjunechoe/jlmerclusterperm/issues

Pkgdown/docs site:https://yjunechoe.github.io

On CRAN:

cluster-based-permutation-test eeg eyetracking mixed-effects-models timeseries

4.99 score 14 stars 14 scripts 567 downloads 23 exports 20 dependencies

Last updated from:a16a95cde5. Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel-x86_64	OK	141
source / vignettes	OK	234
linux-release-x86_64	OK	149
macos-release-arm64	OK	99
macos-oldrel-arm64	OK	102
windows-devel	OK	430
windows-release	OK	599
windows-oldrel	OK	520
wasm-release	OK	137

Exports:calculate_clusters_pvalues clusterpermute clusters_are_comparable compute_timewise_statistics extract_empirical_clusters extract_null_cluster_dists get_rng_seed get_rng_state glance jlmer jlmerclusterperm_setup julia_progress julia_setup_ok make_jlmer_spec permute_by_predictor permute_timewise_statistics reset_rng_state set_rng_seed set_rng_state stopJulia tidy to_jlmer walk_threshold_steps

Dependencies:backports boot cli generics JuliaConnectoR JuliaFormulae lattice lme4 MASS Matrix minqa nlme nloptr rbibutils Rcpp RcppEigen Rdpack reformulas rlang rrapply

Introduction to jlmerclusterperm

Overview of CPA | Package design | Organization of vignettes | Compared to other implementations | Further readings

Rendered fromjlmerclusterperm.Rmdusingknitr::rmarkdown

Last update: 2024-09-04
Started: 2023-06-07

Help page	Topics
Calculate bootstrapped p-values of cluster-mass statistics	calculate_clusters_pvalues clusters_are_comparable
Tidiers for cluster permutation test objects	cluster_permutation_tidiers tidy.empirical_clusters tidy.null_cluster_dists tidy.timewise_statistics
Conduct a cluster-based permutation test	clusterpermute
Fit Julia regression models to each time point of a time series data	compute_timewise_statistics
Detect largest clusters from a time sequence of predictor statistics	extract_empirical_clusters
Construct a null distribution of cluster-mass statistics	extract_null_cluster_dists
Fit a Julia regression model using jlmer specifications	jlmer
Initial setup for the jlmerclusterperm package	jlmerclusterperm_setup
Tidier methods for Julia regression models	glance.jlmer_mod julia_model_tidiers tidy.jlmer_mod
Set/get options for Julia progress bar	julia_progress
Interface to the Julia RNG	get_rng_seed get_rng_state julia_rng reset_rng_state set_rng_seed set_rng_state
Check Julia requirements for jlmerclusterperm	julia_setup_ok
Create a specifications object for fitting regression models in Julia	make_jlmer_spec
Permute data while respecting grouping structure(s) of observations	permute_by_predictor
Simulate cluster-mass statistics via bootstrapped permutations	permute_timewise_statistics
Fit a Julia regression model using lme4 syntax	to_jlmer
Test the probability of cluster-mass statistics over a range of threshold values	walk_threshold_steps

Package: jlmerclusterperm 1.1.4

jlmerclusterperm: Cluster-Based Permutation Analysis for Densely Sampled Time Data

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