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Log File Analysis in International Large-Scale Assessments
Enables users to handle the dataset cleaning for conducting specific analyses with the log files from two international educational assessments: the Programme for International Student Assessment (PISA, < https://www.oecd.org/pisa/>) and the Programme for the International Assessment of Adult Competencies (PIAAC, < https://www.oecd.org/skills/piaac/>). An illustration of the analyses can be found on the LOGAN Shiny app (< https://loganpackage.shinyapps.io/shiny/>) on your browser.
Identify, Ping, and Log Internet Provider Connection Data
To assist you with troubleshooting internet connection issues and assist in isolating packet loss on your network. It does this by allowing you to retrieve the top trace route destinations your internet provider uses, and recursively ping each server in series while capturing the results and writing them to a log file. Each iteration it queries the destinations again, before shuffling the sequence of destinations to ensure the analysis is unbiased and consistent across each trace route.
Parsing, Anonymizing and Visualizing Exported 'WhatsApp' Chat Logs
Imports 'WhatsApp' chat logs and parses them into a usable dataframe object. The parser works on chats exported from Android or iOS phones and on Linux, macOS and Windows. The parser has multiple options for extracting smileys and emojis from the messages, extracting URLs and domains from the messages, extracting names and types of sent media files from the messages, extracting timestamps from messages, extracting and anonymizing author names from messages. Can be used to create anonymized versions of data.
Fitting log-Linear Models in Sparse Contingency Tables
Log-linear modeling is a popular method for the analysis of contingency table data. When the table is sparse, the data can fall on the boundary of the convex support, and we say that "the MLE does not exist" in the sense that some parameters cannot be estimated. However, an extended MLE always exists, and a subset of the original parameters will be estimable. The 'eMLEloglin' package determines which sampling zeros contribute to the non-existence of the MLE. These problematic zero cells can be removed from the contingency table and the model can then be fit (as far as is possible) using the glm() function.
Pseudo-Likelihood Estimation of Log-Multiplicative Association Models
Log-multiplicative association models (LMA) are
models for cross-classifications of categorical variables
where interactions are represented by products of category
scale values and an association parameter. Maximum
likelihood estimation (MLE) fails for moderate to large
numbers of categorical variables. The 'pleLMA' package
overcomes this limitation of MLE by using pseudo-likelihood
estimation to fit the models to small or large
cross-classifications dichotomous or multi-category variables.
Originally proposed by Besag (1974,
Implementing BTYD Models with the Log Sum Exp Patch
Functions for data preparation, parameter estimation, scoring, and plotting for the
BG/BB (Fader, Hardie, and Shang 2010
Configural Frequencies Analysis Using Log-Linear Modeling
Offers several functions for Configural Frequencies Analysis (CFA), which is a useful statistical tool for the analysis of multiway contingency tables. CFA was introduced by G. A. Lienert as 'Konfigurations Frequenz Analyse - KFA'. Lienert, G. A. (1971). Die Konfigurationsfrequenzanalyse: I. Ein neuer Weg zu Typen und Syndromen. Zeitschrift für Klinische Psychologie und Psychotherapie, 19(2), 99–115.
Transform Base Maps Using Log-Azimuthal Projection
Base maps are transformed to focus on a specific location using an azimuthal logarithmic distance transformation.
Read Spectral and Logged Data from Foreign Files
Functions for reading, and in some cases writing, foreign files
containing spectral data from spectrometers and their associated software,
output from daylight simulation models in common use, and some spectral
data repositories. As well as functions for exchange of spectral data with
other R packages. Part of the 'r4photobiology' suite,
Aphalo P. J. (2015)
Imports Log Files from Angstrom Engineering Thermal Evaporator
Opens and imports log files from Angstrom Engineering Thermal Evaporator and extracts basic characteristics, such as base pressure, time of the evaporation. It can visualize the deposition observables for review.