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