This package provides the ability to create dynamic citations in which the bibliographic information is pulled from the web rather than having to be entered into a local database such as bibtex ahead of time. The package is primarily aimed at authoring in the R markdown format, and can provide outputs for web-based authoring such as linked text for inline citations. Cite using a DOI', URL, or bibtex file key. See the package URL for details.

This package provides an efficient implementation of univariate local polynomial kernel density estimators that can handle bounded and discrete data. See Geenens (2014) <doi:10.48550/arXiv.1303.4121>, Geenens and Wang (2018) <doi:10.48550/arXiv.1602.04862>, Nagler (2018a) <doi:10.48550/arXiv.1704.07457>, Nagler (2018b) <doi:10.48550/arXiv.1705.05431>.

This package provides a unified software package simultaneously implemented in Python', R', and Matlab providing a uniform and internally-consistent way of calculating stoichiometric equilibrium constants in modern and palaeo seawater as a function of temperature, salinity, pressure and the concentration of magnesium, calcium, sulphate, and fluorine.

Miscellaneous functions for classification and visualization, e.g. regularized discriminant analysis, sknn() kernel-density naive Bayes, an interface to svmlight and stepclass() wrapper variable selection for supervised classification, partimat() visualization of classification rules and shardsplot() of cluster results as well as kmodes() clustering for categorical data, corclust() variable clustering, variable extraction from different variable clustering models and weight of evidence preprocessing.

This package provides a collection of personal helper functions to avoid redundancy in the spirit of the "Don't repeat yourself" principle of software development (<https://en.wikipedia.org/wiki/Don%27t_repeat_yourself>).

This is designed for use with an arbitrary set of equations with an arbitrary set of unknowns. The user selects "fixed" values for enough unknowns to leave as many variables as there are equations, which in most cases means the system is properly defined and a unique solution exists. The function, the fixed values and initial values for the remaining unknowns are fed to a nonlinear backsolver. The original version of "TK!Solver" , now a product of Universal Technical Systems (<https://www.uts.com>) was the inspiration for this function.

This package provides a function that uses a genetic algorithm to search for a subset of size k from the integers 1:n, such that a user-supplied objective function is minimized at that subset. The selection step is done by tournament selection based on ranks, and elitism may be used to retain a portion of the best solutions from one generation to the next. Population objective function values may optionally be evaluated in parallel.

Kernel Machine Score Test for Pathway Analysis in the Presence of Semi-Competing Risks. Method is detailed in: Neykov, Hejblum & Sinnott (2018) <doi: 10.1177/0962280216653427>.

Selection of k in k-means clustering based on Pham et al. paper ``Selection of k in k-means clustering''.

This package provides tools for working with the Korea Standard Industrial Classification (KSIC). Includes datasets for the 9th, 10th, and 11th revisions. Functions include searching codes and names by keyword, converting codes across revisions, validating KSIC codes, and navigating the classification hierarchy (e.g., identifying parent or child categories). Intended for use in statistical analysis, data processing, and research involving South Koreaâ s industrial classification system.

Convert latex math expressions to HTML and MathML for use in markdown documents or package manual pages. The rendering is done in R using the V8 engine (i.e. server-side), which eliminates the need for embedding the MathJax library into your web pages. In addition a math-to-rd wrapper is provided to automatically render beautiful math in R documentation files.

Simulating species migration and range dynamics under stable or changing environmental conditions based on a simple, raster-based, deterministic or stochastic migration model. KISSMig runs on binary or quantitative suitability maps, which are pre-calculated with niche-based habitat suitability models (also called ecological niche models (ENMs) or species distribution models (SDMs)). Nobis & Normand (2014), <doi:10.1111/ecog.00930>.

Two main functionalities are provided. One of them is predicting values with k-nearest neighbors algorithm and the other is optimizing the parameters k and d of the algorithm. These are carried out in parallel using multiple threads.

Kendall random walks are a continuous-space Markov chains generated by the Kendall generalized convolution. This package provides tools for simulating these random walks and studying distributions related to them. For more information about Kendall random walks see Jasiulis-GoÅ dyn (2014) <arXiv:1412.0220>.

