Release notes | Version 2.0.0 - Major Release | Release Overview | New Features | Enhancements

The data-set | Summary statistics from simple regression | Fine-mapping with susieR using summary statistics | Single-effect summary-statistics analysis without LD | Fine-mapping with susieR using LD matrix from reference panel | Session information

Recap: a "preventable" allele coding artifact | Example using real GWAS data and a 1000 Genomes European LD reference panel | Ordinary SuSiE-RSS | Kriging diagnostic | Finite LD reference panel correction | Empirical Bayes mismatch correction | Diagnostic metrics | Diagnostic with one effect | Summary | Caution: allele coding artifacts are outside this model | Session information | Reference

Example | References | Session information

LD information from the original genotype data | LD information from the reference panel | Session information

The data-set | Simple regression summary statistics | Fine-mapping with susieR | Credible sets | Posterior inclusion probabilities | Choice of priors | A note on covariate adjustment | Sufficient statistics: compute_suff_stat and susie_ss | Session information

Data | Baseline SuSiE fit | SuSiE with Servin-Stephens SER | References

Data | Summary Statistics and Z-Scores | Step 1: Standard SuSiE and False Positives | Step 2: Increasing Purity to Reduce False Positives | Step 3: Fitting SuSiE-inf | Step 4: SuSiE-ash Achieves the Middle Ground | Summary | What if we increase L for standard SuSiE? | Session Information

Define the mr.mash prior | Fit a mr.mash model to the data | Use the fitted mr.mash model to make predictions

Session information

Simulate data | Fit L0Learn | Build an initialization object | Run susieR with initialization | References

Set up environment | Overview | Simulate data | X in a dense form | X in a sparse form | Further step

Introduction | Examples

The scaled-t prior family | Overview of the implementation process | Step 1: Define the prior family class | Step 2: Implement the optimization function | (Optional) Include gradients in the optimization | Step 3: Implement the posterior summary function | Step 4: Put it all together | Step 5: Verify the EBNM function | Step 6: Use the new EBNM function to analyze a data set | Session information

The normal means model and empirical Bayes | An illustration: shrinkage estimation | Session information | References

Set up environment | Preparations | Constant variance example | Non-constant variance example | Estimating the variance function | Unevenly spaced data | Unevenly spaced data with constant variance | Unevenly spaced data with non-constant variance | Poisson-distributed data example | Constant variance | Non-constant variance | Simulation example from Fan & Yao (1998) | Treasury Bill example from Fan & Yao (1998) | Average replicate data | Heavisine example from Delouille et al (2004) | Motorcycle example from Delouille et al (2004) | Session info

The "wOBA" data set | The "ebnm" function | Comparing different priors | Reanalyzing the data using a nonparametric prior | Background on the "weighted on-base average" | Session information | References

The example data set | Initializating and fitting the GLM-PCA model | Session info

Environment set-up | Generate a small data set | Fit mixture model | Session information

Introduction | Dataset simulation | FLASH analysis | Finalize covariances | Fit mash model (estimate mixture proportions) | Compute posterior summaries

Goal | Outline | Simulate some data | Step 1: Read in the data | Step 2: Set up the covariance matrices | Step 3: fit the model | Step 4: Extract Posterior Summaries | Sharing | Measure of fit (log-likelihood) | Estimated mixture proportions | Metaplot | Session information.

Introduction | Method 1 | Method 2

Introduction | Analysis strategy outline | Example | Correlation structure | Data driven covariances | Fit mash model (estimate mixture proportions) | Compute posterior summaries

Introduction | Fit mash model with samples from the posteriors | Pairwise sharing

Introduction | Illustration | The right way | The wrong way

Session information

Introduction | Illustration

Goal | Outline | Data-driven covariances | Step 1: select strong signals | Step 2: Obtain initial data-driven covariance matrices | Step 3: Apply Extreme Deconvolution | Run mash | Session information.

Goal | Simple simulation