Package index • plmmr

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

plmm(): Fit a linear mixed model via 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(<plmm>): Coef method for plmm class
coef(<cv_plmm>): Coef method for cv_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 a filebacked big.matrix
relatedness_mat(): Calculate a relatedness matrix
unzip_example_data(): Companion function to unzip the .gz files that ship with the plmmr package.

Plotting, summarizing, and formatting

summary(<plmm>): A summary method for 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.plmm>): A function to print the summary of a plmm model
print(<summary.cv_plmm>): Print method for summary.cv_plmm objects

Prediction

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

Data sets

admix: Admix: Semi-simulated SNP data