Get insight into a forest of classification trees, by calculating similarities between the trees, and subsequently clustering them. Each cluster is represented by it's most central cluster member. The package implements the methodology described in Sies & Van Mechelen (2020) <doi:10.1007/s00357-019-09350-4>.

Interface to interest and foreign exchange rates published by the Czech National Bank.

This package provides the tools to produce catseye plots, principally by catseyesplot() function which calls R's standard plot() function internally, or alternatively by the catseyes() function to overlay the catseye plot onto an existing R plot window. Catseye plots illustrate the normal distribution of the mean (picture a normal bell curve reflected over its base and rotated 90 degrees), with a shaded confidence interval; they are an intuitive way of illustrating and comparing normally distributed estimates, and are arguably a superior alternative to standard confidence intervals, since they show the full distribution rather than fixed quantile bounds. The catseyesplot and catseyes functions require pre-calculated means and standard errors (or standard deviations), provided as numeric vectors; this allows the flexibility of obtaining this information from a variety of sources, such as direct calculation or prediction from a model. Catseye plots, as illustrations of the normal distribution of the means, are described in Cumming (2013 & 2014). Cumming, G. (2013). The new statistics: Why and how. Psychological Science, 27, 7-29. <doi:10.1177/0956797613504966> pmid:24220629.

Cronbach's alpha and various formulas for confidence intervals. The relevant paper is Tsagris M., Frangos C.C. and Frangos C.C. (2013). "Confidence intervals for Cronbach's reliability coefficient". Recent Techniques in Educational Science, 14-16 May, Athens, Greece.

This package implements a Bayesian approach to causal impact estimation in time series, as described in Brodersen et al. (2015) <DOI:10.1214/14-AOAS788>. See the package documentation on GitHub <https://google.github.io/CausalImpact/> to get started.

This package implements a basis function or functional data analysis framework for several techniques of multivariate analysis in continuous-time setting. Specifically, we introduced continuous-time analogues of several classical techniques of multivariate analysis, such as principal component analysis, canonical correlation analysis, Fisher linear discriminant analysis, K-means clustering, and so on. Details are in Biplab Paul, Philip T. Reiss, Erjia Cui and Noemi Foa (2025) "Continuous-time multivariate analysis" <doi: 10.1080/10618600.2024.2374570>.

This package provides an extension to the purrr family of mapping functions to apply a function to each combination of elements in a list of inputs. Also includes functions for automatically detecting output type in mapping functions, finding every combination of elements of lists or rows of data frames, and applying multiple models to multiple subsets of a dataset.

Non-parametric tests (Wilcoxon rank sum test and Wilcoxon signed rank test) for clustered data documented in Jiang et. al (2020) <doi:10.18637/jss.v096.i06>.

CPP is a multiple criteria decision method to evaluate alternatives on complex decision making problems, by a probabilistic approach. The CPP was created and expanded by Sant'Anna, Annibal P. (2015) <doi:10.1007/978-3-319-11277-0>.

Estimation, prediction, and simulation of nonstationary Gaussian process with modular covariate-based covariance functions. Sources of nonstationarity, such as spatial mean, variance, geometric anisotropy, smoothness, and nugget, can be considered based on spatial characteristics. An induced compact-supported nonstationary covariance function is provided, enabling fast and memory-efficient computations when handling densely sampled domains.

Access the Cumulocity API and retrieve data on devices, measurements, and events. Documentation for the API can be found at <https://www.cumulocity.com/guides/reference/rest-implementation/>.

Data from statistical agencies and other institutions often need to be protected before they can be published. This package can be used to perturb statistical tables in a consistent way. The main idea is to add - at the micro data level - a record key for each unit. Based on these keys, for any cell in a statistical table a cell key is computed as a function on the record keys contributing to a specific cell. Values that are added to the cell in order to perturb it are derived from a lookup-table that maps values of cell keys to specific perturbation values. The theoretical basis for the methods implemented can be found in Thompson, Broadfoot and Elazar (2013) <https://unece.org/fileadmin/DAM/stats/documents/ece/ces/ge.46/2013/Topic_1_ABS.pdf> which was extended and enhanced by Giessing and Tent (2019) <https://unece.org/fileadmin/DAM/stats/documents/ece/ces/ge.46/2019/mtg1/SDC2019_S2_Germany_Giessing_Tent_AD.pdf>.

