This package provides color palettes based on crayon colors since the early 1900s. Colors are based on various crayon colors, sets, and promotional palettes, most of which can be found at <https://en.wikipedia.org/wiki/List_of_Crayola_crayon_colors>. All palettes are discrete palettes and are not necessarily color-blind friendly. Provides scales for ggplot2 for discrete coloring.

This package performs forward and backward stepwise regression for the proportional subdistribution hazards model in competing risks (Fine & Gray 1999). Procedure uses AIC, BIC and BICcr as selection criteria. BICcr has a penalty of k = log(n*), where n* is the number of primary events. This version includes improved handling of factors, interactions, and polynomial terms.

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.

Constrained quantile regression is performed. One constraint is that all beta coefficients (including the constant) cannot be negative, they can be either 0 or strictly positive. Another constraint is that the beta coefficients lie within an interval. References: Koenker R. (2005) Quantile Regression, Cambridge University Press. <doi:10.1017/CBO9780511754098>.

This package provides a tool for analyzing conjoint experiments using Bayesian Additive Regression Trees ('BART'), a machine learning method developed by Chipman, George and McCulloch (2010) <doi:10.1214/09-AOAS285>. This tool focuses specifically on estimating, identifying, and visualizing the heterogeneity within marginal component effects, at the observation- and individual-level. It uses a variable importance measure ('VIMP') with delete-d jackknife variance estimation, following Ishwaran and Lu (2019) <doi:10.1002/sim.7803>, to obtain bias-corrected estimates of which variables drive heterogeneity in the predicted individual-level effects.

This software package provides Cox survival analysis for high-dimensional and multiblock datasets. It encompasses a suite of functions dedicated from the classical Cox regression to newest analysis, including Cox proportional hazards model, Stepwise Cox regression, and Elastic-Net Cox regression, Sparse Partial Least Squares Cox regression (sPLS-COX) incorporating three distinct strategies, and two Multiblock-PLS Cox regression (MB-sPLS-COX) methods. This tool is designed to adeptly handle high-dimensional data, and provides tools for cross-validation, plot generation, and additional resources for interpreting results. While references are available within the corresponding functions, key literature is mentioned below. Terry M Therneau (2024) <https://CRAN.R-project.org/package=survival>, Noah Simon et al. (2011) <doi:10.18637/jss.v039.i05>, Philippe Bastien et al. (2005) <doi:10.1016/j.csda.2004.02.005>, Philippe Bastien (2008) <doi:10.1016/j.chemolab.2007.09.009>, Philippe Bastien et al. (2014) <doi:10.1093/bioinformatics/btu660>, Kassu Mehari Beyene and Anouar El Ghouch (2020) <doi:10.1002/sim.8671>, Florian Rohart et al. (2017) <doi:10.1371/journal.pcbi.1005752>.

This package performs regression analysis for longitudinal count data, allowing for serial dependence among observations from a given individual and two dimensional random effects on the linear predictor. Estimation is via maximization of the exact likelihood of a suitably defined model. Missing values and unbalanced data are allowed. Details can be found in the accompanying scientific papers: Goncalves & Cabral (2021, Journal of Statistical Software, <doi:10.18637/jss.v099.i03>) and Goncalves et al. (2007, Computational Statistics & Data Analysis, <doi:10.1016/j.csda.2007.03.002>).

Frequentist statistical inference for cluster randomised trials with multiple outcomes that controls the family-wise error rate and provides nominal coverage of confidence sets. A full description of the methods can be found in Watson et al. (2023) <doi:10.1002/sim.9831>.

These experimental expression data (5 leukemic CLL B-lymphocyte of aggressive form from GSE39411', <doi:10.1073/pnas.1211130110>), after B-cell receptor stimulation, are used as examples by packages such as the Cascade one, 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>.

Estimate sample sizes needed to capture target levels of genetic diversity from a population (multivariate allele frequencies) for applications like germplasm conservation and breeding efforts. Compares bootstrap samples to a full population using linear regression, employing the R-squared value to represent the proportion of diversity captured. Iteratively increases sample size until a user-defined target R-squared is met. Offers a parallelized R implementation of a previously developed python method. All ploidy levels are supported. For more details, see Sandercock et al. (2024) <doi:10.1073/pnas.2403505121>.

This package contains the adaptation of bubblebath from MATLAB', developed by Adam Danz and available through the MATLAB Central File Exchange, and the tools to transform a dataframe of radii and points to plot-able paths.

Domain mean estimation with monotonicity or block monotone constraints. See Xu X, Meyer MC and Opsomer JD (2021)<doi:10.1016/j.jspi.2021.02.004> for more details.

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.

This package provides a new methodology for linear regression with both curve response and curve regressors, which is described in Cho, Goude, Brossat and Yao (2013) <doi:10.1080/01621459.2012.722900> and (2015) <doi:10.1007/978-3-319-18732-7_3>. The key idea behind this methodology is dimension reduction based on a singular value decomposition in a Hilbert space, which reduces the curve regression problem to several scalar linear regression problems.

