Package index • plmmr

Fitting penalized linear mixed models (PLMMs - the featured presentation)

plmm(): Fit a linear mixed model via non-convex penalized maximum likelihood.

Loss and cross-validation for PLMMs

cv_plmm(): Cross-validation for plmm

plmm_loss(): Loss method for "plmm" class

Coefficient methods for PLMMs

coef(<cv_plmm>): Coef method for "cv_plmm" class

coef(<plmm>): Coef method for "plmm" class

Data (pre)processing and wrangling

create_design(): a function to create a design for PLMM modeling

find_example_data(): A function to help with accessing example PLINK files

process_plink(): Preprocess PLINK files using the bigsnpr package

process_delim(): A function to read in large data files as an FBM

relatedness_mat(): Calculate a relatedness matrix

unzip_example_data(): For Linux/Unix and MacOS only, here is a companion function to unzip the .gz files that ship with the plmmr package

Plotting, summarizing, and formatting

summary(<plmm>): A summary method for the plmm objects

summary(<cv_plmm>): A summary function for cv_plmm objects

plot(<plmm>): Plot method for plmm class

plot(<cv_plmm>): Plot method for cv_plmm class

print(<summary.cv_plmm>): Print method for summary.cv_plmm objects

print(<summary.plmm>): A function to print the summary of a plmm model

Prediction

predict(<plmm>): Predict method for plmm class

Data sets

admix: Admix: Semi-simulated SNP data