Use Pokemon(R) inspired palettes with additional ggplot2 scales. Palettes are the colours in each Pokemon's sprite, ordered by how common they are in the image. The first 386 Pokemon are currently provided.

Conduct simulation-based customized power calculation for clustered time to event data in a mixed crossed/nested design, where a number of cell lines and a number of mice within each cell line are considered to achieve a desired statistical power, motivated by Eckel-Passow and colleagues (2021) <doi:10.1093/neuonc/noab137> and Li and colleagues (2025) <doi:10.51387/25-NEJSDS76>. This package provides two commonly used models for powering a design, linear mixed effects and Cox frailty model. Both models account for within-subject (cell line) correlation while holding different distributional assumptions about the outcome. Alternatively, the counterparts of fixed effects model are also available, which produces similar estimates of statistical power.

This package contains statistical inference tools applied to Partial Linear Regression (PLR) models. Specifically, point estimation, confidence intervals estimation, bandwidth selection, goodness-of-fit tests and analysis of covariance are considered. Kernel-based methods, combined with ordinary least squares estimation, are used and time series errors are allowed. In addition, these techniques are also implemented for both parametric (linear) and nonparametric regression models.

Compute personal values scores from various questionnaires based on the theoretical constructs proposed by professor Shalom H. Schwartz. Designed for researchers and practitioners in psychology, sociology, and related fields, the package facilitates the quantification and visualization of different dimensions related to personal values from survey data. It incorporates the recommended statistical adjustment to enhance the accuracy and interpretation of the results.

Power analysis for AB testing. The calculations are based on the Welch's unequal variances t-test, which is generally preferred over the Student's t-test when sample sizes and variances of the two groups are unequal, which is frequently the case in AB testing. In such situations, the Student's t-test will give biased results due to using the pooled standard deviation, unlike the Welch's t-test.

This package provides functions to automatically build a directory structure for a new R project. Using this structure, ProjectTemplate automates data loading, preprocessing, library importing and unit testing.

This package implements the American Heart Association Predicting Risk of cardiovascular disease EVENTs (PREVENT) equations from Khan SS, Matsushita K, Sang Y, and colleagues (2023) <doi:10.1161/CIRCULATIONAHA.123.067626>, with optional comparison with their de facto predecessor, the Pooled Cohort Equations from the American Heart Association and American College of Cardiology (2013) <doi:10.1161/01.cir.0000437741.48606.98> and the revision to the Pooled Cohort Equations from Yadlowsky and colleagues (2018) <doi:10.7326/M17-3011>.

This package provides a testing workbench to evaluate tools that calculate precision-recall curves. Saito and Rehmsmeier (2015) <doi:10.1371/journal.pone.0118432>.

Efficient calculation of pseudo-ranks and (pseudo)-rank based test statistics. In case of equal sample sizes, pseudo-ranks and mid-ranks are equal. When used for inference mid-ranks may lead to paradoxical results. Pseudo-ranks are in general not affected by such a problem. See Happ et al. (2020, <doi:10.18637/jss.v095.c01>) for details.

Fit linear splines to species time series to detect population growth scenarios based on Hyndman, R J and Mesgaran, M B and Cousens, R D (2015) <doi:10.1007/s10530-015-0962-8>.

This package provides functions to get prediction intervals and prediction points of future observations from any continuous distribution.

This package provides functions for conducting power analysis in ANOVA designs, including between-, within-, and mixed-factor designs, with full support for both main effects and interactions. The package allows calculation of statistical power, required total sample size, significance level, and minimal detectable effect sizes expressed as partial eta squared or Cohen's f for ANOVA terms and planned contrasts. In addition, complementary functions are included for common related tests such as t-tests and correlation tests, making the package a convenient toolkit for power analysis in experimental psychology and related fields.

Evaluate the predictive performance of an existing (i.e. previously developed) prediction/ prognostic model given relevant information about the existing prediction model (e.g. coefficients) and a new dataset. Provides a range of model updating methods that help tailor the existing model to the new dataset; see Su et al. (2018) <doi:10.1177/0962280215626466>. Techniques to aggregate multiple existing prediction models on the new data are also provided; see Debray et al. (2014) <doi:10.1002/sim.6080> and Martin et al. (2018) <doi:10.1002/sim.7586>).

