This package provides tools working with data from ACLED (Armed Conflict Location and Event Data). Functions include simplified access to ACLED's API (<https://apidocs.acleddata.com/>), methods for keeping local versions of ACLED data up-to-date, and functions for common ACLED data transformations.

Provides: (1) Tools to infer dominance hierarchies based on calculating Elo scores, but with custom functions to improve estimates in animals with relatively stable dominance ranks. (2) Tools to plot the shape of the dominance hierarchy and estimate the uncertainty of a given data set.

Modeling associations between covariates and power spectra of replicated time series using a cepstral-based semiparametric framework. Implements a fast two-stage estimation procedure via Whittle likelihood and multivariate regression.The methodology is based on Li and Dong (2025) <doi:10.1080/10618600.2025.2473936>.

This package implements the DAAREM method for accelerating the convergence of slow, monotone sequences from smooth, fixed-point iterations such as the EM algorithm. For further details about the DAAREM method, see Henderson, N.C. and Varadhan, R. (2019) <doi:10.1080/10618600.2019.1594835>.

Computes various effect sizes of the difference, their variance, and confidence interval. This package treats Cohen's d, Hedges d, biased/unbiased c (an effect size between a mean and a constant) and e (an effect size between means without assuming the variance equality).

This package provides tools for automatic model selection and diagnostics for Climate and Environmental data. In particular the envcpt() function does automatic model selection between a variety of trend, changepoint and autocorrelation models. The envcpt() function should be your first port of call.

Estimates item and person parameters for the Continuous Response Model (CRM; Samejima, 1973, <doi:10.1007/BF02291114>), computes item fit residual statistics, draws empirical 3D item category response curves, draws theoretical 3D item category response curves, and generates data under the CRM for simulation studies.

This package provides a function that generates a customized correlation matrix based on limit values and proportions for intervals composed by its limits. It can also generate random matrices with low, medium, and high correlations, in which low, medium, and high thresholds are user-defined.

Seamless integration between R and Goose AI capabilities including memory management, visualization enhancements, and workflow automation. Save R objects to Goose memory, apply Block branding to visualizations, and manage data science project workflows. For more information about Goose AI, see <https://github.com/block/goose>.

The gap encodes the distance between clusters and improves interpretation of cluster heatmaps. The gaps can be of the same distance based on a height threshold to cut the dendrogram. Another option is to vary the size of gaps based on the distance between clusters.

Offers methods for visualising, modelling, and forecasting high-dimensional functional time series, also known as functional panel data. Documentation about hdftsa is initially provided via the paper by Cristian F. Jimenez-Varon, Ying Sun and Han Lin Shang (2024, Journal of Computational and Graphical Statistics).

Suite of tools for managing cached files, targeting use in other R packages. Uses rappdirs for cross-platform paths. Provides utilities to manage cache directories, including targeting files by path or by key; cached directories can be compressed and uncompressed easily to save disk space.

Various functions and algorithms are provided here for solving optimal matching tasks in the context of preclinical cancer studies. Further, various helper and plotting functions are provided for unsupervised and supervised machine learning as well as longitudinal mixed-effects modeling of tumor growth response patterns.

Estimates the density of a spatially distributed animal population sampled with an array of passive detectors, such as traps. Models incorporating distance-dependent detection are fitted by simulation and inverse prediction as proposed by Efford (2004) <doi:10.1111/j.0030-1299.2004.13043.x>.

This is a collection of tools for more efficiently understanding and sharing the results of (primarily) regression analyses. There are also a number of miscellaneous functions for statistical and programming purposes. Support for models produced by the survey and lme4 packages are points of emphasis.

This package contains a collection of useful functions for basic data computation and manipulation, wrapper functions for generating ggplot2 graphics, including statistical model diagnostic plots, methods for computing statistical models quality measures (such as AIC, BIC, r squared, root mean squared error) and general utilities.

This package provides functions to estimate and visualize linear as well as nonlinear impulse responses based on local projections by Jordà (2005) <doi:10.1257/0002828053828518>. The methods and the package are explained in detail in Adämmer (2019) <doi:10.32614/RJ-2019-052>.

Distributions that are typically used for exposure rating in general insurance, in particular to price reinsurance contracts. The vignette shows code snippets to fit the distribution to empirical data. See, e.g., Bernegger (1997) <doi:10.2143/AST.27.1.563208> freely available on-line.

Allows various models for multivariate response variables where each response is assumed to follow double hierarchical generalized linear models. In double hierarchical generalized linear models, the mean, dispersion parameters for variance of random effects, and residual variance can be further modeled as random-effect models.

This package performs nonparametric estimation in mixture cure models, and significance tests for the cure probability. For details, see López-Cheda et al. (2017a) <doi:10.1016/j.csda.2016.08.002> and López-Cheda et al. (2017b) <doi:10.1007/s11749-016-0515-1>.

An implementation of prediction intervals for random-effects meta-analysis: Higgins et al. (2009) <doi:10.1111/j.1467-985X.2008.00552.x>, Partlett and Riley (2017) <doi:10.1002/sim.7140>, and Nagashima et al. (2019) <doi:10.1177/0962280218773520>, <arXiv:1804.01054>.

The approach is based on the closed testing procedure to control familywise error rate in a strong sense. The local tests implemented are Wald-type and rank-score. The method is described in De Santis, et al., (2026), <doi:10.48550/arXiv.2511.07999>.

Simulation of event histories with possibly non-linear baseline hazard rate functions, non-linear (time-varying) covariate effect functions, and dependencies on the past of the history. Random generation of event histories is performed using inversion sampling on the cumulative all-cause hazard rate functions.

Method to estimate the spatial influence scales of landscape variables on a response variable. The method is based on Chandler and Hepinstall-Cymerman (2016) Estimating the spatial scales of landscape effects on abundance, Landscape ecology, 31: 1383-1394, <doi:10.1007/s10980-016-0380-z>.