This package provides a genome-wide survival framework that integrates sequential conditional independent tuples and saddlepoint approximation method, to provide SNP-level false discovery rate control while improving power, particularly for biobank-scale survival analyses with low event rates. The method is based on model-X knockoffs as described in Barber and Candes (2015) <doi:10.1214/15-AOS1337> and fast survival analysis methods from Bi et al. (2020) <doi:10.1016/j.ajhg.2020.06.003>. A shrinkage algorithmic leveraging accelerates multiple knockoffs generation in large genetic cohorts. This CRAN version uses standard Cox regression for association testing. For enhanced performance on very large datasets, users may optionally install the SPACox package from GitHub which provides saddlepoint approximation methods for survival analysis.

Detects multiple changes in slope using the CPOP dynamic programming approach of Fearnhead, Maidstone, and Letchford (2019) <doi:10.1080/10618600.2018.1512868>. This method finds the best continuous piecewise linear fit to data under a criterion that measures fit to data using the residual sum of squares, but penalizes complexity based on an L0 penalty on changes in slope. Further information regarding the use of this package with detailed examples can be found in Fearnhead and Grose (2024) <doi:10.18637/jss.v109.i07>.

Plot confidence interval from the objects of statistical tests such as t.test(), var.test(), cor.test(), prop.test() and fisher.test() ('htest class), Tukey test [TukeyHSD()], Dunnett test [glht() in multcomp package], logistic regression [glm()], and Tukey or Games-Howell test [posthocTGH() in userfriendlyscience package]. Users are able to set the styles of lines and points. This package contains the function to calculate odds ratios and their confidence intervals from the result of logistic regression.

This package provides a set of tools for evaluating clustering robustness using proportion of ambiguously clustered pairs (Senbabaoglu et al. (2014) <doi:10.1038/srep06207>), as well as similarity across methods and method stability using element-centric clustering comparison (Gates et al. (2019) <doi:10.1038/s41598-019-44892-y>). Additionally, this package enables stability-based parameter assessment for graph-based clustering pipelines typical in single-cell data analysis.

This package provides a wrapper for the CDRC API that returns data frames or sf of CDRC data. The API web reference is:<https://api.cdrc.ac.uk/swagger/index.html>.

This package provides a modeling tool allowing gene selection, reverse engineering, and prediction in cascade networks. Jung, N., Bertrand, F., Bahram, S., Vallat, L., and Maumy-Bertrand, M. (2014) <doi:10.1093/bioinformatics/btt705>.

Colocalisation analysis tests whether two traits share a causal genetic variant in a specified genomic region. Proportional testing for colocalisation has been previously proposed [Wallace (2013) <doi:10.1002/gepi.21765>], but is reimplemented here to overcome barriers to its adoption. Its use is complementary to the fine- mapping based colocalisation method in the coloc package, and may be used in particular to identify false "H3" conclusions in coloc'.

The primary motivation of this package is to take the things that are great about the R packages flextable <https://davidgohel.github.io/flextable/> and officer <https://davidgohel.github.io/officer/>, take the standard and complex pieces of formatting clinical tables for regulatory use, and simplify the tedious pieces.

Fast and memory-efficient (or cheap') tools to facilitate efficient programming, saving time and memory. It aims to provide cheaper alternatives to common base R functions, as well as some additional functions.

Simulates time-to-event data with type I right censoring using two methods: the inverse CDF method and our proposed memoryless method. The latter method takes advantage of the memoryless property of survival and simulates a separate distribution between change-points. We include two parametric distributions: exponential and Weibull. Inverse CDF method draws on the work of Rainer Walke (2010), <https://www.demogr.mpg.de/papers/technicalreports/tr-2010-003.pdf>.

Generate synthetic station-based monthly climate time-series including temperature and rainfall, export to Network Common Data Form (NetCDF), and provide visualization helpers for climate workflows. The approach is inspired by statistical weather generator concepts described in Wilks (1992) <doi:10.1016/S0168-1923(99)00037-4> and Richardson (1981) <doi:10.1029/WR017i001p00182>.

Estimate different types of cluster robust standard errors (CR0, CR1, CR2) with degrees of freedom adjustments. Standard errors are computed based on Liang and Zeger (1986) <doi:10.1093/biomet/73.1.13> and Bell and McCaffrey <https://www150.statcan.gc.ca/n1/en/pub/12-001-x/2002002/article/9058-eng.pdf?st=NxMjN1YZ>. Functions used in Huang and Li <doi:10.3758/s13428-021-01627-0>, Huang, Wiedermann', and Zhang <doi:10.1080/00273171.2022.2077290>, and Huang, Zhang', and Li (forthcoming: Journal of Research on Educational Effectiveness).