This package implements the Changepoints for a Range of Penalties (CROPS) algorithm of Haynes et al. (2017) <doi:10.1080/10618600.2015.1116445> for finding all of the optimal segmentations for multiple penalty values over a continuous range.

This package provides tools for analyzing performances of cricketers based on stats in ESPN Cricinfo Statsguru. The toolset can be used for analysis of Tests,ODIs and Twenty20 matches of both batsmen and bowlers. The package can also be used to analyze team performances.

Implementation of the Wilkinson and Ivany (2002) approach to paleoclimate analysis, applied to isotope data extracted from clams.

Provide the CrossClustering algorithm (Tellaroli et al. (2016) <doi:10.1371/journal.pone.0152333>), which is a partial clustering algorithm that combines the Ward's minimum variance and Complete Linkage algorithms, providing automatic estimation of a suitable number of clusters and identification of outlier elements.

Measuring child development starts by collecting responses to developmental milestones, such as "able to sit" or "says two words". There are many ways to combine such responses into summaries. The package bundles publicly available datasets with individual milestone data for children aged 0-5 years, with the aim of supporting the construction, evaluation, validation and interpretation of methodologies that aggregate milestone data into informative measures of child development.

Logic game in the style of the early 1980s home computers that can be played in the R console. This game is inspired by Mastermind, a game that became popular in the 1970s. Can you break the code?

Examine any number of time series data frames to identify instances in which various criteria are met within specified time frames. In clinical medicine, these types of events are often called "constellations of signs and symptoms", because a single condition depends on a series of events occurring within a certain amount of time of each other. This package was written to work with any number of time series data frames and is optimized for speed to work well with data frames with millions of rows.

Cronbach's alpha and McDonald's omega are widely used reliability or internal consistency measures in social, behavioral and education sciences. Alpha is reported in nearly every study that involves measuring a construct through multiple test items. The package coefficientalpha calculates coefficient alpha and coefficient omega with missing data and non-normal data. Robust standard errors and confidence intervals are also provided. A test is also available to test the tau-equivalent and homogeneous assumptions. Since Version 0.5, the bootstrap confidence intervals were added.

Genome-wide association studies (GWAS) have been widely used for identifying common variants associated with complex diseases. Due to the small effect sizes of common variants, the power to detect individual risk variants is generally low. Complementary to SNP-level analysis, a variety of gene-based association tests have been proposed. However, the power of existing gene-based tests is often dependent on the underlying genetic models, and it is not known a priori which test is optimal. Here we proposed COMBined Association Test (COMBAT) to incorporate strengths from multiple existing gene-based tests, including VEGAS, GATES and simpleM. Compared to individual tests, COMBAT shows higher overall performance and robustness across a wide range of genetic models. The algorithm behind this method is described in Wang et al (2017) <doi:10.1534/genetics.117.300257>.

This package provides functionality for computing support intervals for univariate parameters based on confidence intervals or parameter estimates with standard errors (Pawel et al., 2022) <doi:10.48550/arXiv.2206.12290>.

This is an add-on to the cna package <https://CRAN.R-project.org/package=cna> comprising various functions for optimizing consistency and coverage scores of models of configurational comparative methods as Coincidence Analysis (CNA) and Qualitative Comparative Analysis (QCA). The function conCovOpt() calculates con-cov optima, selectMax() selects con-cov maxima among the con-cov optima, DNFbuild() can be used to build models actually reaching those optima, and findOutcomes() identifies those factor values in analyzed data that can be modeled as outcomes. For a theoretical introduction to these functions see Baumgartner and Ambuehl (2021) <doi:10.1177/0049124121995554>.

This package performs survival analysis using general non-linear models. Risk models can be the sum or product of terms. Each term is the product of exponential/linear functions of covariates. Additionally sub-terms can be defined as a sum of exponential, linear threshold, and step functions. Cox Proportional hazards, Poisson, and Fine-Gray competing risks regression are supported. This work was sponsored by NASA Grants 80NSSC19M0161 and 80NSSC23M0129 through a subcontract from the National Council on Radiation Protection and Measurements (NCRP). The computing for this project was performed on the Beocat Research Cluster at Kansas State University, which is funded in part by NSF grants CNS-1006860, EPS-1006860, EPS-0919443, ACI-1440548, CHE-1726332, and NIH P20GM113109.

Directory reads and summaries are provided for one or more of the subdirectories of the <https://cran.r-project.org/incoming/> directory, and a compact summary object is returned. The package name is a contraption of CRAN Incoming Watcher'.

