This package provides nonparametric CUSUM tests for detecting changes in possibly serially dependent univariate or low-dimensional multivariate observations. Retrospective tests sensitive to changes in the expectation, the variance, the covariance, the autocovariance, the distribution function, Spearman's rho, Kendall's tau, Gini's mean difference, and the copula are provided, as well as a test for detecting changes in the distribution of independent block maxima (with environmental studies in mind). The package also contains a test sensitive to changes in the autocopula and a combined test of stationarity sensitive to changes in the distribution function and the autocopula. The latest additions are an open-end sequential test based on the retrospective CUSUM statistic that can be used for monitoring changes in the mean of possibly serially dependent univariate observations, as well as closed-end and open-end sequential tests based on empirical distribution functions that can be used for monitoring changes in the contemporary distribution of possibly serially dependent univariate or low-dimensional multivariate observations.

This package provides functions to compute the non-negative garrote estimator as proposed by Breiman (1995) <https://www.jstor.org/stable/1269730> with the penalized initial estimators extension as proposed by Yuan and Lin (2007) <https://www.jstor.org/stable/4623260>.

This package implements the navigated weighting (NAWT) proposed by Katsumata (2020) <arXiv:2005.10998>, which improves the inverse probability weighting by utilizing estimating equations suitable for a specific pre-specified parameter of interest (e.g., the average treatment effects or the average treatment effects on the treated) in propensity score estimation. It includes the covariate balancing propensity score proposed by Imai and Ratkovic (2014) <doi:10.1111/rssb.12027>, which uses covariate balancing conditions in propensity score estimation. The point estimate of the parameter of interest as well as coefficients for propensity score estimation and their uncertainty are produced using the M-estimation. The same functions can be used to estimate average outcomes in missing outcome cases.

This package provides a set of convenience functions as well as geographical/political data about Nigeria, aimed at simplifying work with data and information that are specific to the country.

This package provides a complete and seamless Nonmem simulation interface within R. Turns Nonmem control streams into simulation control streams, executes them with specified simulation input data and returns the results. The simulation is performed by Nonmem', eliminating manual work and risks of re-implementation of models in other tools.

Macros to generate nimble code from a concise syntax. Included are macros for generating linear modeling code using a formula-based syntax and for building for() loops. For more details review the nimble manual: <https://r-nimble.org/html_manual/cha-writing-models.html#subsec:macros>.

An estimation procedure for the analysis of nonparametric proportional hazards model (e.g. h(t) = h0(t)exp(b(t)'Z)), providing estimation of b(t) and its pointwise standard errors, and semiparametric proportional hazards model (e.g. h(t) = h0(t)exp(b(t)'Z1 + c*Z2)), providing estimation of b(t), c and their standard errors. More details can be found in Lu Tian et al. (2005) <doi:10.1198/016214504000000845>.

This package provides a non-parametric test for multi-observer concordance and differences between concordances in (un)balanced data.

Essentials for PK/PD (pharmacokinetics/pharmacodynamics) such as area under the curve, (geometric) coefficient of variation, and other calculations that are not part of base R. This is not a noncompartmental analysis (NCA) package.

Digital map data of Japan for choropleth mapping, including a circle cartogram.

Piecewise constant hazard functions are used to flexibly model survival distributions with non-proportional hazards and to simulate data from the specified distributions. A function to calculate weighted log-rank tests for the comparison of two hazard functions is included. Also, a function to calculate a test using the maximum of a set of test statistics from weighted log-rank tests (MaxCombo test) is provided. This test utilizes the asymptotic multivariate normal joint distribution of the separate test statistics. The correlation is estimated from the data. These methods are described in Ristl et al. (2021) <doi:10.1002/pst.2062>. Finally, a function is provided for the estimation and inferential statistics of various parameters that quantify the difference between two survival curves. Eligible parameters are differences in survival probabilities, log survival probabilities, complementary log log (cloglog) transformed survival probabilities, quantiles of the survival functions, log transformed quantiles, restricted mean survival times, as well as an average hazard ratio, the Cox model score statistic (logrank statistic), and the Cox-model hazard ratio. Adjustments for multiple testing and simultaneous confidence intervals are calculated using a multivariate normal approximation to the set of selected parameters.

Nonparametric efficiency measurement and statistical inference via DEA type estimators (see Färe, Grosskopf, and Lovell (1994) <doi:10.1017/CBO9780511551710>, Kneip, Simar, and Wilson (2008) <doi:10.1017/S0266466608080651> and Badunenko and Mozharovskyi (2020) <doi:10.1080/01605682.2019.1599778>) as well as Stochastic Frontier estimators for both cross-sectional data and 1st, 2nd, and 4th generation models for panel data (see Kumbhakar and Lovell (2003) <doi:10.1017/CBO9781139174411>, Badunenko and Kumbhakar (2016) <doi:10.1016/j.ejor.2016.04.049>). The stochastic frontier estimators can handle both half-normal and truncated normal models with conditional mean and heteroskedasticity. The marginal effects of determinants can be obtained.

