This package provides a collection of shiny applications for the tesselle packages <https://www.tesselle.org/>. This package provides applications for archaeological data analysis and visualization. These mainly, but not exclusively, include applications for chronological modelling (e.g. matrix seriation, aoristic analysis) and count data analysis (e.g. diversity measures, compositional data analysis).

The official website of the Trends in International Mathematics and Science Study (TIMSS) 2023 provides Student Process Data Files for Grade 4 in RData format. However, the data are presented exclusively in numerical form. This package converts the numeric values into categorical variables, allowing for easier interpretation and reducing ambiguity in statistical analysis. The category labels are presented in Bahasa Indonesia. This effort also supports the promotion of Bahasa Indonesia in programming, in line with its recognition as one of the official languages of the United Nations. For further information, visit <https://timss2023.org/>.

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>.

Clustering typically assigns data points into discrete groups, but the clusters can sometimes be indistinct. Cluster sharpening adjusts an existing clustering to create contrast between groups. This package provides a general interface for cluster sharpening along with several implementations based on different excision criteria.

Nonparametric kernel distribution function estimation is performed. Three bandwidth selectors are implemented: the plug-in selectors of Altman and Leger and of Polansky and Baker, and the cross-validation selector of Bowman, Hall and Prvan. The exceedance function, the mean return period and the return level are also computed. For details, see Quintela-del-Rà o and Estévez-Pérez (2012) <doi:10.18637/jss.v050.i08>.

This package provides functions to search, retrieve, apply and update classification standards and code lists using Statistics Norway's API <https://www.ssb.no/klass> from the system KLASS'. Retrieves classifications by date with options to choose language, hierarchical level and formatting.

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.

Caches and then connects to a sqlite database containing half a million pediatric drug safety signals. The database is part of a family of resources catalogued at <https://nsides.io>. The database contains 17 tables where the description table provides a map between the fields the field's details. The database was created by Nicholas Giangreco during his PhD thesis which you can read in Giangreco (2022) <doi:10.7916/d8-5d9b-6738>. The observations are from the Food and Drug Administration's Adverse Event Reporting System. Generalized additive models estimated drug effects across child development stages for the occurrence of an adverse event when exposed to a drug compared to other drugs. Read more at the methods detailed in Giangreco (2022) <doi:10.1016/j.medj.2022.06.001>.

Given constraints for right censored data, we use a recursive computational algorithm to calculate the the "constrained" Kaplan-Meier estimator. The constraint is assumed given in linear estimating equations or mean functions. We also illustrate how this leads to the empirical likelihood ratio test with right censored data and accelerated failure time model with given coefficients. EM algorithm from emplik package is used to get the initial value. The properties and performance of the EM algorithm is discussed in Mai Zhou and Yifan Yang (2015)<doi: 10.1007/s00180-015-0567-9> and Mai Zhou and Yifan Yang (2017) <doi: 10.1002/wics.1400>. More applications could be found in Mai Zhou (2015) <doi: 10.1201/b18598>.

This package provides tools for keeping track of information, named "keys", about rows of data frame like objects. This is done by creating special attribute "keys" which is updated after every change in rows (subsetting, ordering, etc.). This package is designed to work tightly with dplyr package.

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

This package provides a new set of tools to help with the development of detailed ecological niche models using multiple algorithms. Pre-modeling analyses and explorations can be done to prepare data. Model calibration (model selection) can be done by creating and testing models with several parameter combinations. Handy options for producing final models with transfers are included. Other tools to assess extrapolation risks and variability in model transfers are also available. Methodological and theoretical basis for the methods implemented here can be found in: Peterson et al. (2011) <https://www.degruyter.com/princetonup/view/title/506966>, Radosavljevic and Anderson (2014) <doi:10.1111/jbi.12227>, Peterson et al. (2018) <doi:10.1111/nyas.13873>, Cobos et al. (2019) <doi:10.7717/peerj.6281>, Alkishe et al. (2020) <doi:10.1016/j.pecon.2020.03.002>, Machado-Stredel et al. (2021) <doi:10.21425/F5FBG48814>, Arias-Giraldo and Cobos (2024) <doi:10.17161/bi.v18i.21742>, Cobos et al. (2024) <doi:10.17161/bi.v18i.21742>.

This package contains kernel smoothing tools designed for use by historical dialectologists and philologists for exploring spatial and temporal patterns in noisy historical language data, such as that obtained from historical texts. The main way in which these might differ from other implementations of kernel smoothing is that they assume that the function (linguistic variable) being explored has the form of the relative frequency of a series of discrete possibilities (linguistic variants). This package also offers a way of exploring distributions in 2-dimensional space and in time with separate kernels, and tools for identifying appropriate bandwidths for these.

Makes visually pleasing diagrams of knot projections using optimized Bezier curves.

State space modelling is an efficient and flexible framework for statistical inference of a broad class of time series and other data. KFAS includes computationally efficient functions for Kalman filtering, smoothing, forecasting, and simulation of multivariate exponential family state space models, with observations from Gaussian, Poisson, binomial, negative binomial, and gamma distributions. See the paper by Helske (2017) <doi:10.18637/jss.v078.i10> for details.

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 a variable selection procedure, dubbed KKO, for nonparametric additive model with finite-sample false discovery rate control guarantee. The method integrates three key components: knockoffs, subsampling for stability, and random feature mapping for nonparametric function approximation. For more information, see the accompanying paper: Dai, X., Lyu, X., & Li, L. (2021). â Kernel Knockoffs Selection for Nonparametric Additive Modelsâ . arXiv preprint <arXiv:2105.11659>.

