A Fast and Lightweight Logging System for R, Based on 'log4j'

The log4r package is meant to provide a fast, lightweight, object-oriented approach to logging in R based on the widely-emulated 'log4j' system and etymology.

The 'plog' C++ Logging Library

A simple header-only logging library for C++. Add 'LinkingTo: plogr' to 'DESCRIPTION', and '#include ' in your C++ modules to use it.

Log Execution of Scripts

Logging of scripts suitable for clinical trials using 'Quarto' to create nice human readable logs. 'whirl' enables execution of scripts in batch, while simultaneously creating logs for the execution of each script, and providing an overview summary log of the entire batch execution.

Logging for 'dplyr' and 'tidyr' Functions

Provides feedback about 'dplyr' and 'tidyr' operations.

R Logging Package

Pure R implementation of the ubiquitous log4j package. It offers hierarchic loggers, multiple handlers per logger, level based filtering, space handling in messages and custom formatting.

Estimate a Log-Concave Probability Density from Iid Observations

Given independent and identically distributed observations X(1), ..., X(n), compute the maximum likelihood estimator (MLE) of a density as well as a smoothed version of it under the assumption that the density is log-concave, see Rufibach (2007) and Duembgen and Rufibach (2009). The main function of the package is 'logConDens' that allows computation of the log-concave MLE and its smoothed version. In addition, we provide functions to compute (1) the value of the density and distribution function estimates (MLE and smoothed) at a given point (2) the characterizing functions of the estimator, (3) to sample from the estimated distribution, (5) to compute a two-sample permutation test based on log-concave densities, (6) the ROC curve based on log-concave estimates within cases and controls, including confidence intervals for given values of false positive fractions (7) computation of a confidence interval for the value of the true density at a fixed point. Finally, three datasets that have been used to illustrate log-concave density estimation are made available.

Background Resource Logging

Intense parallel workloads can be difficult to monitor. Packages 'crew.cluster', 'clustermq', and 'future.batchtools' distribute hundreds of worker processes over multiple computers. If a worker process exhausts its available memory, it may terminate silently, leaving the underlying problem difficult to detect or troubleshoot. Using the 'autometric' package, a worker can proactively monitor itself in a detached background thread. The worker process itself runs normally, and the thread writes to a log every few seconds. If the worker terminates unexpectedly, 'autometric' can read and visualize the log file to reveal potential resource-related reasons for the crash. The 'autometric' package borrows heavily from the methods of packages 'ps' and 'psutil'.

A Simple, Opinionated Logging Utility

A very lightweight package that writes out log messages in an opinionated way. Simpler and lighter than other logging packages, 'rlog' provides a compact feature set that focuses on getting the job done in a Unix-like way.

Pretty Scientific Plotting with Minor-Tick and Log Minor-Tick Support

Functions to make useful (and pretty) plots for scientific plotting. Additional plotting features are added for base plotting, with particular emphasis on making attractive log axis plots.

Simulation and Estimation of Log-GARCH Models

Simulation and estimation of univariate and multivariate log-GARCH models. The main functions of the package are: lgarchSim(), mlgarchSim(), lgarch() and mlgarch(). The first two functions simulate from a univariate and a multivariate log-GARCH model, respectively, whereas the latter two estimate a univariate and multivariate log-GARCH model, respectively.