The aim of neo2R is to provide simple and low level connectors for querying neo4j graph databases (<https://neo4j.com/>). The objects returned by the query functions are either lists or data.frames with very few post-processing. It allows fast processing of queries returning many records. And it let the user handle post-processing according to the data model and his needs.

This package provides several direct search optimization algorithms based on the simplex method. The provided algorithms are direct search algorithms, i.e. algorithms which do not use the derivative of the cost function. They are based on the update of a simplex. The following algorithms are available: the fixed shape simplex method of Spendley, Hext and Himsworth (unconstrained optimization with a fixed shape simplex, 1962) <doi:10.1080/00401706.1962.10490033>, the variable shape simplex method of Nelder and Mead (unconstrained optimization with a variable shape simplex made, 1965) <doi:10.1093/comjnl/7.4.308>, and Box's complex method (constrained optimization with a variable shape simplex, 1965) <doi: 10.1093/comjnl/8.1.42>.

Simulate DNA sequences for the node substitution model. In the node substitution model, substitutions accumulate additionally during a speciation event, providing a potential mechanistic explanation for substitution rate variation. This package provides tools to simulate such a process, simulate a reference process with only substitutions along the branches, and provides tools to infer phylogenies from alignments. More information can be found in Janzen (2021) <doi:10.1093/sysbio/syab085>.

Semissupervised model for geographical document classification (Watanabe 2018) <doi:10.1080/21670811.2017.1293487>. This package currently contains seed dictionaries in English, German, French, Spanish, Italian, Russian, Hebrew, Arabic, Turkish, Japanese and Chinese (Simplified and Traditional).

This package provides functions to flash your hue lights, or text yourself, from R. Designed to be used with long running scripts.

This package implements several nonparametric regression approaches for the inclusion of covariate information on the receiver operating characteristic (ROC) framework.

This package provides a collection of data structures and methods for handling volumetric brain imaging data, with a focus on functional magnetic resonance imaging (fMRI). Provides efficient representations for three-dimensional and four-dimensional neuroimaging data through sparse and dense array implementations, memory-mapped file access for large datasets, and spatial transformation capabilities. Implements methods for image resampling, spatial filtering, region of interest analysis, and connected component labeling. General introduction to fMRI analysis can be found in Poldrack et al. (2024, "Handbook of functional MRI data analysis", <ISBN:9781108795760>).

Access the New Zealand Freshwater Fish Database from R and a few functions to clean the data once in R.

Researchers often want to evaluate whether there is a negligible relationship among variables. The negligible package provides functions that are useful for conducting negligible effect testing (also called equivalence testing). For example, there are functions for evaluating the equivalence of means or the presence of a negligible association (correlation or regression). Beribisky, N., Mara, C., & Cribbie, R. A. (2020) <doi:10.20982/tqmp.16.4.p424>. Beribisky, N., Davidson, H., Cribbie, R. A. (2019) <doi:10.7717/peerj.6853>. Shiskina, T., Farmus, L., & Cribbie, R. A. (2018) <doi:10.20982/tqmp.14.3.p167>. Mara, C. & Cribbie, R. A. (2017) <doi:10.1080/00220973.2017.1301356>. Counsell, A. & Cribbie, R. A. (2015) <doi:10.1111/bmsp.12045>. van Wieringen, K. & Cribbie, R. A. (2014) <doi:10.1111/bmsp.12015>. Goertzen, J. R. & Cribbie, R. A. (2010) <doi:10.1348/000711009x475853>. Cribbie, R. A., Gruman, J. & Arpin-Cribbie, C. (2004) <doi:10.1002/jclp.10217>.

Get or set UNIX priority (niceness) of running R process.

This package provides efficient implementation of the Narrowest-Over-Threshold methodology for detecting an unknown number of change-points occurring at unknown locations in one-dimensional data following deterministic signal + noise model. Currently implemented scenarios are: piecewise-constant signal, piecewise-constant signal with a heavy-tailed noise, piecewise-linear signal, piecewise-quadratic signal, piecewise-constant signal and with piecewise-constant variance of the noise. For details, see Baranowski, Chen and Fryzlewicz (2019) <doi:10.1111/rssb.12322>.

This package performs nonlinear Invariant Causal Prediction to estimate the causal parents of a given target variable from data collected in different experimental or environmental conditions, extending Invariant Causal Prediction from Peters, Buehlmann and Meinshausen (2016), <arXiv:1501.01332>, to nonlinear settings. For more details, see C. Heinze-Deml, J. Peters and N. Meinshausen: Invariant Causal Prediction for Nonlinear Models', <arXiv:1706.08576>.