This package provides a multiple testing procedure for clustered alternative hypotheses. It is assumed that the p-values under the null hypotheses follow U(0,1) and that the distributions of p-values from the alternative hypotheses are stochastically smaller than U(0,1). By aggregating information, this method is more sensitive to detecting signals of low magnitude than standard methods. Additionally, sporadic small p-values appearing within a null hypotheses sequence are avoided by averaging on the neighboring p-values.

This package contains some commonly used categorical variable encoders, such as LabelEncoder and OneHotEncoder'. Inspired by the encoders implemented in Python sklearn.preprocessing package (see <http://scikit-learn.org/stable/modules/preprocessing.html>).

Recent developments in modern coexistence theory have advanced our understanding on how species are able to persist and co-occur with other species at varying abundances. However, applying this mathematical framework to empirical data is still challenging, precluding a larger adoption of the theoretical tools developed by empiricists. This package provides a complete toolbox for modelling interaction effects between species, and calculate fitness and niche differences. The functions are flexible, may accept covariates, and different fitting algorithms can be used. A full description of the underlying methods is available in Garcà a-Callejas, D., Godoy, O., and Bartomeus, I. (2020) <doi:10.1111/2041-210X.13443>. Furthermore, the package provides a series of functions to calculate dynamics for stage-structured populations across sites.

Contrast trees represent a new approach for assessing the accuracy of many types of machine learning estimates that are not amenable to standard (cross) validation methods; see "Contrast trees and distribution boosting", Jerome H. Friedman (2020) <doi:10.1073/pnas.1921562117>. In situations where inaccuracies are detected, boosted contrast trees can often improve performance. Functions are provided to to build such trees in addition to a special case, distribution boosting, an assumption free method for estimating the full probability distribution of an outcome variable given any set of joint input predictor variable values.

Utilizes spatial association marginal contributions derived from spatial stratified heterogeneity to capture the degree of correlation between spatial patterns.

Fits convolution-based nonstationary Gaussian process models to point-referenced spatial data. The nonstationary covariance function allows the user to specify the underlying correlation structure and which spatial dependence parameters should be allowed to vary over space: the anisotropy, nugget variance, and process variance. The parameters are estimated via maximum likelihood, using a local likelihood approach. Also provided are functions to fit stationary spatial models for comparison, calculate the Kriging predictor and standard errors, and create various plots to visualize nonstationarity.

Connect and pull data from the CJA API, which powers CJA Workspace <https://github.com/AdobeDocs/cja-apis>. The package was developed with the analyst in mind and will continue to be developed with the guiding principles of iterative, repeatable, timely analysis. New features are actively being developed and we value your feedback and contribution to the process.

Employs a two-parameter family of distributions for modelling random variables on the (0, 1) interval by applying the cumulative distribution function (cdf) of one parent distribution to the quantile function of another.

This package contains functions to estimate a smoothed and a non-smoothed (empirical) time-dependent receiver operating characteristic curve and the corresponding area under the receiver operating characteristic curve and the optimal cutoff point for the right and interval censored survival data. See Beyene and El Ghouch (2020)<doi:10.1002/sim.8671> and Beyene and El Ghouch (2022) <doi:10.1002/bimj.202000382>.

Facilitates the identification of counterfactual queries in structural causal models via the ID* and IDC* algorithms by Shpitser, I. and Pearl, J. (2007, 2008) <doi:10.48550/arXiv.1206.5294>, <https://jmlr.org/papers/v9/shpitser08a.html>. Provides a simple interface for defining causal diagrams and counterfactual conjunctions. Construction of parallel worlds graphs and counterfactual graphs is carried out automatically based on the counterfactual query and the causal diagram. See Tikka, S. (2023) <doi:10.32614/RJ-2023-053> for a tutorial of the package.

This package implements non-parametric analyses for clustered binary and multinomial data. The elements of the cluster are assumed exchangeable, and identical joint distribution (also known as marginal compatibility, or reproducibility) is assumed for clusters of different sizes. A trend test based on stochastic ordering is implemented. Szabo A, George EO. (2010) <doi:10.1093/biomet/asp077>; George EO, Cheon K, Yuan Y, Szabo A (2016) <doi:10.1093/biomet/asw009>.

