This package provides tools for maximum likelihood estimation of parameters of scientific models. Based on Goffe et al (1994) <doi:10.1016/0304-4076(94)90038-8>.

Estimation of Latent Order Logistic (LOLOG) Models for Networks. LOLOGs are a flexible and fully general class of statistical graph models. This package provides functions for performing MOM, GMM and variational inference. Visual diagnostics and goodness of fit metrics are provided. See Fellows (2018) <doi:10.48550/arXiv.1804.04583> for a detailed description of the methods.

Estimate the mean of a Gaussian vector, by choosing among a large collection of estimators, following the method developed by Y. Baraud, C. Giraud and S. Huet (2014) <doi:10.1214/13-AIHP539>. In particular it solves the problem of variable selection by choosing the best predictor among predictors emanating from different methods as lasso, elastic-net, adaptive lasso, pls, randomForest. Moreover, it can be applied for choosing the tuning parameter in a Gauss-lasso procedure.

This package provides a robust collection of functions tailored for microbial ecology analysis, encompassing both data analysis and visualization. It introduces an encapsulation feature that streamlines the process into a summary object. With the initial configuration of this summary object, users can execute a wide range of analyses with a single line of code, requiring only two essential parameters for setup. The package delivers comprehensive outputs including analysis objects, statistical outcomes, and visualization-ready data, enhancing the efficiency of research workflows. Designed with user-friendliness in mind, it caters to both novices and seasoned researchers, offering an intuitive interface coupled with adaptable customization options to meet diverse analytical needs.

Logger to keep track of informational events and errors useful for debugging.

Lag-sequential analysis is a method of assessing of patterns (what tends to follow what?) in sequences of codes. The codes are typically for discrete behaviors or states. The functions in this package read a stream of codes, or a frequency transition matrix, and produce a variety of lag sequential statistics, including transitional frequencies, expected transitional frequencies, transitional probabilities, z values, adjusted residuals, Yule's Q values, likelihood ratio tests of stationarity across time and homogeneity across groups or segments, transformed kappas for unidirectional dependence, bidirectional dependence, parallel and nonparallel dominance, and significance levels based on both parametric and randomization tests. The methods are described in Bakeman & Quera (2011) <doi:10.1017/CBO9781139017343>, O'Connor (1999) <doi:10.3758/BF03200753>, Wampold & Margolin (1982) <doi:10.1037/0033-2909.92.3.755>, and Wampold (1995, ISBN:0-89391-919-5).

This package provides functions for validating and normalizing bibliographic codes such as ISBN, ISSN, and LCCN. Also includes functions to communicate with the WorldCat API, translate Call numbers (Library of Congress and Dewey Decimal) to their subject classifications or subclassifications, and provides various loadable data files such call number / subject crosswalks and code tables.

Local Mean Decomposition is an iterative and self-adaptive approach for demodulating, processing, and analyzing multi-component amplitude modulated and frequency modulated signals. This R package is based on the approach suggested by Smith (2005) <doi:10.1098/rsif.2005.0058> and the Python library PyLMD'.

Processing of Landsat or other multispectral satellite imagery. Includes relative normalization, image-based radiometric correction, and topographic correction options. The original package description was published as Goslee (2011) <doi:10.18637/jss.v043.i04>, and details of the topographic corrections in Goslee (2012) <doi:10.14358/PERS.78.9.973>.

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.

Logic Forest is an ensemble machine learning method that identifies important and interpretable combinations of binary predictors using logic regression trees to model complex relationships with an outcome. Wolf, B.J., Slate, E.H., Hill, E.G. (2010) <doi:10.1093/bioinformatics/btq354>.

Tests whether the linear hypothesis of a model is correct specified using Dominguez-Lobato test. Also Ramsey's RESET (Regression Equation Specification Error Test) test is implemented and Wald tests can be carried out. Although RESET test is widely used to test the linear hypothesis of a model, Dominguez and Lobato (2019) proposed a novel approach that generalizes well known specification tests such as Ramsey's. This test relies on wild-bootstrap; this package implements this approach to be usable with any function that fits linear models and is compatible with the update() function such as stats'::lm(), lfe'::felm() and forecast'::Arima(), for ARMA (autoregressiveâ moving-average) models. Also the package can handle custom statistics such as Cramer von Mises and Kolmogorov Smirnov, described by the authors, and custom distributions such as Mammen (discrete and continuous) and Rademacher. Manuel A. Dominguez & Ignacio N. Lobato (2019) <doi:10.1080/07474938.2019.1687116>.

