Allows analyzing time series representing two-dimensional movements. It accepts a data frame with a time (t), horizontal (x) and vertical (y) coordinate as columns, and returns several dynamical properties such as speed, acceleration or curvature.

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>.

Sequences encoding by using the chaos game representation. Löchel et al. (2019) <doi:10.1093/bioinformatics/btz493>.

Write beautiful yet customizable letters in R Markdown and directly obtain the finished PDF. Smooth generation of PDFs is realized by rmarkdown', the pandoc-letter template and the KOMA-Script letter class. KOMA-Script provides enhanced replacements for the standard LaTeX classes with emphasis on typography and versatility. KOMA-Script is particularly useful for international writers as it handles various paper formats well, provides layouts for many common window envelope types (e.g. German, US, French, Japanese) and lets you define your own layouts. The package comes with a default letter layout based on DIN 5008B'.

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.

Knowledge space theory by Doignon and Falmagne (1999) <doi:10.1007/978-3-642-58625-5> is a set- and order-theoretical framework, which proposes mathematical formalisms to operationalize knowledge structures in a particular domain. The kstMatrix package provides basic functionalities to generate, handle, and manipulate knowledge structures and knowledge spaces. Opposed to the kst package, kstMatrix uses matrix representations for knowledge structures. Furthermore, kstMatrix contains several knowledge spaces obtained in the 1990s by the research group around Cornelia Dowling through querying experts.

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>.

Predicts any variable in any categorical dataset for given values of predictor variables. If a dataset contains 4 variables, then any variable can be predicted based on the values of the other three variables given by the user. The user can upload their own datasets and select what variable they want to predict. A handsontable is provided to enter the predictor values and also accuracy of the prediction is also shown.

This package implements the Clock of Regimes (KRONX) framework for regime-switching fragility analysis of financial time series. The package fits Gaussian and Student-t Hidden Markov Models (HMMs) to return data, constructs a hazard-adjusted transition operator Q, derives the associated generator K = Q - I, and computes the fundamental matrix N = -K inverse to characterize expected residence times under structural fragility.

This package provides a new practical method to evaluate whether relationships between two sets of high-dimensional variables are different or not across two conditions. Song, H. and Wu, M.C. (2023) <arXiv:2307.15268>.

Distance metrics for mixed-type data consisting of continuous, nominal, and ordinal variables. This methodology uses additive and product kernels to calculate similarity functions and metrics, and selects variables relevant to the underlying distance through bandwidth selection via maximum similarity cross-validation. These methods can be used in any distance-based algorithm, such as distance-based clustering. For further details, we refer the reader to Ghashti and Thompson (2024) <doi:10.1007/s00357-024-09493-z> for dkps() methodology, and Ghashti (2024) <doi:10.14288/1.0443975> for dkss() methodology.

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 provides a higher-level interface to the torch package for defining, training, and fine-tuning neural networks through code generation. The package supports several architectures, including feedforward (multi-layer perceptron) and recurrent neural networks (RNN, LSTM, GRU), while reducing boilerplate torch code. Model training methods also bridge to machine learning frameworks in R, particularly the tidymodels ecosystem, including parsnip model specifications, workflows, recipes, and tuning tools.

Identification of putative causal variants in genome-wide association studies with trio and duo families. The package calculates the W feature statistics from KnockoffTrio and p-values from the family-based association test (FBAT) using trio and/or duo data. Compared to previous versions, a significant improvement has been made in Version 1.1.0 to allow the package to be applied not only to trio families but also to duo families. The package implements the methods in the paper: "Yang, Y., Wang, C., Liu, L., Buxbaum, J., He, Z., & Ionita-Laza, I. (2022). KnockoffTrio: A knockoff framework for the identification of putative causal variants in genome-wide association studies with trio design. The American Journal of Human Genetics, 109(10), 1761-1776.".

Metadata about populations and data about samples from the 1000 Genomes Project, including the 2,504 samples sequenced for the Phase 3 release and the expanded collection of 3,202 samples with 602 additional trios. The data is described in Auton et al. (2015) <doi:10.1038/nature15393> and Byrska-Bishop et al. (2022) <doi:10.1016/j.cell.2022.08.004>, and raw data is available at <http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/>. See Turner (2022) <doi:10.48550/arXiv.2210.00539> for more details.

