Calculates the minimal sample size for the Wilcoxon-Mann-Whitney test that is needed for a given power and two sided type I error rate. The method works for metric data with and without ties, count data, ordered categorical data, and even dichotomous data. But data is needed for the reference group to generate synthetic data for the treatment group based on a relevant effect. See Happ et al. (2019, <doi:10.1002/sim.7983>) for details.

R clients to the Web of Science and InCites <https://clarivate.com/products/data-integration/> APIs, which allow you to programmatically download publication and citation data indexed in the Web of Science and InCites databases.

Run mixed-effects models that include weights at every level. The WeMix package fits a weighted mixed model, also known as a multilevel, mixed, or hierarchical linear model (HLM). The weights could be inverse selection probabilities, such as those developed for an education survey where schools are sampled probabilistically, and then students inside of those schools are sampled probabilistically. Although mixed-effects models are already available in R, WeMix is unique in implementing methods for mixed models using weights at multiple levels. Both linear and logit models are supported. Models may have up to three levels. Random effects are estimated using the PIRLS algorithm from lme4pureR (Walker and Bates (2013) <https://github.com/lme4/lme4pureR>).

Import WIG data into R in long format.

Computationally easy modeling, interpolation, forecasting of massive temporal-spacial data.

Power calculator for the two-sample Wilcoxon-Mann-Whitney rank-sum test for a continuous outcome (Mollan, Trumble, Reifeis et. al., Mar. 2020) <doi:10.1080/10543406.2020.1730866> <arXiv:1901.04597>, (Mann and Whitney 1947) <doi:10.1214/aoms/1177730491>, (Shieh, Jan, and Randles 2006) <doi:10.1080/10485250500473099>.

This package provides a computationally efficient way of fitting weighted linear fixed effects estimators for causal inference with various weighting schemes. Weighted linear fixed effects estimators can be used to estimate the average treatment effects under different identification strategies. This includes stratified randomized experiments, matching and stratification for observational studies, first differencing, and difference-in-differences. The package implements methods described in Imai and Kim (2017) "When should We Use Linear Fixed Effects Regression Models for Causal Inference with Longitudinal Data?", available at <https://imai.fas.harvard.edu/research/FEmatch.html>.

The distributions of the weight of evidence (log Bayes factor) favouring case over noncase status in a test dataset (or test folds generated by cross-validation) can be used to quantify the performance of a diagnostic test (McKeigue (2019), <doi:10.1177/0962280218776989>). The package can be used with any test dataset on which you have observed case-control status and have computed prior and posterior probabilities of case status using a model learned on a training dataset. To quantify how the predictor will behave as a risk stratifier, the quantiles of the distributions of weight of evidence in cases and controls can be calculated and plotted.

Converts pathways from WikiPathways GPML format or KEGG KGML format into igraph objects. Includes tools to find all cycles in the resulting graphs and determine which ones involve negative feedback (inhibition).

This package performs 1, 2 and 3D real and complex-valued wavelet transforms, nondecimated transforms, wavelet packet transforms, nondecimated wavelet packet transforms, multiple wavelet transforms, complex-valued wavelet transforms, wavelet shrinkage for various kinds of data, locally stationary wavelet time series, nonstationary multiscale transfer function modeling, density estimation.

Builds a joint probabilistic forecast across series and horizons using adaptive copulas (Gaussian/t) with shrinkage-repaired correlations. At the low level it calls a probabilistic mixer per series and horizon, which backtests several simple predictors, predicts next-window Continuous Ranked Probability Score (CRPS), and converts those scores into softmax weights to form a calibrated mixture (r/q/p/dfun). The mixer blends eight simple predictors: a naive predictor that wraps the last move in a PERT distribution; an arima predictor using auto.arima for one-step forecasts; an Exponentially Weighted Moving Average (EWMA) gaussian predictor with mean/variance under a Gaussian; a historical bootstrap predictor that resamples past horizon-aligned moves; a drift residual bootstrap predictor combining linear trend with bootstrapped residuals; a volatility-scaled naive predictor centering on the last move and scaling by recent volatility; a robust median mad predictor using median/MAD with Laplace or Normal shape; and a shrunk quantile predictor that fits a few quantile regressions over time and interpolates to a full predictive. The function then couples the per-series mixtures on a common transform (additive/multiplicative/log-multiplicative), simulates coherent draws, and returns both transformed- and level-scale samplers and summaries.

