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.

Log-Linear Modelling of Triad Genotype Data

Triad Log-Linear modelling of Imprinting Environmental interactions, and Maternal effects (TriLLIEM). This is an implementation of the log-linear model described in a series of papers, see for example Ainsworth et al. (2010) .

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.

Tamper-Evident Audit Logging for Regulated Environments

Provides tamper-evident, hash-chained audit logging for analytical applications. Every log entry is linked via an 'SHA-256' hash to its predecessor, making insertions, deletions, and modifications detectable. Covers user attribution, timestamp integrity, mandatory reason capture, chain verification, structured export, and 'shiny' session instrumentation. For more details see < https://reprostats.org/regulog/>. Suitable for any context where accountability and traceability matter: regulated environments (21 CFR Part 11, EU Annex 11), internal tooling, data pipelines, and multi-user 'shiny' applications. Ships with optional qualification scripts (IQ, OQ, PQ) for use in validated computerised systems.

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, ).

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) , and data augmentation techniques Tanner and Wong (1987) .

Log Binomial Regression Model in Exact Method

Fit the log binomial regression model (LBM) by Exact method. Limited parameter space of LBM causes trouble to find admissible estimates and fail to converge when MLE is close to or on the boundary of space. Exact method utilizes the property of boundary vectors to re-parametrize the model without losing any information, and fits the model on the standard fitting algorithm with no convergence issues.

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) . More generally, CoDaCoRe can be applied to any regression problem where the independent variable is Compositional (CoDa), to derive a set of scale-invariant log-ratios (ILR or SLR) that are maximally associated to a dependent variable.

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) , and several association models: Goodman (1979) row-column association models of the RC(M) and RC(M)-L families with one or several dimensions; two skew-symmetric association models proposed by Yamaguchi (1990) and by van der Heijden & Mooijaart (1995) Functions allow computing the intrinsic association coefficient (see Bouchet-Valat (2022) ) and the Altham (1970) index , including via the Bayes shrinkage estimator proposed by Zhou (2015) ; and the RAS/IPF/Deming-Stephan algorithm.