Variable selection for Gaussian model-based clustering as implemented in the mclust package. The methodology allows to find the (locally) optimal subset of variables in a data set that have group/cluster information. A greedy or headlong search can be used, either in a forward-backward or backward-forward direction, with or without sub-sampling at the hierarchical clustering stage for starting mclust models. By default the algorithm uses a sequential search, but parallelisation is also available.

Copula-based imputation methods: parametric and nonparametric algorithms for missing multivariate data through conditional copulas.

This package provides a simple way to manage application settings by loading configuration values from .env or .ini files. It supports default values, type casting, and environment variable overrides, enabling a clean separation of configuration from code. Ideal for managing credentials, API keys, and deployment-specific settings.

This package provides functions for performing quick observations or evaluations of data, including a variety of ways to list objects by size, class, etc. The functions seqle and reverse.seqle mimic the base rle but can search for linear sequences. The function splatnd allows the user to generate zero-argument commands without the need for makeActiveBinding . Functions provided to convert from any base to any other base, and to find the n-th greatest max or n-th least min. In addition, functions which mimic Unix shell commands, including head', tail ,'pushd ,and popd'. Various other goodies included as well.

Calculates equitable overload compensation for college instructors based on institutional policies, enrollment thresholds, and regular teaching load limits. Compensation is awarded only for credit hours that exceed the regular load and meet minimum enrollment criteria. When enrollment is below a specified threshold, pay is prorated accordingly. The package prioritizes compensation from high-enrollment courses, or optionally from low-enrollment courses for fairness, depending on user-defined strategy. Includes tools for flexible policy settings, instructor filtering, and produces clean, audit-ready summary tables suitable for payroll and administrative reporting.

Enable seamless interaction with Consibio Cloud <https://consibio.cloud> API <https://api.v2.consibio.com/api-docs/>. This package provides tools to query data from resources like projects, elements, devices, and datalogs.

Computes p-value according to the CRT using the HierNet test statistic. For more details, see Ham, Imai, Janson (2022) "Using Machine Learning to Test Causal Hypotheses in Conjoint Analysis" <arXiv:2201.08343>.

Computation of a cubic B-spline basis for arbitrary knots. It also provides the 1st and 2nd derivatives, as well as the integral of the basis elements. It is used by the author to fit penalized B-spline models, see e.g. Jullion, A. and Lambert, P. (2006) <doi:10.1016/j.csda.2006.09.027>, Lambert, P. and Eilers, P.H.C. (2009) <doi:10.1016/j.csda.2008.11.022> and, more recently, Lambert, P. (2021) <doi:10.1016/j.csda.2021.107250>. It is inspired by the algorithm developed by de Boor, C. (1977) <doi:10.1137/0714026>.

This package provides a collection of functions that make it easier to understand crime (or other) data, and assist others in understanding it. The package helps you read data from various sources, clean it, fix column names, and graph the data.

General functions for performing extreme value analysis on a circular domain as part of the statistical methodology in the paper by Konzen, E., Neves, C., and Jonathan, P. (2021). Modeling nonstationary extremes of storm severity: Comparing parametric and semiparametric inference. Environmetrics, 32(4), e2667 <doi:10.1002/env.2667>.

This package provides a collection of data sets for teaching cluster analysis.

This package provides functions for nonlinear regression parameters estimation by algorithms based on Controlled Random Search algorithm. Both functions (crs4hc(), crs4hce()) adapt current search strategy by four heuristics competition. In addition, crs4hce() improves adaptability by adaptive stopping condition.

Allows users to seamlessly query several CDC PLACES APIs (<https://data.cdc.gov/browse?q=PLACES%20&sortBy=relevance>) by geography, state, measure, and release year. This package also contains a function to explore the available measures for each release year.

Cohort plAtform Trial Simulation whereby every cohort consists of two arms, control and experimental treatment. Endpoints are co-primary binary endpoints and decisions are made using either Bayesian or frequentist decision rules. Realistic trial trajectories are simulated and the operating characteristics of the designs are calculated.