Read Quake assets including bitmap images and textures in wal file format. This package also provides support for extracting these assets from WAD and PAK file archives. It can also read models in MDL and MD2 formats.

This package provides a collection of color palettes that were extracted from various books on my sons(Wren) bookshelf. Also included are a number of functions and wrappers to utilize them, as well as to subset the palettes to desired number/specific colors.

This package provides a series of tools to compute and plot quantities related to classical and robust wavelet variance for time series and regular lattices. More details can be found, for example, in Serroukh, A., Walden, A.T., & Percival, D.B. (2000) <doi:10.2307/2669537> and Guerrier, S. & Molinari, R. (2016) <doi:10.48550/arXiv.1607.05858>.

Estimation of observation-specific weights for incomplete longitudinal data and bootstrap procedure for weighted quantile regressions. See Jacqmin-Gadda, Rouanet, Mba, Philipps, Dartigues (2020) for details <doi:10.1177/0962280220909986>.

This package provides a clean syntax for vectorising the use of Non-Standard Evaluation (NSE), for example in ggplot2', dplyr', or data.table'.

This package provides a prediction model is calibrated if, roughly, for any percentage x we can expect that x subjects out of 100 experience the event among all subjects that have a predicted risk of x%. A calibration plot provides a simple, yet useful, way of assessing the calibration assumption. The Wally plot consists of a sequence of usual calibration plots. Among the plots contained within the sequence, one is the actual calibration plot which has been obtained from the data and the others are obtained from similar simulated data under the calibration assumption. It provides the investigator with a direct visual understanding of the shape and sampling variability that are common under the calibration assumption. The original calibration plot from the data is included randomly among the simulated calibration plots, similarly to a police lineup. If the original calibration plot is not easily identified then the calibration assumption is not contradicted by the data. The method handles the common situations in which the data contain censored observations and occurrences of competing events.

Computes inequality measures of a given variable taking into account weights. Suitable for ratio, interval and ordered scale. Includes Gini, Theil, Leti index, Palma ratio, 20:20 ratio, Allison and Foster index, Jenkins index, Cowell and Flechaire index, Abul Naga and Yalcin index, Apouey index, Blair and Lacy index. Bootstrap provides distribution of inequality measures enabling significance tests.

This package provides a framework for developing n-gram models for text prediction. It provides data cleaning, data sampling, extracting tokens from text, model generation, model evaluation and word prediction. For information on how n-gram models work we referred to: "Speech and Language Processing" <https://web.archive.org/web/20240919222934/https%3A%2F%2Fweb.stanford.edu%2F~jurafsky%2Fslp3%2F3.pdf>. For optimizing R code and using R6 classes we referred to "Advanced R" <https://adv-r.hadley.nz/r6.html>. For writing R extensions we referred to "R Packages", <https://r-pkgs.org/index.html>.

Fast computation of Receiver Operating Characteristic (ROC) curves and Area Under the Curve (AUC) for weighted binary classification problems (weights are example-specific cost values).

This package provides a not uncommon task for quants is to create waterfall charts'. There seems to be no simple way to do this in ggplot2 currently. This package contains a single function (waterfall) that simply draws a waterfall chart in a ggplot2 object. Some flexibility is provided, though often the object created will need to be modified through a theme.

Application to estimate statistical values using properties provided by a group of individuals to describe concepts using shiny'. It estimates the underlying distribution to generate new descriptive words Canessa et al. (2023) <doi:10.3758/s13428-022-01811-w>, applies a new clustering model, and uses simulations to estimate the probability that two persons describe the same words based on their descriptions Canessa et al. (2022) <doi:10.3758/s13428-022-02030-z>.

The weighted ensemble method is a valuable approach for combining forecasts. This algorithm employs several optimization techniques to generate optimized weights. This package has been developed using algorithm of Armstrong (1989) <doi:10.1016/0024-6301(90)90317-W>.

The weighted scores method and composite likelihood information criteria as an intermediate step for variable/correlation selection for longitudinal ordinal and count data in Nikoloulopoulos, Joe and Chaganty (2011) <doi:10.1093/biostatistics/kxr005>, Nikoloulopoulos (2016) <doi:10.1002/sim.6871> and Nikoloulopoulos (2017) <arXiv:1510.07376>.