Vectorised Tools for URL Handling and Parsing

A toolkit for all URL-handling needs, including encoding and decoding, parsing, parameter extraction and modification. All functions are designed to be both fast and entirely vectorised. It is intended to be useful for people dealing with web-related datasets, such as server-side logs, although may be useful for other situations involving large sets of URLs.

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.

Tools for Analyzing 'ShinyProxy' Containers Logs

Provides functions to parse and analyze logs generated by 'ShinyProxy' containers. It extracts metadata from log file names, reads log contents, and computes summary statistics (such as the total number of lines and lines containing error messages), facilitating efficient monitoring and debugging of 'ShinyProxy' deployments.

Log-Analytic Methods for Multiplicative Effects

Log-analytic methods intended for testing multiplicative effects.

Weighted Tidy Log Odds Ratio

How can we measure how the usage or frequency of some feature, such as words, differs across some group or set, such as documents? One option is to use the log odds ratio, but the log odds ratio alone does not account for sampling variability; we haven't counted every feature the same number of times so how do we know which differences are meaningful? Enter the weighted log odds, which 'tidylo' provides an implementation for, using tidy data principles. In particular, here we use the method outlined in Monroe, Colaresi, and Quinn (2008) to weight the log odds ratio by a prior. By default, the prior is estimated from the data itself, an empirical Bayes approach, but an uninformative prior is also available.

One-Sample Log-Rank Test

The log-rank test is performed to assess the survival outcomes between two group. When there is no proper control group or obtaining such data is cumbersome, one sample log-rank test can be applied. This package performs one sample log-rank test as described in Finkelstein et al. (2003) and variation of the test for small sample sizes which is detailed in FD Liddell (1984) paper. Visualization function in the package generates Kaplan-Meier Curve comparing survival curve of the general population against that of the population of interest.

Log-Binomial Regression with Constrained Optimization

Maximum likelihood estimation of log-binomial regression with special functionality when the MLE is on the boundary of the parameter space.

Z(log) Transformation for Laboratory Measurements

Transformation of laboratory measurements into z or z(log)-value based on given or empirical reference limits as proposed in Hoffmann et al. 2017 .

Velocity and Accuracy of the LOg-RAnk TEst

The algorithm implemented in this package was designed to quickly estimates the distribution of the log-rank especially for heavy unbalanced groups. VALORATE estimates the null distribution and the p-value of the log-rank test based on a recent formulation. For a given number of alterations that define the size of survival groups, the estimation involves a weighted sum of distributions that are conditional on a co-occurrence term where mutations and events are both present. The estimation of conditional distributions is quite fast allowing the analysis of large datasets in few minutes < https://bioinformatics.mx/index.php/bioinfo-tools/>.

A Novel Automatic Shifted Log Transformation

A novel parametrization of log transformation and a shift parameter to automate the transformation process are proposed in R package 'AutoTransQF' based on Feng et al. (2016). Please read Feng et al. (2016) for more details of the method.