This package provides a home for the S3 generics shared across the r-causal ecosystem, including propensity', halfmoon', positively', and balancing'. Owning the generic definitions in one place lets those packages register methods without masking one another when several are attached at once. The generics cover inverse probability weighted estimation, effective sample size, and the metadata carried by causal weight vectors, and the package supplies the abstract causal weight class and the result class those estimates are returned in, together with the methods both classes carry, so that the ecosystem packages inherit them rather than writing their own. The design follows that of the generics package, which provides commonly used S3 generics for the same purpose: letting packages share a single definition instead of each defining its own.

Includes wrapper functions around existing functions for the analysis of categorical data and introduces functions for calculating risk differences and matched odds ratios. R currently supports a wide variety of tools for the analysis of categorical data. However, many functions are spread across a variety of packages with differing syntax and poor compatibility with each another. prop_test() combines the functions binom.test(), prop.test() and BinomCI() into one output. prop_power() allows for power and sample size calculations for both balanced and unbalanced designs. riskdiff() is used for calculating risk differences and matched_or() is used for calculating matched odds ratios. For further information on methods used that are not documented in other packages see Nathan Mantel and William Haenszel (1959) <doi:10.1093/jnci/22.4.719> and Alan Agresti (2002) <ISBN:0-471-36093-7>.

Sample and cell filtering as well as visualisation of output metrics from Cell Ranger by Grace X.Y. Zheng et al. (2017) <doi:10.1038/ncomms14049>. CRMetrics allows for easy plotting of output metrics across multiple samples as well as comparative plots including statistical assessments of these. CRMetrics allows for easy removal of ambient RNA using SoupX by Matthew D Young and Sam Behjati (2020) <doi:10.1093/gigascience/giaa151> or CellBender by Stephen J Fleming et al. (2022) <doi:10.1101/791699>. Furthermore, it is possible to preprocess data using Pagoda2 by Nikolas Barkas et al. (2021) <https://github.com/kharchenkolab/pagoda2> or Seurat by Yuhan Hao et al. (2021) <doi:10.1016/j.cell.2021.04.048> followed by embedding of cells using Conos by Nikolas Barkas et al. (2019) <doi:10.1038/s41592-019-0466-z>. Finally, doublets can be detected using scrublet by Samuel L. Wolock et al. (2019) <doi:10.1016/j.cels.2018.11.005> or DoubletDetection by Gayoso et al. (2020) <doi:10.5281/zenodo.2678041>. In the end, cells are filtered based on user input for use in downstream applications.

This package provides automated methods for downloading, recoding, and merging selected years of the Current Population Survey's Voting and Registration Supplement <https://www.nber.org/research/data/current-population-survey-cps-supplements-voting-and-registration>, a large N national survey about registration, voting, and non-voting in United States federal elections. Provides documentation for appropriate use of sample weights to generate statistical estimates, drawing from Hur & Achen (2013) <doi:10.1093/poq/nft042> and McDonald (2018) <https://www.electproject.org/election-data/voter-turnout-data>.

Implementation of the Control Polygon Reduction and Control Net Reduction methods for finding parsimonious B-spline regression models.

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.

Explore calcium (Ca) and phosphate (Pi) homeostasis with two novel Shiny apps, building upon on a previously published mathematical model written in C, to ensure efficient computations. The underlying model is accessible here <https://pubmed.ncbi.nlm.nih.gov/28747359/)>. The first application explores the fundamentals of Ca-Pi homeostasis, while the second provides interactive case studies for in-depth exploration of the topic, thereby seeking to foster student engagement and an integrative understanding of Ca-Pi regulation.

Convert BCD (raw bytes) to decimal numbers and vice versa. BCD format is used to preserve decimals exactly, as opposed to the binary rounding errors inherent in "numeric" or "floating-point" formats.

This package provides harmonized and non-harmonized population pyramid datasets from the Indonesian population censuses (1971â 2020), along with tools for visualization and an interactive shiny'-based explorer application. Data are processed from IPUMS International (1971â 2010) and the Population Census 2020 (BPS Indonesia).

This package provides tools for calculating coordinate representations of hypocycloids, epicyloids, hypotrochoids, and epitrochoids (altogether called cycloids here) with different scaling and positioning options. The cycloids can be visualised with any appropriate graphics function in R.