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, ), pseudo-likelihood estimation takes large complex models and breaks it down into smaller ones. Rather than maximizing the likelihood of the joint distribution of all the variables, a pseudo-likelihood function, which is the product likelihoods from conditional distributions, is maximized. LMA models can be derived from a number of different frameworks including (but not limited to) graphical models and uni-dimensional and multi-dimensional item response theory models. More details about the models and estimation can be found in the vignette.

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.

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 ), BG/NBD (Fader, Hardie, and Lee 2005 ) and Pareto/NBD and Gamma/Gamma (Fader, Hardie, and Lee 2005 ) models.

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) .

Transform Base Maps Using Log-Azimuthal Projection

Base maps are transformed to focus on a specific location using an azimuthal logarithmic distance transformation.

Tidy Import, Indexing, and Export of LAS Well Log Data

Provides tools for reading, parsing, indexing, and exporting LAS (Log ASCII Standard) well log files into tidy, analysis-ready tabular formats. The package separates LAS header information and log data into structured components, builds a searchable index across collections of LAS files, and enables reproducible subsetting of wells based on metadata or curve availability. Output tables can be written to CSV or Parquet formats to support large-scale statistical, machine learning, and earth science workflows. The tidy data structure follows Wickham (2014) . The LAS file structure follows the Canadian Well Logging Society LAS standard < https://www.cwls.org/wp-content/uploads/2017/02/Las2_Update_Jan2017.pdf>.

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.

Application Directories: Determine Where to Save Data, Caches, and Logs

An easy way to determine which directories on the users computer you should use to save data, caches and logs. A port of Python's 'Appdirs' (< https://github.com/ActiveState/appdirs>) to R.

Computing Log-Transformed Kernel Density Estimates for Positive Data

Computes log-transformed kernel density estimates for positive data using a variety of kernels. It follows the methods described in Jones, Nguyen and McLachlan (2018) .