Various tools and data sets that support the study of kanji, including their morphology, decomposition and concepts of distance and similarity between them.

This package provides methods for selecting the optimal bandwidth in kernel density estimation for dependent samples, such as those generated by Markov chain Monte Carlo (MCMC). Implements a modified biased cross-validation (mBCV) approach that accounts for sample dependence, improving the accuracy of estimated density functions.

This package performs variable selection for many types of L1-regularised regressions using the revisited knockoffs procedure. This procedure uses a matrix of knockoffs of the covariates independent from the response variable Y. The idea is to determine if a covariate belongs to the model depending on whether it enters the model before or after its knockoff. The procedure suits for a wide range of regressions with various types of response variables. Regression models available are exported from the R packages glmnet and ordinalNet'. Based on the paper linked to via the URL below: Gegout A., Gueudin A., Karmann C. (2019) <arXiv:1907.03153>.

This package implements approaches of non-parametric smooth test to compare simultaneously K(K>1) copulas and non-parametric clustering of multivariate populations with arbitrary sizes. See Yves I. Ngounou Bakam and Denys Pommeret (2022) <arXiv:2112.05623> and Yves I. Ngounou Bakam and Denys Pommeret (2022) <arXiv:2211.06338>.

This package provides functions to implement K Nearest Neighbor forecasting using a weighted similarity metric tailored to the problem of forecasting univariate time series where recent observations, seasonal patterns, and exogenous predictors are all relevant in predicting future observations of the series in question. For more information on the formulation of this similarity metric please see Trupiano (2021) <arXiv:2112.06266>.

This package provides a collection of functions for analyzing data typically collected or used by behavioral scientists. Examples of the functions include a function that compares groups in a factorial experimental design, a function that conducts two-way analysis of variance (ANOVA), and a function that cleans a data set generated by Qualtrics surveys. Some of the functions will require installing additional package(s). Such packages and other references are cited within the section describing the relevant functions. Many functions in this package rely heavily on these two popular R packages: Dowle et al. (2021) <https://CRAN.R-project.org/package=data.table>. Wickham et al. (2021) <https://CRAN.R-project.org/package=ggplot2>.

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>.

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.

Rcpp implementation of the multivariate Kim filter, which combines the Kalman and Hamilton filters for state probability inference. The filter is designed for state space models and can handle missing values and exogenous data in the observation and state equations. Kim, Chang-Jin and Charles R. Nelson (1999) "State-Space Models with Regime Switching: Classical and Gibbs-Sampling Approaches with Applications" <doi:10.7551/mitpress/6444.001.0001><http://econ.korea.ac.kr/~cjkim/>.

Control your keyboard and mouse with R code by simulating key presses and mouse clicks. The input simulation is implemented with the Windows API.

Matches a data set with semi-structured address data, e.g., street and house number as a concatenated string, wrongly spelled street names or non-existing house numbers to a reference index. The methods are specifically designed for German municipalities ('KOR'-community) and German address schemes.

We developed a package Keyboard for designing single-agent, drug-combination, or phase I/II dose-finding clinical trials. The Keyboard designs are novel early phase trial designs that can be implemented simply and transparently, similar to the 3+3 design, but yield excellent performance, comparable to those of more-complicated, model-based designs (Yan F, Mandrekar SJ, Yuan Y (2017) <doi:10.1158/1078-0432.CCR-17-0220>, Li DH, Whitmore JB, Guo W, Ji Y. (2017) <doi:10.1158/1078-0432.CCR-16-1125>, Liu S, Johnson VE (2016) <doi:10.1093/biostatistics/kxv040>, Zhou Y, Lee JJ, Yuan Y (2019) <doi:10.1002/sim.8475>, Pan H, Lin R, Yuan Y (2020) <doi:10.1016/j.cct.2020.105972>). The Keyboard package provides tools for designing, conducting, and analyzing single-agent, drug-combination, and phase I/II dose-finding clinical trials. For more details about how to use this packge, please refer to Li C, Sun H, Cheng C, Tang L, and Pan H. (2022) "A software tool for both the maximum tolerated dose and the optimal biological dose finding trials in early phase designs". Manuscript submitted for publication.

