Download Logs from the 'RStudio' 'CRAN' Mirror

'API' to the database of 'CRAN' package downloads from the 'RStudio' 'CRAN mirror'. The database itself is at < http://cranlogs.r-pkg.org>, see < https://github.com/r-hub/cranlogs.app> for the raw 'API'.

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.

Logging Setup for the 'teal' Family of Packages

Utilizing the 'logger' framework to record events within a package, specific to 'teal' family of packages. Supports logging namespaces, hierarchical logging, various log destinations, vectorization, and more.

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.

R and C++ Interfaces to 'spdlog' C++ Header Library for Logging

The mature and widely-used C++ logging library 'spdlog' by Gabi Melman provides many desirable features. This package bundles these header files for easy use by R packages from both their R and C or C++ code. Explicit use via 'LinkingTo:' is also supported. Also see the 'spdl' package which enhanced this package with a consistent R and C++ interface.

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'.

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.

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.

Logging for 'dplyr' and 'tidyr' Functions

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

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.