Automated flagging of common spatial and temporal errors in biological and paleontological collection data, for the use in conservation, ecology and paleontology. Includes automated tests to easily flag (and exclude) records assigned to country or province centroid, the open ocean, the headquarters of the Global Biodiversity Information Facility, urban areas or the location of biodiversity institutions (museums, zoos, botanical gardens, universities). Furthermore identifies per species outlier coordinates, zero coordinates, identical latitude/longitude and invalid coordinates. Also implements an algorithm to identify data sets with a significant proportion of rounded coordinates. Especially suited for large data sets. The reference for the methodology is: Zizka et al. (2019) <doi:10.1111/2041-210X.13152>.

Circumplex models, which organize constructs in a circle around two underlying dimensions, are popular for studying interpersonal functioning, mood/affect, and vocational preferences/environments. This package provides tools for analyzing and visualizing circular data, including scoring functions for relevant instruments and a generalization of the bootstrapped structural summary method from Zimmermann & Wright (2017) <doi:10.1177/1073191115621795> and functions for creating publication-ready tables and figures from the results.

Auto, Cross and Multi-dimensional recurrence quantification analysis. Different methods for computing recurrence, cross vs. multidimensional or profile iti.e., only looking at the diagonal recurrent points, as well as functions for optimization and plotting are proposed. in-depth measures of the whole cross-recurrence plot, Please refer to Coco and others (2021) <doi:10.32614/RJ-2021-062>, Coco and Dale (2014) <doi:10.3389/fpsyg.2014.00510> and Wallot (2018) <doi: 10.1080/00273171.2018.1512846> for further details about the method.

This package contains selected variables from the time series profiles for statistical areas level 2 from the 2006, 2011, and 2016 censuses of population and housing, Australia. Also provides methods for viewing the questions asked for convenience during analysis.

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.

Iterate and repel visually similar colors away in various ggplot2 plots. When many groups are plotted at the same time on multiple axes, for instance stacked bars or scatter plots, effectively ordering colors becomes difficult. This tool iterates through color combinations to find the best solution to maximize visual distinctness of nearby groups, so plots are more friendly toward colorblind users. This is achieved by two distance measurements, distance between groups within the plot, and CIELAB color space distances between colors as described in Carter et al., (2018) <doi:10.25039/TR.015.2018>.

This package implements the expectation-maximization (EM) algorithm as described in Fiksel et al. (2022) <doi:10.1111/biom.13465> for transformation-free linear regression for compositional outcomes and predictors.

Predict Scope 1, 2 and 3 carbon emissions for UK Small and Medium-sized Enterprises (SMEs), using Standard Industrial Classification (SIC) codes and annual turnover data, as well as Scope 1 carbon emissions for UK farms. The carbonpredict package provides single and batch prediction, plotting, and workflow tools for carbon accounting and reporting. The package utilises pre-trained models, leveraging rich classified transaction data to accurately predict Scope 1, 2 and 3 carbon emissions for UK SMEs as well as identifying emissions hotspots. It also provides Scope 1 carbon emissions predictions for UK farms of types: Cereals ex. rice, Dairy, Mixed farming, Sheep and goats, Cattle & buffaloes, Poultry, Animal production and Support for crop production. The methodology used to produce the estimates in this package is fully detailed in the following peer-reviewed publications: Phillpotts, A., Owen. A., Norman, J., Trendl, A., Gathergood, J., Jobst, Norbert., Leake, D. (2025) <doi:10.1111/jiec.70106> "Bridging the SME Reporting Gap: A New Model for Predicting Scope 1 and 2 Emissions" and Wells, J., Trendl, A., Owen, A., Barrett, J., Gridley, J., Jobst, N., Leake, D. (2025) <doi:10.1088/1748-9326/ae20ab> "A Scalable Tool for Farm-Level Carbon Accounting: Evidence from UK Agriculture".

Various cladogenesis-related calculations that are slow in pure R are implemented in C++ with Rcpp. These include the calculation of the probability of various scenarios for the inheritance of geographic range at the divergence events on a phylogenetic tree, and other calculations necessary for models which are not continuous-time markov chains (CTMC), but where change instead occurs instantaneously at speciation events. Typically these models must assess the probability of every possible combination of (ancestor state, left descendent state, right descendent state). This means that there are up to (# of states)^3 combinations to investigate, and in biogeographical models, there can easily be hundreds of states, so calculation time becomes an issue. C++ implementation plus clever tricks (many combinations can be eliminated a priori) can greatly speed the computation time over naive R implementations. CITATION INFO: This package is the result of my Ph.D. research, please cite the package if you use it! Type: citation(package="cladoRcpp") to get the citation information.

Adjusts the loglikelihood of common econometric models for clustered data based on the estimation process suggested in Chandler and Bate (2007) <doi:10.1093/biomet/asm015>, using the chandwich package <https://cran.r-project.org/package=chandwich>, and provides convenience functions for inference on the adjusted models.