This package provides a set of functions to analyze and print the development of a commodity using the Point and Figure (P&F) approach. A P&F processor can be used to calculate daily statistics for the time series. These statistics can be used for deeper investigations as well as to create plots. Plots can be generated as well known X/O Plots in plain text format, and additionally in a more graphical format.

Estimate diagnosis performance (Sensitivity, Specificity, Positive predictive value, Negative predicted value) of a diagnostic test where can not measure the golden standard but can estimate it using the attributable fraction.

This package provides the source and examples for James P. Howard, II, "Computational Methods for Numerical Analysis with R," <https://jameshoward.us/cmna/>, a book on numerical methods in R.

This package performs least squares constrained optimization on a linear objective function. It contains a number of algorithms to choose from and offers a formula syntax similar to lm().

Fit logistic functions to observed dose-response continuous data and evaluate goodness-of-fit measures. See Malyutina A., Tang J., and Pessia A. (2023) <doi:10.18637/jss.v106.i04>.

This package contains data sets, examples and software from the Second Edition of "Design of Observational Studies"; see Rosenbaum, P.R. (2010) <doi:10.1007/978-1-4419-1213-8>.

Statistical and graphical tools for detecting and measuring discrimination and bias, be it racial, gender, age or other. Detection and remediation of bias in machine learning algorithms. Python interfaces available.

Bayesian networks with continuous and/or discrete variables can be learned and compared from data. The method is described in Boettcher and Dethlefsen (2003), <doi:10.18637/jss.v008.i20>.

Background correction of spectral like data. Handles variations in scaling, polynomial baselines, interferents, constituents and replicate variation. Parameters for corrections are stored for further analysis, and spectra are corrected accordingly.

This package provides tools for fluctuations analysis of mutant cells counts. Main reference is A. Mazoyer, R. Drouilhet, S. Despreaux and B. Ycart (2017) <doi:10.32614/RJ-2017-029>.

This package implements common geostatistical methods in a clean, straightforward, efficient manner. The methods are discussed in Schabenberger and Gotway (2004, <ISBN:9781584883227>) and Waller and Gotway (2004, <ISBN:9780471387718>).

This package implements assessment of benefit-risk balance using Bayesian Discrete Choice Experiment. For more details see the article by Mukhopadhyay et al. (2019) <DOI:10.1080/19466315.2018.1527248>.

Smoothing techniques and computing bandwidth selectors of the nth derivative of a probability density for one-dimensional data (described in Arsalane Chouaib Guidoum (2020) <arXiv:2012.06102> [stat.CO]).

Generates multivariate subgaussian stable probabilities using the QRSVN algorithm as detailed in Genz and Bretz (2002) <DOI:10.1198/106186002394> but by sampling positive stable variates not chi/sqrt(nu).

Analyzing data under multivariate mixed effects model using multivariate REML and multivariate Henderson3 methods. See Meyer (1985) <doi:10.2307/2530651> and Wesolowska Janczarek (1984) <doi:10.1002/bimj.4710260613>.

This package implements orbit counting using a fast combinatorial approach. Counts orbits of nodes and edges from edge matrix or data frame, or a graph object from the graph package.

This package provides functions to generate ensembles of generalized linear models using a greedy projected subset gradient descent algorithm. The sparsity and diversity tuning parameters are selected by cross-validation.

Transforms datetime data into a format ready for analysis. It offers two core functionalities; aggregating data to a higher level interval (thicken) and imputing records where observations were absent (pad).

This function produces both the numerical and graphical summaries of the QTL hotspot detection in the genomes that are available on the worldwide web including the flanking markers of QTLs.

This package provides a set of consistent, opinionated functions to quickly check function arguments, coerce them to the desired configuration, or deliver informative error messages when that is not possible.

This package provides tools to help developers and producers manipulate R objects and outputs. It includes tools for displaying results and objects, and for formatting them in the correct format.

This package provides functions for statistical analysis, modeling and simulation of time series with state space model, based on the methodology in Kitagawa (2020, ISBN: 978-0-367-18733-0).

Uniform manifold approximation and projection is a technique for dimension reduction. This package provides an interface to the UMAP algorithm in R, including a translation of the original algorithm into R.

This package provides functions to handle basic input output. These functions always read and write UTF-8 (8-bit Unicode Transformation Format) files and provide more explicit control over line endings.