Estimates kriging models for geographical point-referenced data. Method is described in Gill (2020) <doi:10.1177/1532440020930197>.

Demo and dataset accompaying the books : De l'analyse des réseaux expérimentaux à la méta-analyse: Méthodes et applications avec le logiciel R pour les sciences agronomiques et environnementales (Published 2018-06-28, Quae, for french version) by David Makowski, Francois Piraux and Francois Brun - <https://www.quae.com/produit/1514/9782759228164/de-l-analyse-des-reseaux-experimentaux-a-la-meta-analyse> Knowledge Synthesis in Agriculture : from Experimental Network to Meta-Analysis (in preparation for 2018-06, Springer , for English version) by David Makowski, Francois Piraux and Francois Brun A full description of all the material is in both books. ACKNOWLEDGMENTS : The French network "RMT modeling and data analysis for agriculture" (<http://www.modelia.org>) have contributed to the development of this R package. This project and network are lead by ACTA (French Technical Institute for Agriculture) and was funded by a grant from the Ministry of Agriculture and Fishing of France.

Package implements Kernel-based Regularized Least Squares (KRLS), a machine learning method to fit multidimensional functions y=f(x) for regression and classification problems without relying on linearity or additivity assumptions. KRLS finds the best fitting function by minimizing the squared loss of a Tikhonov regularization problem, using Gaussian kernels as radial basis functions. For further details see Hainmueller and Hazlett (2014).

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.

This package provides tools to calculate the theoretical hydrodynamic response of an aquifer undergoing harmonic straining or pressurization, or analyze measured responses. There are two classes of models here, designed for use with confined aquifers: (1) for sealed wells, based on the model of Kitagawa et al (2011, <doi:10.1029/2010JB007794>), and (2) for open wells, based on the models of Cooper et al (1965, <doi:10.1029/JZ070i016p03915>), Hsieh et al (1987, <doi:10.1029/WR023i010p01824>), Rojstaczer (1988, <doi:10.1029/JB093iB11p13619>), Liu et al (1989, <doi:10.1029/JB094iB07p09453>), and Wang et al (2018, <doi:10.1029/2018WR022793>). Wang's solution is a special exception which allows for leakage out of the aquifer (semi-confined); it is equivalent to Hsieh's model when there is no leakage (the confined case). These models treat strain (or aquifer head) as an input to the physical system, and fluid-pressure (or water height) as the output. The applicable frequency band of these models is characteristic of seismic waves, atmospheric pressure fluctuations, and solid earth tides.

Search and download data from the API for Japanese Diet Proceedings (see the reference at <https://kokkai.ndl.go.jp/api.html>).

Restore underlining numeric data from rating history graph of KGS (an online platform of the game of go, <http://www.gokgs.com/>). A shiny application is also provided.

Rank-based tests for enrichment of KOG (euKaryotic Orthologous Groups) classes with up- or down-regulated genes based on a continuous measure. The meta-analysis is based on correlation of KOG delta-ranks across datasets (delta-rank is the difference between mean rank of genes belonging to a KOG class and mean rank of all other genes). With binary measure (1 or 0 to indicate significant and non-significant genes), one-tailed Fisher's exact test for over-representation of each KOG class among significant genes will be performed.

This package contains basic tools for sample size estimation in studies of interobserver/interrater agreement (reliability). Includes functions for both the power-based and confidence interval-based methods, with binary or multinomial outcomes and two through six raters.

Implementation for Kendall functional principal component analysis. Kendall functional principal component analysis is a robust functional principal component analysis technique for non-Gaussian functional/longitudinal data. The crucial function of this package is KFPCA() and KFPCA_reg(). Moreover, least square estimates of functional principal component scores are also provided. Refer to Rou Zhong, Shishi Liu, Haocheng Li, Jingxiao Zhang. (2021) <arXiv:2102.01286>. Rou Zhong, Shishi Liu, Haocheng Li, Jingxiao Zhang. (2021) <doi:10.1016/j.jmva.2021.104864>.