Solves kernel ridge regression, within the the mixed model framework, for the linear, polynomial, Gaussian, Laplacian and ANOVA kernels. The model components (i.e. fixed and random effects) and variance parameters are estimated using the expectation-maximization (EM) algorithm. All the estimated components and parameters, e.g. BLUP of dual variables and BLUP of random predictor effects for the linear kernel (also known as RR-BLUP), are available. The kernel ridge mixed model (KRMM) is described in Jacquin L, Cao T-V and Ahmadi N (2016) A Unified and Comprehensible View of Parametric and Kernel Methods for Genomic Prediction with Application to Rice. Front. Genet. 7:145. <doi:10.3389/fgene.2016.00145>.

This package provides a high-performance R interface to the kuzu graph database. It uses the reticulate package to wrap the official Python client ('kuzu', pandas', and networkx'), allowing users to interact with kuzu seamlessly from within R'. Key features include managing database connections, executing Cypher queries, and efficiently loading data from R data frames. It also provides seamless integration with the R ecosystem by converting query results directly into popular R data structures, including tibble', igraph', tidygraph', and g6R objects, making kuzu's powerful graph computation capabilities readily available for data analysis and visualization workflows in R'. The kuzu documentation can be found at <https://kuzudb.github.io/docs/>.

The K-sample omnibus non-proportional hazards (KONP) tests are powerful non-parametric tests for comparing K (>=2) hazard functions based on right-censored data (Gorfine, Schlesinger and Hsu, 2020, <doi:10.1177/0962280220907355>). These tests are consistent against any differences between the hazard functions of the groups. The KONP tests are often more powerful than other existing tests, especially under non-proportional hazard functions.

Handles univariate non-parametric density estimation with parametric starts and asymmetric kernels in a simple and flexible way. Kernel density estimation with parametric starts involves fitting a parametric density to the data before making a correction with kernel density estimation, see Hjort & Glad (1995) <doi:10.1214/aos/1176324627>. Asymmetric kernels make kernel density estimation more efficient on bounded intervals such as (0, 1) and the positive half-line. Supported asymmetric kernels are the gamma kernel of Chen (2000) <doi:10.1023/A:1004165218295>, the beta kernel of Chen (1999) <doi:10.1016/S0167-9473(99)00010-9>, and the copula kernel of Jones & Henderson (2007) <doi:10.1093/biomet/asm068>. User-supplied kernels, parametric starts, and bandwidths are supported.

Implementation of Kmeans clustering algorithm and a supervised KNN (K Nearest Neighbors) learning method. It allows users to perform unsupervised clustering and supervised classification on their datasets. Additional features include data normalization, imputation of missing values, and the choice of distance metric. The package also provides functions to determine the optimal number of clusters for Kmeans and the best k-value for KNN: knn_Function(), find_Knn_best_k(), KMEANS_FUNCTION(), and find_Kmeans_best_k().

New kernel-based test and fast tests for testing whether two samples are from the same distribution. They work well particularly for high-dimensional data. Song, H. and Chen, H. (2023) <arXiv:2011.06127>.

Maximum likelihood estimation of component lifetime parameters from system-level observations of k-out-of-n systems. Supports exponential and Weibull component distributions under multiple observation schemes: Scheme 0 (system lifetime only), Scheme 1 (periodic inspection), and Scheme 2 (complete monitoring). Provides an EM algorithm for Weibull parallel systems and Fisher information comparison across schemes. The k-out-of-n framework unifies series (k=1) and parallel (k=m) systems as a censoring problem on component lifetimes. Conforms to the likelihood.model generics and returns fitted objects compatible with algebraic.mle'. The data-generating process and topology infrastructure (system survival, density, signature, structure function, importance measures) are delegated to the dist.structure package; kofn focuses exclusively on inference for the k-out-of-n family.

It uses species accumulation curves and diverse estimators to assess, at the same time, the levels of survey coverage in multiple geographic cells of a size defined by the user or polygons. It also enables the geographical depiction of observed species richness, survey effort and completeness values including a background with administrative areas.

This package provides methods to extract information on pathways, genes and various single-nucleotid polymorphisms (SNPs) from online databases. It provides functions for data preparation and evaluation of genetic influence on a binary outcome using the logistic kernel machine test (LKMT). Three different kernel functions are offered to analyze genotype information in this variance component test: A linear kernel, a size-adjusted kernel and a network-based kernel).