This package implements weighted estimation in Cox regression as proposed by Schemper, Wakounig and Heinze (Statistics in Medicine, 2009, <doi:10.1002/sim.3623>) and as described in Dunkler, Ploner, Schemper and Heinze (Journal of Statistical Software, 2018, <doi:10.18637/jss.v084.i02>). Weighted Cox regression provides unbiased average hazard ratio estimates also in case of non-proportional hazards. Approximated generalized concordance probability an effect size measure for clear-cut decisions can be obtained. The package provides options to estimate time-dependent effects conveniently by including interactions of covariates with arbitrary functions of time, with or without making use of the weighting option.

Many modern C/C++ development tools in the clang toolchain, such as clang-tidy or clangd', rely on the presence of a compilation database in JSON format <https://clang.llvm.org/docs/JSONCompilationDatabase.html>. This package temporarily injects additional build flags into the R build process to generate such a compilation database.

This package contains the prepared data that is needed for the shiny application examples in the canvasXpress package. This package also includes datasets used for automated testthat tests. Scotto L, Narayan G, Nandula SV, Arias-Pulido H et al. (2008) <doi:10.1002/gcc.20577>. Davis S, Meltzer PS (2007) <doi:10.1093/bioinformatics/btm254>.

This package provides functions for computing the one-sided p-values of the Cochran-Armitage trend test statistic for the asymptotic and the exact conditional test. The computation of the p-value for the exact test is performed using an algorithm following an idea by Mehta, et al. (1992) <doi:10.2307/1390598>.

Estimates sugar beet canopy closure with remotely sensed leaf area index and estimates when action might be needed to protect the crop from a Leaf Spot epidemic with a negative prognosis model based on published models.

Perform Nonlinear Mixed-Effects (NLME) Modeling using Certara's NLME-Engine. Access the same Maximum Likelihood engines used in the Phoenix platform, including algorithms for parametric methods, individual, and pooled data analysis. The Quasi-Random Parametric Expectation-Maximization Method (QRPEM) is also supported <https://www.page-meeting.org/default.asp?abstract=2338>. Execution is supported both locally or on remote machines. Remote execution includes support for Linux Sun Grid Engine (SGE), Simple Linux Utility for Resource Management (SLURM) grids, Linux and Windows multicore, and individual runs.

This package provides functions for predictor pruning using association-based and model-based approaches. Includes corrPrune() for fast correlation-based pruning, modelPrune() for VIF-based regression pruning, and exact graph-theoretic algorithms (Eppsteinâ Löfflerâ Strash, Bronâ Kerbosch) for exhaustive subset enumeration. Supports linear models, GLMs, and mixed models ('lme4', glmmTMB').

Learning the structure of graphical models from datasets with thousands of variables. More information about the research papers detailing the theory behind Chordalysis is available at <http://www.francois-petitjean.com/Research> (KDD 2016, SDM 2015, ICDM 2014, ICDM 2013). The R package development site is <https://github.com/HerrmannM/Monash-ChoR>.

Nonparametric two-sample procedure for comparing survival quantiles.

This package provides a graphical user interface for simulating the effects of mergers, tariffs, and quotas under an assortment of different economic models. The interface is powered by the Shiny web application framework from RStudio'.

Computes the center of gravity (COG) of character-like binary images using three different methods. This package provides functions for estimating stroke-based, contour-based, and potential energy-based COG. It is useful for analyzing glyph structure in areas such as visual cognition research and font development. The contour-based method was originally proposed by Kotani et al. (2004) <https://ipsj.ixsq.nii.ac.jp/records/36793> and Kotani (2011) <https://shonan-it.repo.nii.ac.jp/records/2000243>, while the potential energy-based method was introduced by Kotani et al. (2006) <doi:10.11371/iieej.35.296>.

Java JAR files for the Apache Commons Mathematics Library for use by users and other packages.

Combining Univariate Association Test Results of Multiple Phenotypes for Detecting Pleiotropy.