The primary goal of phase I clinical trials is to find the maximum tolerated dose (MTD). To reach this objective, we introduce a new design for phase I clinical trials, the posterior predictive (PoP) design. The PoP design is an innovative model-assisted design that is as simply as the conventional algorithmic designs as its decision rules can be pre-tabulated prior to the onset of trial, but is of more flexibility of selecting diverse target toxicity rates and cohort sizes. The PoP design has desirable properties, such as coherence and consistency. Moreover, the PoP design provides better empirical performance than the BOIN and Keyboard design with respect to high average probabilities of choosing the MTD and slightly lower risk of treating patients at subtherapeutic or overly toxic doses.

This package provides a function kitten() which creates cute little packages which pass R package checks. This sets it apart from package.skeleton() which it calls, and which leaves imperfect files behind. As this is not exactly helpful for beginners, kitten() offers an alternative. Unit test support can be added via the tinytest package (if present), and documentation-creation support can be added via roxygen2 (if present).

This package provides a suite of diagnostic tools for univariate point processes. This includes tools for simulating and fitting both common and more complex temporal point processes. We also include functions to visualise these point processes and collect existing diagnostic tools of Brown et al. (2002) <doi:10.1162/08997660252741149> and Wu et al. (2021) <doi:10.1002/9781119821588.ch7>, which can be used to assess the fit of a chosen point process model.

This package provides functionality to support data preparation and exploration for palaeobiological analyses, improving code reproducibility and accessibility. The wider aim of palaeoverse is to bring the palaeobiological community together to establish agreed standards. The package currently includes functionality for data cleaning, binning (time and space), exploration, summarisation and visualisation. Reference datasets (i.e. Geological Time Scales <https://stratigraphy.org/chart/>) and auxiliary functions are also provided. Details can be found in: Jones et al., (2023) <doi: 10.1111/2041-210X.14099>.

This package provides a small, dependency-free way to generate random names. Methods provided include the adjective-surname approach of Docker containers ('<https://github.com/moby/moby/blob/master/pkg/namesgenerator/names-generator.go>'), and combinations of common English or Spanish words.

Density, distribution function, quantile function and random generation for the family of power and reversal power distributions.

Compute detailed and aggregated performance spectrum for event data. The detailed performance spectrum describes the event data in terms of segments, where the performance of each segment is measured and plotted for any occurrences of this segment over time and can be classified, e.g., regarding the overall population. The aggregated performance spectrum visualises the amount of cases of particular performance over time. Denisov, V., Fahland, D., & van der Aalst, W. M. P. (2018) <doi:10.1007/978-3-319-98648-7_9>.

An interactive shiny application for Health Technology Assessment (HTA) parameter estimation. Converts between rates, probabilities, odds, and hazard ratios (HR); extrapolates survival curves (Exponential, Weibull, Log-Logistic); fits probabilistic sensitivity analysis (PSA) distributions (Beta, Gamma, LogNormal, Dirichlet) via the method of moments; calculates incremental cost-effectiveness ratios (ICERs), net monetary benefit (NMB), value-based pricing, and budget impact; adjusts costs for inflation, discounting, and purchasing power parity (PPP) across 30 countries; and adjusts background mortality using life-table methods. Designed for researchers building cost-effectiveness and budget-impact models who need auditable, formula-documented parameter transformations. Methods include Zhang and Yu (1998) <doi:10.1001/jama.280.19.1690> for odds ratio (OR) to relative risk (RR) conversion and Chinn (2000, Statistics in Medicine, 19, 3127-3131) for effect-size transformations.

Creation of linkage maps in polyploid species from marker dosage scores of an F1 cross from two heterozygous parents. Currently works for outcrossing diploid, autotriploid, autotetraploid and autohexaploid species, as well as segmental allotetraploids. Methods are described in a manuscript of Bourke et al. (2018) <doi:10.1093/bioinformatics/bty371>. Since version 1.1.0, both discrete and probabilistic genotypes are acceptable input; for more details on the latter see Liao et al. (2021) <doi:10.1007/s00122-021-03834-x>.

Utilities for multiple hypothesis testing, companion datasets from "Probability and Statistics for Economics and Business: An Introduction Using R" by Jason Abrevaya (MIT Press, under contract).

This package implements a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects. It works best with time series that have strong seasonal effects and several seasons of historical data. Prophet is robust to missing data and shifts in the trend, and typically handles outliers well.