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.

Estimation of the log Likelihood of the Saturated Model

When the values of the outcome variable Y are either 0 or 1, the function lsm() calculates the estimation of the log likelihood in the saturated model. This model is characterized by Llinas (2006, ISSN:2389-8976) in section 2.3 through the assumptions 1 and 2. The function LogLik() works (almost perfectly) when the number of independent variables K is high, but for small K it calculates wrong values in some cases. For this reason, when Y is dichotomous and the data are grouped in J populations, it is recommended to use the function lsm() because it works very well for all K.

Bayesian Inference for Log-Normal Data

Bayesian inference under log-normality assumption must be performed very carefully. In fact, under the common priors for the variance, useful quantities in the original data scale (like mean and quantiles) do not have posterior moments that are finite (Fabrizi et al. 2012 ). This package allows to easily carry out a proper Bayesian inferential procedure by fixing a suitable distribution (the generalized inverse Gaussian) as prior for the variance. Functions to estimate several kind of means (unconditional, conditional and conditional under a mixed model) and quantiles (unconditional and conditional) are provided.

Import and Handling for 'WhatsApp' Chat Logs

A straightforward, easy-to-use and robust parsing package which aims to digest history files from the popular messenger service 'WhatsApp' in all locales and from all devices.

Tools for Reading Formatted Access Log Files

R is used by a vast array of people for a vast array of purposes - including web analytics. This package contains functions for consuming and munging various common forms of request log, including the Common and Combined Web Log formats and various Amazon access logs.

Process the Apache Web Server Log Files

Provides capabilities to process Apache HTTPD Log files.The main functionalities are to extract data from access and error log files to data frames.

Profiling a Log-Likelihood to Calculate Confidence Intervals

Provides tools for profiling a user-supplied log-likelihood function to calculate confidence intervals for model parameters. Speed of computation can be improved by adjusting the step sizes in the profiling and/or starting the profiling from limits based on the approximate large sample normal distribution for the maximum likelihood estimator of a parameter. The accuracy of the limits can be set by the user. A plot method visualises the log-likelihood and confidence interval. Cases where the profile log-likelihood flattens above the value at which a confidence limit is defined can be handled, leading to a limit at plus or minus infinity. Disjoint confidence intervals will not be found.

Log-Concave Density Estimation in Arbitrary Dimensions

Software for computing a log-concave (maximum likelihood) estimator for independent and identically distributed data in any number of dimensions. For a detailed description of the method see Cule, Samworth and Stewart (2010, Journal of Royal Statistical Society Series B, ).