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).

The format KVH is a lightweight format that can be read/written both by humans and machines. It can be useful in situations where XML or alike formats seem to be an overkill. We provide an ability to parse KVH files in R pretty fast due to Rcpp use.

This package provides a comprehensive set of geostatistical, visual, and analytical methods, in conjunction with the expanded version of the acclaimed J.E. Klovan's mining dataset, are included in klovan'. This makes the package an excellent learning resource for Principal Component Analysis (PCA), Factor Analysis (FA), kriging, and other geostatistical techniques. Originally published in the 1976 book Geological Factor Analysis', the included mining dataset was assembled by Professor J. E. Klovan of the University of Calgary. Being one of the first applications of FA in the geosciences, this dataset has significant historical importance. As a well-regarded and published dataset, it is an excellent resource for demonstrating the capabilities of PCA, FA, kriging, and other geostatistical techniques in geosciences. For those interested in these methods, the klovan datasets provide a valuable and illustrative resource. Note that some methods require the RGeostats package. Please refer to the README or Additional_repositories for installation instructions. This material is based upon research in the Materials Data Science for Stockpile Stewardship Center of Excellence (MDS3-COE), and supported by the Department of Energy's National Nuclear Security Administration under Award Number DE-NA0004104.

Online, Semi-online, and Offline K-medians algorithms are given. For both methods, the algorithms can be initialized randomly or with the help of a robust hierarchical clustering. The number of clusters can be selected with the help of a penalized criterion. We provide functions to provide robust clustering. Function gen_K() enables to generate a sample of data following a contaminated Gaussian mixture. Functions Kmedians() and Kmeans() consists in a K-median and a K-means algorithms while Kplot() enables to produce graph for both methods. Cardot, H., Cenac, P. and Zitt, P-A. (2013). "Efficient and fast estimation of the geometric median in Hilbert spaces with an averaged stochastic gradient algorithm". Bernoulli, 19, 18-43. <doi:10.3150/11-BEJ390>. Cardot, H. and Godichon-Baggioni, A. (2017). "Fast Estimation of the Median Covariation Matrix with Application to Online Robust Principal Components Analysis". Test, 26(3), 461-480 <doi:10.1007/s11749-016-0519-x>. Godichon-Baggioni, A. and Surendran, S. "A penalized criterion for selecting the number of clusters for K-medians" <arXiv:2209.03597> Vardi, Y. and Zhang, C.-H. (2000). "The multivariate L1-median and associated data depth". Proc. Natl. Acad. Sci. USA, 97(4):1423-1426. <doi:10.1073/pnas.97.4.1423>.

This package contains functions to compute p-values for the one-sample and two-sample Kolmogorov-Smirnov (KS) tests and the two-sample Kuiper test for any fixed critical level and arbitrary (possibly very large) sample sizes. For the one-sample KS test, this package implements a novel, accurate and efficient method named Exact-KS-FFT, which allows the pre-specified cumulative distribution function under the null hypothesis to be continuous, purely discrete or mixed. In the two-sample case, it is assumed that both samples come from an unspecified (unknown) continuous, purely discrete or mixed distribution, i.e. ties (repeated observations) are allowed, and exact p-values of the KS and the Kuiper tests are computed. Note, the two-sample Kuiper test is often used when data samples are on the line or on the circle (circular data). To cite this package in publication: (for the use of the one-sample KS test) Dimitrina S. Dimitrova, Vladimir K. Kaishev, and Senren Tan. Computing the Kolmogorov-Smirnov Distribution When the Underlying CDF is Purely Discrete, Mixed, or Continuous. Journal of Statistical Software. 2020; 95(10): 1--42. <doi:10.18637/jss.v095.i10>. (for the use of the two-sample KS and Kuiper tests) Dimitrina S. Dimitrova, Yun Jia and Vladimir K. Kaishev (2024). The R functions KS2sample and Kuiper2sample: Efficient Exact Calculation of P-values of the Two-sample Kolmogorov-Smirnov and Kuiper Tests. submitted.

Statistical methods that quantify the conditions necessary to alter inferences, also known as sensitivity analysis, are becoming increasingly important to a variety of quantitative sciences. A series of recent works, including Frank (2000) <doi:10.1177/0049124100029002001> and Frank et al. (2013) <doi:10.3102/0162373713493129> extend previous sensitivity analyses by considering the characteristics of omitted variables or unobserved cases that would change an inference if such variables or cases were observed. These analyses generate statements such as "an omitted variable would have to be correlated at xx with the predictor of interest (e.g., the treatment) and outcome to invalidate an inference of a treatment effect". Or "one would have to replace pp percent of the observed data with nor which the treatment had no effect to invalidate the inference". We implement these recent developments of sensitivity analysis and provide modules to calculate these two robustness indices and generate such statements in R. In particular, the functions konfound(), pkonfound() and mkonfound() allow users to calculate the robustness of inferences for a user's own model, a single published study and multiple studies respectively.

The official Trends in International Mathematics and Science Study (TIMSS) 2023 website provides Student Achievement Data Files for Grade 4 in RData format. However, the available data are presented solely as numerical values. This package transforms the numerical data into categorical variables, enabling clearer interpretation and reducing ambiguity in statistical analysis. The category labels are provided in Bahasa Indonesia. This initiative contributes to promoting the use of Bahasa Indonesia in programming, in line with its designation as one of the official languages of the United Nations. For more details see <https://timss2023.org/>.