The currentSurvival package contains functions for the estimation of the current cumulative incidence (CCI) and the current leukaemia-free survival (CLFS). The CCI is the probability that a patient is alive and in any disease remission (e.g. complete cytogenetic remission in chronic myeloid leukaemia) after initiating his or her therapy (e.g. tyrosine kinase therapy for chronic myeloid leukaemia). The CLFS is the probability that a patient is alive and in any disease remission after achieving the first disease remission.

Retail shopping transactions for 2,469 households over one year. Originates from the 84.51° Complete Journey 2.0 source files <https://www.8451.com/area51> which also includes useful metadata on products, coupons, campaigns, and promotions.

While individual calibrated radiocarbon dates can span several centuries, combining multiple dates together with any chronological constraints can make a chronology much more robust and precise. This package uses Bayesian methods to enforce the chronological ordering of radiocarbon and other dates, for example for trees with multiple radiocarbon dates spaced at exactly known intervals (e.g., 10 annual rings). For methods see Christen 2003 <doi:10.11141/ia.13.2>. Another example is sites where the relative chronological position of the dates is taken into account - the ages of dates further down a site must be older than those of dates further up (Buck, Kenworthy, Litton and Smith 1991 <doi:10.1017/S0003598X00080534>; Nicholls and Jones 2001 <doi:10.1111/1467-9876.00250>). The paper accompanying this R package is Blaauw et al. 2024 <doi:10.1017/RDC.2024.56>.

This package provides the facility to perform the chi-square and G-square test of independence, calculates the retrospective power of the traditional chi-square test, compute permutation and Monte Carlo p-value, and provides measures of association for tables of any size such as Phi, Phi corrected, odds ratio with 95 percent CI and p-value, Yule Q and Y, adjusted contingency coefficient, Cramer's V, V corrected, V standardised, bias-corrected V, W, Cohen's w, Goodman-Kruskal's lambda, and tau. It also calculates standardised, moment-corrected standardised, and adjusted standardised residuals, and their significance, as well as the Quetelet Index, IJ association factor, and adjusted standardised counts. It also computes the chi-square-maximising version of the input table. Different outputs are returned in nicely formatted tables.

High dimensional discriminant analysis with compositional data is performed. The compositional data are first transformed using the alpha-transformation of Tsagris M., Preston S. and Wood A.T.A. (2011) <doi:10.48550/arXiv.1106.1451>, and then the High Dimensional Discriminant Analysis (HDDA) algorithm of Bouveyron C. Girard S. and Schmid C. (2007) <doi:10.1080/03610920701271095> is applied.

This package provides a fast and general implementation of the Elston-Stewart algorithm that can calculate the likelihoods of large and complex pedigrees. References for the Elston-Stewart algorithm are Elston & Stewart (1971) <doi:10.1159/000152448>, Lange & Elston (1975) <doi:10.1159/000152714> and Cannings et al. (1978) <doi:10.2307/1426718>.

Package to analyze the clinical utility of a biomarker. It provides the clinical utility curve, clinical utility table, efficacy of a biomarker, clinical efficacy curve and tests to compare efficacy between markers.

Software which provides numerous functionalities for detecting and removing group-level effects from high-dimensional scientific data which, when combined with additional assumptions, allow for causal conclusions, as-described in our manuscripts Bridgeford et al. (2024) <doi:10.1101/2021.09.03.458920> and Bridgeford et al. (2023) <doi:10.48550/arXiv.2307.13868>. Also provides a number of useful utilities for generating simulations and balancing covariates across multiple groups/batches of data via matching and propensity trimming for more than two groups.

Create and integrate maps in your R workflow. This package helps to design cartographic representations such as proportional symbols, choropleth, typology, flows or discontinuities maps. It also offers several features that improve the graphic presentation of maps, for instance, map palettes, layout elements (scale, north arrow, title...), labels or legends. See Giraud and Lambert (2017) <doi:10.1007/978-3-319-57336-6_13>.

Record and generate a gif of your R sessions plots. When creating a visualization, there is inevitably iteration and refinement that occurs. Automatically save the plots made to a specified directory, previewing them as they would be saved. Then combine all plots generated into a gif to show the plot refinement over time.

Identification and network inference of genetic loci associated with correlation changes in quantitative traits (called correlated trait loci, CTLs). Arends et al. (2016) <doi:10.21105/joss.00087>.

Quick and easy access to datasets that let you replicate the empirical examples in Cameron and Trivedi (2005) "Microeconometrics: Methods and Applications" (ISBN: 9780521848053).The data are available as soon as you install and load the package (lazy-loading) as data frames. The documentation includes reference to chapter sections and page numbers where the datasets are used.

