Package index
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EBmvFR() - Empirical Bayes multivariate functional regression
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EBmvFR.workhorse() - Empirical Bayes multivariate functional regression
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HFT() - Perform Haar-Fisz tranform on count matrix
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HMM_regression() - Compute refined estimate using HMM regression
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TI_regression() - Compute refined estimate using translation invariant wavelet transform
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affected_reg() - Extract coordinates of the regions affected by the different CS
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ash_hmm_version() - Return the implementation version.
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bin_Y() - Binned reponsed data
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cal_Bhat_Shat() - Compute Bhat / Shat for one outcome block (joint-sigma2 form)
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change_fit() - Change postprocessing used in susiF object
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colScale() - Scaling function from r-blogger
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fit_ash_hmm() - Fit an adaptive-shrinkage hidden Markov model
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fit_binary_markov() - Fit the exact two-state binary Markov model
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fit_hmm() - Backward-compatible ash-HMM entry point
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fsusie_log_plot() - fSuSiE Plots using Gviz
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plot_susiF()plot(<susiF>)plot_susiF_pip()plot_susiF_effect() - fSuSiE Plots
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get_fitted_effect() - Access fitted effect l
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init_EBmvFR_obj() - Create an EBmvFR object
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init_susiF_obj() - Initialize a susiF object using regression coefficients
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remap_data() - Ramp matrix of unevenly space data (or non power of two)
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simu_IBSS_ash_vanilla() - Simulate data under a simple mixture normal prior
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simu_IBSS_per_level() - Simulate data under the mixture normal prior
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smash_regression() - Compute refined estimate using translation invariant wavelet transform
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susiF() - Sum of Single Function
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susiF.workhorse() - Sum of Single Function
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univariate_functional_regression() - Wrapper for univariate functional regression used on susiF