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Bayesian Mixture Log-Normal Survival Model
Bayesian Survival models via the mixture of Log-Normal distribution extends the well-known survival models and accommodates different behaviour over time and considers higher censored survival times. The proposal combines mixture distributions Fruhwirth-Schnatter(2006)
Learning Sparse Log-Ratios for Compositional Data
In the context of high-throughput genetic data,
CoDaCoRe identifies a set of sparse biomarkers that are
predictive of a response variable of interest (Gordon-Rodriguez
et al., 2021)
Log-Multiplicative Models, Including Association Models
Functions to fit log-multiplicative models using 'gnm', with
support for convenient printing, plots, and jackknife/bootstrap
standard errors. For complex survey data, models can be fitted from
design objects from the 'survey' package. Currently supported models
include UNIDIFF (Erikson & Goldthorpe, 1992),
a.k.a. log-multiplicative layer effect model (Xie, 1992)
Compare Models with Cross-Validated Log-Likelihood
An implementation of the cross-validated difference in means (CVDM) test by Desmarais and Harden (2014)
Send Log Messages to Remote 'syslog' Server
Send 'syslog' protocol messages to a remote 'syslog' server specified by host name and TCP network port.
Utilities from 'Seminar fuer Statistik' ETH Zurich
Useful utilities ['goodies'] from Seminar fuer Statistik ETH Zurich, some of which were ported from S-plus in the 1990s. For graphics, have pretty (Log-scale) axes eaxis(), an enhanced Tukey-Anscombe plot, combining histogram and boxplot, 2d-residual plots, a 'tachoPlot()', pretty arrows, etc. For robustness, have a robust F test and robust range(). For system support, notably on Linux, provides 'Sys.*()' functions with more access to system and CPU information. Finally, miscellaneous utilities such as simple efficient prime numbers, integer codes, Duplicated(), toLatex.numeric() and is.whole().
Fitting Semi-Parametric log-Symmetric Regression Models
Set of tools to fit a semi-parametric regression model suitable for analysis of data sets in which the response variable is continuous, strictly positive, asymmetric and possibly, censored. Under this setup, both the median and the skewness of the response variable distribution are explicitly modeled by using semi-parametric functions, whose non-parametric components may be approximated by natural cubic splines or P-splines. Supported distributions for the model error include log-normal, log-Student-t, log-power-exponential, log-hyperbolic, log-contaminated-normal, log-slash, Birnbaum-Saunders and Birnbaum-Saunders-t distributions.
Produces Markdown Log File with a Built-in Function Call
Produces clean and neat Markdown log file and also provide an argument to include the function call inside the Markdown log.
Analyze Download Logs from the CRAN RStudio Mirror
Analyze download logs from the CRAN RStudio mirror (< http://cran.rstudio.com/>). This CRAN mirror is the default one used in RStudio. The available data is the result of parsed and anonymised raw log data from that CRAN mirror.
Dynamic Logging for R Inspired by Configuration Driven Development
A comprehensive and dynamic configuration driven logging package for R. While there are several excellent logging solutions already in the R ecosystem, I always feel constrained in some way by each of them. Every project is designed differently to solve it's domain specific problem, and ultimately the utility of a logging solution is its ability to adapt to this design. This is the raison d'ĂȘtre for 'dyn.log': to provide a modular design, template mechanics and a configuration-based integration model, so that the logger can integrate deeply into your design, even though it knows nothing about it.