This package implements a data language engine for incorporating data directly in rmarkdown documents so that they can be made completely standalone.

Kernel Fisher Discriminant Analysis (KFDA) is performed using Kernel Principal Component Analysis (KPCA) and Fisher Discriminant Analysis (FDA). There are some similar packages. First, lfda is a package that performs Local Fisher Discriminant Analysis (LFDA) and performs other functions. In particular, lfda seems to be impossible to test because it needs the label information of the data in the function argument. Also, the ks package has a limited dimension, which makes it difficult to analyze properly. This package is a simple and practical package for KFDA based on the paper of Yang, J., Jin, Z., Yang, J. Y., Zhang, D., and Frangi, A. F. (2004) <DOI:10.1016/j.patcog.2003.10.015>.

Smoothed bootstrap and functions for random generation from univariate and multivariate kernel densities. It does not estimate kernel densities.

An implementation of the k-means-- algorithm proposed by Chawla and Gionis, 2013 in their paper, "k-means-- : A unified approach to clustering and outlier detection. SIAM International Conference on Data Mining (SDM13)", <doi:10.1137/1.9781611972832.21> and using ordering described by Howe, 2013 in the thesis, Clustering and anomaly detection in tropical cyclones". Useful for creating (potentially) tighter clusters than standard k-means and simultaneously finding outliers inexpensively in multidimensional space.

Interface to Keras <https://keras.io>, a high-level neural networks API'. Keras was developed with a focus on enabling fast experimentation, supports both convolution based networks and recurrent networks (as well as combinations of the two), and runs seamlessly on both CPU and GPU devices.

Originally design to characterise Aqueous Two Phase Systems, LLSR provide a simple way to analyse experimental data and obtain phase diagram parameters, among other properties, systematically. The package will include (every other update) new functions in order to comprise useful tools in liquid-liquid extraction research.

It uses phenological and productivity-related variables derived from time series of vegetation indexes, such as the Normalized Difference Vegetation Index, to assess ecosystem dynamics and change, which eventually might drive to land degradation. The final result of the Land Productivity Dynamics indicator is a categorical map with 5 classes of land productivity dynamics, ranging from declining to increasing productivity. See www.sciencedirect.com/science/article/pii/S1470160X21010517/ for a description of the methods used in the package to calculate the indicator.

This package provides two methods of estimating income inequality statistics from binned income data, such as the income data provided in the Census. These methods use different interpolation techniques to infer the distribution of incomes within income bins. One method is an implementation of Jargowsky and Wheeler's mean-constrained integration over brackets (MCIB). The other method is based on a new technique, Lorenz interpolation, which estimates income inequality by constructing an interpolated Lorenz curve based on the binned income data. These methods can be used to estimate three income inequality measures: the Gini (the default measure returned), the Theil, and the Atkinson's index. Jargowsky and Wheeler (2018) <doi:10.1177/0081175018782579>.

Automatic detection of hyperlinks for packages and calls in the text of rmarkdown or quarto documents.

Provide sets of functions and methods to learn and practice data science using idea of algorithmic trading. Main goal is to process information within "Decision Support System" to come up with analysis or predictions. There are several utilities such as dynamic and adaptive risk management using reinforcement learning and even functions to generate predictions of price changes using pattern recognition deep regression learning. Summary of Methods used: Awesome H2O tutorials: <https://github.com/h2oai/awesome-h2o>, Market Type research of Van Tharp Institute: <https://vantharp.com/>, Reinforcement Learning R package: <https://CRAN.R-project.org/package=ReinforcementLearning>.

Four measures of linkage disequilibrium are provided: the usual r^2 measure, the r^2_S measure (r^2 corrected by the structure sample), the r^2_V (r^2 corrected by the relatedness of genotyped individuals), the r^2_VS measure (r^2 corrected by both the relatedness of genotyped individuals and the structure of the sample).

Maximum likelihood estimation and likelihood ratio test are essential for modern statistics. This package supports in calculating likelihood based inference. Reference: Pawitan Y. (2001, ISBN:0-19-850765-8).

Introduces in-sample, out-of-sample, pseudo out-of-sample, and benchmark model forecast tests and a new class for working with forecast data, Forecast.

This package provides a collection of colour palettes inspired by some of our dearest butterfly species. This package provides continuous and categorical palettes, including some colour blind friendly options.