This package provides a user friendly function crrcbcv to compute bias-corrected variances for competing risks regression models using proportional subdistribution hazards with small-sample clustered data. Four types of bias correction are included: the MD-type bias correction by Mancl and DeRouen (2001) <doi:10.1111/j.0006-341X.2001.00126.x>, the KC-type bias correction by Kauermann and Carroll (2001) <doi:10.1198/016214501753382309>, the FG-type bias correction by Fay and Graubard (2001) <doi:10.1111/j.0006-341X.2001.01198.x>, and the MBN-type bias correction by Morel, Bokossa, and Neerchal (2003) <doi:10.1002/bimj.200390021>.

Isotonic regression (IR) and its improvement: centered isotonic regression (CIR). CIR is recommended in particular with small samples. Also, interval estimates for both, and additional utilities such as plotting dose-response data. For dev version and change history, see GitHub assaforon/cir.

Includes functions for the analysis of circular data using distributions based on Nonnegative Trigonometric Sums (NNTS). The package includes functions for calculation of densities and distributions, for the estimation of parameters, for plotting and more.

Find the numbers of test tubes that can be balanced in centrifuge rotors and show various ways to load them. Refer to Pham (2020) <doi:10.31224/osf.io/4xs38> for more information on package functionality.

Geospatial data computation is parallelized by grid, hierarchy, or raster files. Based on future (Bengtsson, 2024 <doi:10.32614/CRAN.package.future>) and mirai (Gao et al., 2025 <doi:10.32614/CRAN.package.mirai>) parallel back-ends, terra (Hijmans et al., 2025 <doi:10.32614/CRAN.package.terra>) and sf (Pebesma et al., 2024 <doi:10.32614/CRAN.package.sf>) functions as well as convenience functions in the package can be distributed over multiple threads. The simplest way of parallelizing generic geospatial computation is to start from par_pad_*() functions to par_grid(), par_hierarchy(), or par_multirasters() functions. Virtually any functions accepting classes in terra or sf packages can be used in the three parallelization functions. A common raster-vector overlay operation is provided as a function extract_at(), which uses exactextractr (Baston, 2023 <doi:10.32614/CRAN.package.exactextractr>), with options for kernel weights for summarizing raster values at vector geometries. Other convenience functions for vector-vector operations including simple areal interpolation (summarize_aw()) and summation of exponentially decaying weights (summarize_sedc()) are also provided.

This package implements a specific form of segmented linear regression with two independent variables. The visualization of that function looks like a quarter segment of a cowbell giving the package its name. The package has been specifically constructed for the case where minimum and maximum value of the dependent and two independent variables are known a prior, which is usually the case when those values are derived from Likert scales.

Covariate-augumented generalized factor model is designed to account for cross-modal heterogeneity, capture nonlinear dependencies among the data, incorporate additional information, and provide excellent interpretability while maintaining high computational efficiency.

This package provides a function that facilitates fitting three types of models for contrast-based Bayesian Network Meta Analysis. The first model is that which is described in Lu and Ades (2006) <doi:10.1198/016214505000001302>. The other two models are based on a Bayesian nonparametric methods that permit ties when comparing treatment or for a treatment effect to be exactly equal to zero. In addition to the model fits, the package provides a summary of the interplay between treatment effects based on the procedure described in Barrientos, Page, and Lin (2023) <doi:10.48550/arXiv.2207.06561>.

This package implements several statistical tests for structural change, specifically the tests featured in Horváth, Rice and Miller (in press): CUSUM (with weighted/trimmed variants), Darling-Erdös, Hidalgo-Seo, Andrews, and the new Rényi-type test.

Color values in R are often represented as strings of hexadecimal colors or named colors. This package offers fast conversion of these color representations to either an array of red/green/blue/alpha values or to the packed integer format used in native raster objects. Functions for conversion are also exported at the C level for use in other packages. This fast conversion of colors is implemented using an order-preserving minimal perfect hash derived from Majewski et al (1996) "A Family of Perfect Hashing Methods" <doi:10.1093/comjnl/39.6.547>.

Temporally autocorrelated populations are correlated in their vital rates (growth, death, etc.) from year to year. It is very common for populations, whether they be bacteria, plants, or humans, to be temporally autocorrelated. This poses a challenge for stochastic population modeling, because a temporally correlated population will behave differently from an uncorrelated one. This package provides tools for simulating populations with white noise (no temporal autocorrelation), red noise (positive temporal autocorrelation), and blue noise (negative temporal autocorrelation). The algebraic formulation for autocorrelated noise comes from Ruokolainen et al. (2009) <doi:10.1016/j.tree.2009.04.009>. Models for unstructured populations and for structured populations (matrix models) are available.