Collection of utility functions used in the KEHRA project (see http://www.brunel.ac.uk/ife/britishcouncil). It refers to the multidimensional analysis of air pollution, weather and health data.

This package provides arrays with flexible control over dimension dropping when subscripting.

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>.

Application of a Known Biomass Production Model (KBPM): (1) the fitting of KBPM to each stock; (2) the estimation of the effects of environmental variability; (3) the retrospective analysis to identify regime shifts; (4) the estimation of forecasts. For more details see Schaefer (1954) <https://www.iattc.org/GetAttachment/62d510ee-13d0-40f2-847b-0fde415476b8/Vol-1-No-2-1954-SCHAEFER,-MILNER-B-_Some-aspects-of-the-dynamics-of-populations-important-to-the-management-of-the-commercial-marine-fisheries.pdf>, Pella and Tomlinson (1969) <https://www.iattc.org/GetAttachment/9865079c-6ee7-40e2-9e30-c4523ff81ddf/Vol-13-No-3-1969-PELLA,-JEROME-J-,-and-PATRICK-K-TOMLINSON_A-generalized-stock-production-model.pdf> and MacCall (2002) <doi:10.1577/1548-8675(2002)022%3C0272:UOKBPM%3E2.0.CO;2>.

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.

This package provides a function called COTUCKER3() (Co-Inertia Analysis + Tucker3 method) which performs a Co-Tucker3 analysis of two sequences of matrices, as well as other functions called PCA() (Principal Component Analysis) and BGA() (Between-Groups Analysis), which perform analysis of one matrix, COIA() (Co-Inertia Analysis), which performs analysis of two matrices, PTA() (Partial Triadic Analysis), STATIS(), STATISDUAL() and TUCKER3(), which perform analysis of a sequence of matrices, and BGCOIA() (Between-Groups Co-Inertia Analysis), STATICO() (STATIS method + Co-Inertia Analysis), COSTATIS() (Co-Inertia Analysis + STATIS method), which also perform analysis of two sequences of matrices.

The running statistics of interest is first extracted using a time window which is slid across the time series, and in each window, the running statistics value is computed. KCP (Kernel Change Point) detection proposed by Arlot et al. (2012) <arXiv:1202.3878> is then implemented to flag the change points on the running statistics (Cabrieto et al., 2018, <doi:10.1016/j.ins.2018.03.010>). Change points are located by minimizing a variance criterion based on the pairwise similarities between running statistics which are computed via the Gaussian kernel. KCP can locate change points for a given k number of change points. To determine the optimal k, the KCP permutation test is first carried out by comparing the variance of the running statistics extracted from the original data to that of permuted data. If this test is significant, then there is sufficient evidence for at least one change point in the data. Model selection is then used to determine the optimal k>0.

Analysis of DNA copy number in single cells using custom genome-wide targeted DNA sequencing panels for the Mission Bio Tapestri platform. Users can easily parse, manipulate, and visualize datasets produced from the automated Tapestri Pipeline', with support for normalization, clustering, and copy number calling. Functions are also available to deconvolute multiplexed samples by genotype and parsing barcoded reads from exogenous lentiviral constructs.

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.

Fast implementation of Nadaraya-Watson kernel regression for either univariate or multivariate responses, with one or more bandwidths. K-fold cross-validation is also performed.

One-way and two-way analysis of variance for replicated point patterns, grouped by one or two classification factors, on the basis of the corresponding K-functions.

Identification of putative causal variants in genome-wide association studies using hybrid analysis of both the trio and population designs. The package implements the method in the paper: Yang, Y., Wang, Q., Wang, C., Buxbaum, J., & Ionita-Laza, I. (2024). KnockoffHybrid: A knockoff framework for hybrid analysis of trio and population designs in genome-wide association studies. The American Journal of Human Genetics, in press.