LUCID with Multiple Omics Data

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Documentation for package ‘LUCIDus’ version 3.1.0

• DESCRIPTION file

Help Pages

analyze_missing_pattern	Analyze missing data patterns in detail
boot_lucid	Inference of LUCID model based on bootstrap resampling
check_and_stabilize_sigma	Check matrix condition and stabilize if needed
check_convergence	Check convergence with both absolute and relative criteria
check_imputation_quality	Check quality of imputed data
check_na	Check missing patterns in omics data
estimate_lucid	Fit LUCID models with one or multiple omics layers
fill_data	Impute missing data by optimizing the likelihood function
gen_ci	generate bootstrp ci (normal, basic and percentile)
Istep_Z	I-step of LUCID
lucid	Fit a lucid model for integrated analysis on exposure, outcome and multi-omics data, allowing for tuning
plot	Visualize LUCID model through a Sankey diagram
predict_lucid	Predict Cluster Assignment and Outcome From a Fitted LUCID Model
print.sumlucid_early	Print the output of LUCID in a nicer table
print.sumlucid_parallel	Print the output of LUCID in a nicer table
print.sumlucid_serial	Print the output of LUCID in a nicer table
safe_impute	Safe imputation for edge cases
safe_log_sum_exp	Safe log-sum-exp computation
safe_normalize	Safe probability normalization
safe_solve	Safe matrix inversion with stability checks
simulated_HELIX_data	A simulated HELIX dataset for LUCID
sim_data	A simulated dataset for LUCID
summarize_missing_stats	Summarize missing-data patterns from check_na output
summary.early_lucid	Summarize results of the early LUCID model
summary.lucid_parallel	Summarize results of the parallel LUCID model
summary.lucid_serial	Summarize results of the serial LUCID model
summary_lucid	Summarize results of the early LUCID model
tune_lucid	Wrapper for LUCID Model and Penalty Tuning