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"fmri_benchmark_datasets" ] }, { "page": "fmri_hrf_estimate-methods", "title": "Methods for smooth HRF estimates", "topics": [ "as.matrix.fmri_hrf_estimate", "coef.fmri_hrf_estimate", "fmri_hrf_estimate-methods", "predict.fmri_hrf_estimate", "print.fmri_hrf_estimate" ] }, { "page": "fmri_job", "title": "Construct a per-subject job recipe", "topics": [ "fmri_job" ] }, { "page": "fmri_latent_lm", "title": "Fast fMRI Regression Model Estimation from a Latent Component Dataset", "topics": [ "fmri_latent_lm" ] }, { "page": "fmri_lm", "title": "Fit a Linear Regression Model for fMRI Data Analysis", "topics": [ "fmri_lm", "fmri_lm.fmri_model", "fmri_lm.formula" ] }, { "page": "fmri_lm_control", "title": "Configure an fMRI linear model", "topics": [ "fmri_lm_control" ] }, { "page": "fmri_meta", "title": "Fit Group-Level Meta-Analysis", "topics": [ "fmri_meta" ] }, { "page": "fmri_meta_fit", "title": "Fit Meta-Analysis Models", "topics": [ "fmri_meta_fit" ] }, { "page": "fmri_meta_fit_contrasts", "title": "Fit Meta-Analysis Models with Exact Contrasts", "topics": [ "fmri_meta_fit_contrasts" ] }, { "page": "fmri_meta_fit_cov", "title": "Fit Meta-Analysis and return packed covariance per voxel", "topics": [ "fmri_meta_fit_cov" ] }, { "page": "fmri_meta_fit_extended", "title": "Extended Meta-Analysis Fit with Voxelwise Covariate", "topics": [ "fmri_meta_fit_extended" ] }, { "page": "fmri_model", "title": "Construct an fMRI Regression Model", "topics": [ "fmri_model" ] }, { "page": "fmri_ols_fit", "title": "OLS Fit with Optional Voxelwise Covariate", "topics": [ "fmri_ols_fit" ] }, { "page": "fmri_rlm", "title": "Fit a Robust Linear Model for fMRI Data Analysis", "topics": [ "fmri_rlm" ] }, { "page": "fmri_template", "title": "Define a subject-invariant model template", "topics": [ "fmri_template" ] }, { "page": "fmri_ttest", "title": "fmrireg t-tests for Group Analysis", "topics": [ "fmri_ttest" ] }, { "page": "fmrireg_cli", "title": "Run the fmrireg 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"latent_frame", "title": "Build an fmri_frame from a latent (basis) decomposition", "topics": [ "latent_frame" ] }, { "page": "list_benchmark_datasets", "title": "List Available Benchmark Datasets", "topics": [ "list_benchmark_datasets" ] }, { "page": "load_benchmark_dataset", "title": "Load fMRI Benchmark Datasets", "topics": [ "load_benchmark_dataset" ] }, { "page": "lowrank_control", "title": "Low-rank / sketch controls for fast GLM", "topics": [ "lowrank_control" ] }, { "page": "ma_parameters", "title": "Extract Estimated MA Parameters from fmri_lm Fit", "topics": [ "ma_parameters", "ma_parameters.fmri_lm" ] }, { "page": "matrix_frame", "title": "Build an fmri_frame from an in-memory matrix", "topics": [ "matrix_frame" ] }, { "page": "meta_effective_n", "title": "Compute Effective Sample Size for Meta-Analysis", "topics": [ "meta_effective_n" ] }, { "page": "meta_fit_vcov_cpp", "title": "Meta-regression with ONE voxelwise covariate", "topics": [ "meta_fit_vcov_cpp" ] }, { "page": "mixed_solve_cpp", "title": "Mixed Model Solver using Rcpp and roptim", "topics": [ "mixed_solve_cpp" ] }, { "page": "n_subjects", "title": "Extract Number of Subjects", "topics": [ "n_subjects" ] }, { "page": "neurovec_frame", "title": "Build an fmri_frame from in-memory NeuroVec objects", "topics": [ "neurovec_frame" ] }, { "page": "nifti_frame", "title": "Build a lazy fmri_frame from NIfTI files", "topics": [ "nifti_frame" ] }, { "page": "noise_spec", "title": "Temporal-noise specification", "topics": [ "noise_spec" ] }, { "page": "ols_t_cpp", "title": "OLS t-test / ANCOVA across features", "topics": [ "ols_t_cpp" ] }, { "page": "ols_t_vcov_cpp", "title": "OLS with ONE voxelwise covariate", "topics": [ "ols_t_vcov_cpp" ] }, { "page": "p_values", "title": "Extract P-values from a Model Fit", "concept": [ "statistical_measures" ], "topics": [ "p_values", "p_values.fmri_lm" ] }, { "page": "paired_diff_block", "title": "Helper Functions for fmri_ttest", "topics": [ "paired_diff_block" ] 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