Crabs in the English channel, deer skulls, English monarchs, half-caste Manga characters, Jamaican cities, Shakespeare's The Tempest, drugged up cyclists and sexually transmitted diseases.

Time-dependent Receiver Operating Characteristic curves, Area Under the Curve, and Net Reclassification Indexes for repeated measures. It is based on methods in Barbati and Farcomeni (2017) <doi:10.1007/s10260-017-0410-2>.

This package provides functions to prepare, visualize, and analyse diachronic network data on local political actors, with a particular focus on the development of local party systems and identification of actor groups. Formalizes and automates a continuity diagram method that has been previously applied in research on Czech local politics, e.g. Bubenicek and Kubalek (2010, ISSN:1803-8220), Kubalek and Bubenicek (2012, ISSN:1803-8220), and Cmejrek, Bubenicek, and Copik (2010, ISBN:978-80-247-3061-5). The package also includes several example datasets derived from Czech municipal elections, compiled from official election results, field research, and previously published case studies on Czech local politics.

"Learning with Subset Stacking" is a supervised learning algorithm that is based on training many local estimators on subsets of a given dataset, and then passing their predictions to a global estimator. You can find the details about LESS in our manuscript at <arXiv:2112.06251>.

This package implements the kK-NN algorithm, an adaptive k-nearest neighbor classifier that adjusts the neighborhood size based on local data curvature. The method estimates local Gaussian curvature by approximating the shape operator of the data manifold. This approach aims to improve classification performance, particularly in datasets with limited samples.

This package performs recursive partitioning of linear and nonlinear mixed effects models, specifically for longitudinal data. The package is an extension of the original longRPart package by Stewart and Abdolell (2013) <https://cran.r-project.org/package=longRPart>.

Prototypes for construction of a Gaussian Stochastic Process emulator (GASP) of a computer model. This is done within the objective Bayesian implementation of the GASP. The package allows for construction of a linked GASP of the composite computer model. Computational implementation follows the mathematical exposition given in publication: Ksenia N. Kyzyurova, James O. Berger, Robert L. Wolpert. Coupling computer models through linking their statistical emulators. SIAM/ASA Journal on Uncertainty Quantification, 6(3): 1151-1171, (2018).<DOI:10.1137/17M1157702>.

The Lorentz transform in special relativity; also the gyrogroup structure of three-velocities. Performs active and passive transforms and has the ability to use units in which the speed of light is not unity. Includes some experimental functionality for celerity and rapidity. For general relativity, see the schwarzschild package.

Allows researchers to conduct multivariate statistical analyses of survey data with list experiments. This survey methodology is also known as the item count technique or the unmatched count technique and is an alternative to the commonly used randomized response method. The package implements the methods developed by Imai (2011) <doi:10.1198/jasa.2011.ap10415>, Blair and Imai (2012) <doi:10.1093/pan/mpr048>, Blair, Imai, and Lyall (2013) <doi:10.1111/ajps.12086>, Imai, Park, and Greene (2014) <doi:10.1093/pan/mpu017>, Aronow, Coppock, Crawford, and Green (2015) <doi:10.1093/jssam/smu023>, Chou, Imai, and Rosenfeld (2017) <doi:10.1177/0049124117729711>, and Blair, Chou, and Imai (2018) <https://imai.fas.harvard.edu/research/files/listerror.pdf>. This includes a Bayesian MCMC implementation of regression for the standard and multiple sensitive item list experiment designs and a random effects setup, a Bayesian MCMC hierarchical regression model with up to three hierarchical groups, the combined list experiment and endorsement experiment regression model, a joint model of the list experiment that enables the analysis of the list experiment as a predictor in outcome regression models, a method for combining list experiments with direct questions, and methods for diagnosing and adjusting for response error. In addition, the package implements the statistical test that is designed to detect certain failures of list experiments, and a placebo test for the list experiment using data from direct questions.

Reproduces the harmonized DB of the ESTAT survey of the same name. The survey data is served as separate spreadsheets with noticeable differences in the collected attributes. The tool here presented carries out a series of instructions that harmonize the attributes in terms of name, meaning, and occurrence, while also introducing a series of new variables, instrumental to adding value to the product. Outputs include one harmonized table with all the years, and three separate geometries, corresponding to the theoretical point, the gps location where the measurement was made and the 250m east-facing transect.