person("Mario", "Frasca", email = "mariotomo@gmail.com", role = c("aut")),
person("Walerian", "Sokolowski", email = "r-logging@wlogsolutions.com", role = c("cre"))
Description: Pure R implementation of the ubiquitous log4j package. It offers hierarchic
loggers, multiple handlers per logger, level based filtering, space handling in messages

Description: logging is a pure R package that implements the ubiquitous

A very simple and easy-to-use set of suspiciously-familiar functions. 'loggit'
provides a set of wrappings for base R's message(), warning(), and stop()
functions that maintain identical functionality, but also log the handler
message to a 'JSON' log file. While mostly automatic, powerful custom logging
is available via these handlers' logging function, loggit(), which is also
exported for use. No change in existing code is necessary to use this
c(person("Ryan", "Price", email = "ryapric@gmail.com", role = c("cre", "aut")))
Imports: dplyr (>= 0.7.4), jsonlite (>= 1.5)
Suggests: knitr (>= 1.19), rmarkdown (>= 1.8), testthat (>= 2.0.0)

Title: Modern Logging for the R Ecosystem
An effortless 'ndjson' (newline-delimited 'JSON') logger, with two primary
log-writing interfaces. It provides a set of wrappings for base R's
message(), warning(), and stop() functions that maintain identical
functionality, but also log the handler message to an 'ndjson' log file.
'loggit' also exports its internal 'loggit()' function for powerful and
configurable custom logging. No change in existing code is necessary to use
this package, and should only require additions to fully leverage the power
of the logging system. 'loggit' also provides a log reader for reading an
'ndjson' log file into a data frame, log rotation, and live echo of the
'ndjson' log messages to terminal 'stdout' for log capture by external
systems (like containers). 'loggit' is ideal for Shiny apps, data pipelines,
modeling work flows, and more. Please see the vignettes for detailed example

Title: Local Group Graphical Lasso Estimation
Description: Provides a set of methods that learn time-varying graphical models based on data measured over a temporal grid. The underlying statistical model is motivated by the needs to describe and understand evolving interacting relationships among a set of random variables in many real applications, for instance the study of how stocks interact with each other and how such interactions change over time. The time-varying graphical models are estimated under the assumption that the graph topology changes gradually over time. For more details on estimating time-varying graphical models, please refer to: Yang, J. & Peng, J. (2018) <arXiv:1804.03811>.
Imports: Matrix (>= 1.2), doParallel (>= 1.0.8), foreach (>= 1.2.0),
Suggests: sparseMVN, matrixcalc, XML, RCurl, quantmod