This package provides methods to help selecting General Circulation Models (GCMs) in the context of projecting models to future scenarios. It is provided clusterization algorithms, distance and correlation metrics, as well as a tailor-made algorithm to detect the optimum subset of GCMs that recreate the environment of all GCMs as proposed in Esser et al. (2025) <doi:10.1111/gcb.70008>.

Draws systematic samples from a population that follows linear trend. The function returns a matrix comprising of the required samples as its column vectors. The samples produced are highly efficient and the inter sampling variance is minimum. The scheme will be useful in various field like Bioinformatics where the samples are expensive and must be precise in reflecting the population by possessing least sampling variance.

This package provides some simple functions for printing text in color in markdown or Quarto documents, to be rendered as HTML or LaTeX. This is useful when writing about the use of colors in graphs or tables, where you want to print their names in their actual color to give a direct impression of the color, like â redâ shown in red, or â blueâ shown in blue.

Sequential and batch change detection for univariate data streams, using the change point model framework. Functions are provided to allow nonparametric distribution-free change detection in the mean, variance, or general distribution of a given sequence of observations. Parametric change detection methods are also provided for Gaussian, Bernoulli and Exponential sequences. Both the batch (Phase I) and sequential (Phase II) settings are supported, and the sequences may contain either a single or multiple change points. A full description of this package is available in Ross, G.J (2015) - "Parametric and nonparametric sequential change detection in R" available at <https://www.jstatsoft.org/article/view/v066i03>.

CHAP-GWAS (Chromosomal Haplotype-Integrated Genome-Wide Association Study) provides a dynamically adaptive framework for genome-wide association studies (GWAS) that integrates chromosome-scale haplotypes with single nucleotide polymorphism (SNP) analysis. The method identifies and extends haplotype variants based on their phenotypic associations rather than predefined linkage blocks, enabling high-resolution detection of quantitative trait loci (QTL). By leveraging long-range phased haplotype information, CHAP-GWAS improves statistical power and offers a more comprehensive view of the genetic architecture underlying complex traits.

Produce an averaging estimate/prediction by combining all candidate models for partial linear functional additive models, using multi-fold cross-validation criterion. More details can be referred to arXiv e-Prints via <doi:10.48550/arXiv.2105.00966>.

Connect to the California Irrigation Management Information System (CIMIS) Web API. See the CIMIS main page <https://cimis.water.ca.gov> and web API documentation <https://et.water.ca.gov> for more information.

Interface with and extract data from the United Nations Comtrade API <https://comtradeplus.un.org/>. Comtrade provides country level shipping data for a variety of commodities, these functions allow for easy API query and data returned as a tidy data frame.

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.

An R implementation of the Critical Path Method (CPM). CPM is a method used to estimate the minimum project duration and determine the amount of scheduling flexibility on the logical network paths within the schedule model. The flexibility is in terms of early start, early finish, late start, late finish, total float and free float. Beside, it permits to quantify the complexity of network diagram through the analysis of topological indicators. Finally, it permits to change the activities duration to perform what-if scenario analysis. The package was built based on following references: To make topological sorting and other graph operation, we use Csardi, G. & Nepusz, T. (2005) <https://www.researchgate.net/publication/221995787_The_Igraph_Software_Package_for_Complex_Network_Research>; For schedule concept, the reference was Project Management Institute (2017) <https://www.pmi.org/pmbok-guide-standards/foundational/pmbok>; For standards terms, we use Project Management Institute (2017) <https://www.pmi.org/pmbok-guide-standards/lexicon>; For algorithms on Critical Path Method development, we use Vanhoucke, M. (2013) <doi:10.1007/978-3-642-40438-2> and Vanhoucke, M. (2014) <doi:10.1007/978-3-319-04331-9>; And, finally, for topological definitions, we use Vanhoucke, M. (2009) <doi:10.1007/978-1-4419-1014-1>.

This package provides functions to perform the following analyses: i) inferring epistasis from RNAi double knockdown data; ii) identifying gene pairs of multiple mutation patterns; iii) assessing association between gene pairs and survival; and iv) calculating the smallworldness of a graph (e.g., a gene interaction network). Data and analyses are described in Wang, X., Fu, A. Q., McNerney, M. and White, K. P. (2014). Widespread genetic epistasis among breast cancer genes. Nature Communications. 5 4828. <doi:10.1038/ncomms5828>.

Counts colors within color range(s) in images, and provides a masked version of the image with targeted pixels changed to a different color. Output includes the locations of the pixels in the images, and the proportion of the image within the target color range with optional background masking. Users can specify multiple color ranges for masking.