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