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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 < http://bioinformatica.mty.itesm.mx/valorate>.
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)
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
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.
Distributions for Generalized Additive Models for Location Scale and Shape
A set of distributions which can be used for modelling the response variables in Generalized Additive Models for Location Scale and Shape, Rigby and Stasinopoulos (2005),
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.