Package index • bkstat

All functions

as_df.coxphw(): Convert coxphw::coxphw into a data.frame
as_df.rcorr(): Convert Hmisc::rcorr into a data.frame
as_df.rms(): Convert a generic rms object into a data.frame
as_df.validate(): Convert rms::validate into a data.frame
class_acc(): Classification accuracy and agreement estimates
cor_sparse(): Correlation of a sparse matrix
descript(): Descriptive statistics
dichotomize(): Dichotomize continuous variables
dxy2auc(): Convert Dxy to AUC
geometric_mean(): Geometric mean
landmark(): Build a landmark-analysis dataset
lmm_marginal_varcov(): Marginal variance-covariance matrix from an LMM
lmm_re_varcor(): Extract random-effects variances, standard deviation, and correlations from an LMM
logit(): Convert probabilities to logits
logit_inv(): Convert logits to probabilities
normalize() normalize_maxabs(): Normalize a numeric vector
oddspath(): Odds of Pathogenicity
plot_model(): Create a coefficient/model/forest plot
pmsampsize_r2(): Estimate Cox-Snell R2 using AUC
predict_glm(): Logistic Model Predictions
predict_surv_prob(): Predicted survival probability at a given time
predict_surv_time(): Predicted median survival time
print(<landmark>): Print a landmark-analysis dataset
pval(): Format p-values
rcv_glmnet(): Repeated Cross Validated Elastic Net Regression
reldif_means(): Relative/Percent Difference in Means
sd_within_ss(): Sample size for the within subject standard deviation
standardize(): Standardize a numeric vector
standardize_quantile(): Standardize a numeric vector using quantiles
stat_centroid(): Connect points to their centroid
surv_prob(): Get the survival probability at a given time
surv_time(): Get the survival time at a given probability of survival
svyglm_nested_test(): Design-based test for nested survey-weighted GLMs
walsh(): Walsh averages