Conditional Samplings for Max-Linear Models
Computational tools for conditional samplings from
max-linear models.

Estimates a linear combination of predictors by maximizing a smooth approximation to the estimated true positive rate (TPR; sensitivity) while constraining a smooth approximation to the estimated false positive rate (FPR; 1-specificity) at a user-specified level.

Fits a linear combination of predictors by maximizing a smooth approximation to the estimated covariate-adjusted area under the receiver operating characteristic curve (AUC) for a discrete covariate. (Meisner, A, Parikh, CR, and Kerr, KF (2017) <http://biostats.bepress.com/uwbiostat/paper421/>.)

The Group Sequential Max-Combo Test for Comparing Survival
Curves
Functions for comparing survival curves using the max-combo test at a single timepoint or repeatedly at successive respective timepoints while controlling type I error (i.e., the group sequential setting), as published by Prior (2020) <doi:10.1177/0962280220931560>.  The max-combo test is a generalization of the weighted log-rank test, which itself is a generalization of the log-rank test, which is a commonly used statistical test for comparing survival curves, e.g., during or after a clinical trial as part of an effort to determine if a new drug or therapy is more effective at delaying undesirable outcomes than an established drug or therapy or a placebo.
magrittr, purrr, rlang, mstate, MCMCpack, dplyr, mvtnorm,
survival

R (>= 2.12.0), methods, Rcpp, SparseM, tm
maxent is an R package with tools for text classification
using multinomial logistic regression, also known as maximum
entropy. The focus of this maximum entropy classifier is to
minimize memory consumption on very large datasets,
particularly sparse document-term matrices represented by the
tm package.

Text Classification Using Low-memory Multinomial Logistic
Regression
R (>= 2.13.0), methods, Rcpp, SparseM, tm

Low-memory Multinomial Logistic Regression with Support for Text
Classification
maxent is an R package with tools for low-memory multinomial logistic regression, also known as maximum entropy. The focus of this maximum entropy classifier is to minimize memory consumption on very large datasets, particularly sparse document-term matrices represented by the tm package.