This package implements transfer learning methods for low-rank matrix estimation. These methods leverage similarity in the latent row and column spaces between the source and target populations to improve estimation in the target population. The methods include the LatEnt spAce-based tRaNsfer lEaRning (LEARNER) method and the direct projection LEARNER (D-LEARNER) method described by McGrath et al. (2024) <doi:10.48550/arXiv.2412.20605>.

This package provides flexible but lightweight logging facilities for R scripts. Supports priority levels for logs and messages, flagging messages, capturing script output, switching logs, and logging to files or connections.

Select statistically similar research groups by backward selection using various robust algorithms, including a heuristic based on linear discriminant analysis, multiple heuristics based on the test statistic, and parallelized exhaustive search.

This package provides tools to help storing and handling case line list data. The linelist class adds a tagging system to classical data.frame objects to identify key epidemiological data such as dates of symptom onset, epidemiological case definition, age, gender or disease outcome. Once tagged, these variables can be seamlessly used in downstream analyses, making data pipelines more robust and reliable.

Download Internet Protocol (IP) address location and more from the ip-api application programming interface (API) <https://ip-api.com/>. The package makes it easy to get the latitude, longitude, country, region, and organisation associated to the provided IP address. The information is conveniently returned in a rectangular format.

Transforms away factors with many levels prior to doing an OLS. Useful for estimating linear models with multiple group fixed effects, and for estimating linear models which uses factors with many levels as pure control variables. See Gaure (2013) <doi:10.1016/j.csda.2013.03.024> Includes support for instrumental variables, conditional F statistics for weak instruments, robust and multi-way clustered standard errors, as well as limited mobility bias correction (Gaure 2014 <doi:10.1002/sta4.68>). Since version 3.0, it provides dedicated functions to estimate Poisson models.

Implementation of the drift-diffusion mixed model for category learning as described in Paulon et al. (2021) <doi:10.1080/01621459.2020.1801448>.

Implementation of the methods described in Holzmann, Klar (2024) <doi: 10.1111/sjos.12733>. Lancaster correlation is a correlation coefficient which equals the absolute value of the Pearson correlation for the bivariate normal distribution, and is equal to or slightly less than the maximum correlation coefficient for a variety of bivariate distributions. Rank and moment-based estimators and corresponding confidence intervals are implemented, as well as independence tests based on these statistics.

This package provides Shiny gadgets to search, type, and insert IPA symbols into documents or scripts, requiring only knowledge about phonetics or X-SAMPA'. Also provides functions to facilitate the rendering of IPA symbols in LaTeX and PDF format, making IPA symbols properly rendered in all output formats. A minimal R Markdown template for authoring Linguistics related documents is also bundled with the package. Some helper functions to facilitate authoring with R Markdown is also provided.

Implementation of a theoretically supported alternative to k-nearest neighbors for functional data to solve problems of estimating unobserved segments of a partially observed functional data sample, functional classification and outlier detection. The approximating neighbor curves are piecewise functions built from a functional sample. Instead of a distance on a function space we use a locally defined distance function that satisfies stabilization criteria. The package allows the implementation of the methodology and the replication of the results in Elà as, A., Jiménez, R. and Yukich, J. (2020) <arXiv:2007.16059>.

Automatically install, update, and load CRAN', GitHub', and Bioconductor packages in a single function call. By accepting bare unquoted names for packages, it's easy to add or remove packages from the list.

Calculate mean statistics and leaf angle distribution type from measured leaf inclination angles. LAD distribution is fitted using a two-parameters (mu, nu) Beta distribution and compared with six theoretical LAD distributions. Additional information is provided in Chianucci and Cesaretti (2022) <doi:10.1101/2022.10.28.513998>.