This package creates structured R and Quarto projects with a consistent directory layout: scripts in R/, analysis documents in analysis/, and web assets in www/. The primary entry point, init(), downloads the latest template from a companion GitHub repository so that project structure evolves independently of package releases. Supports persistent author metadata and Quarto brand configuration that carry across projects automatically.

This package provides a computationally efficient and statistically rigorous fast Kernel Machine method for multi-kernel analysis. The approach is based on a low-rank approximation to the nuisance effect kernel matrices. The algorithm is applicable to continuous, binary, and survival traits and is implemented using the existing single-kernel analysis software SKAT and coxKM'. coxKM can be obtained from <https://github.com/lin-lab/coxKM>.

This package provides tools for portfolio construction and risk analytics, including mean-variance optimization, conditional value at risk (expected shortfall) minimization, risk parity, regime clustering, correlation analysis, Monte Carlo simulation, and option pricing. Includes utilities for portfolio evaluation, clustering, and risk reporting. Methods are based in part on Markowitz (1952) <doi:10.1111/j.1540-6261.1952.tb01525.x>, Rockafellar and Uryasev (2000) <doi:10.21314/JOR.2000.038>, Maillard et al. (2010) <doi:10.3905/jpm.2010.36.4.060>, Black and Scholes (1973) <doi:10.1086/260062>, and Cox et al. (1979) <doi:10.1016/0304-405X(79)90015-1>.

This package provides methods for performing fMRI quality assurance (QA) measurements of test objects. Heavily based on the fBIRN procedures detailed by Friedman and Glover (2006) <doi:10.1002/jmri.20583>.

This package provides methods and tools designed to improve the forecast accuracy for a linearly constrained multiple time series, while fulfilling the linear/aggregation relationships linking the components (Girolimetto and Di Fonzo, 2024 <doi:10.48550/arXiv.2412.03429>). FoCo2 offers multi-task forecast combination and reconciliation approaches leveraging input from multiple forecasting models or experts and ensuring that the resulting forecasts satisfy specified linear constraints. In addition, linear inequality constraints (e.g., non-negativity of the forecasts) can be imposed, if needed.

Base maps are transformed to focus on a specific location using an azimuthal logarithmic distance transformation.

This package provides a set of helper functions for constructing file paths relative to the root of various types of projects, such as R packages, Git repositories, and more. File paths are specified with function arguments, or `$` to navigate into folders to specific files supported by auto-completion.

Bindings to libfluidsynth to parse and synthesize MIDI files. It can read MIDI into a data frame, play it on the local audio device, or convert into an audio file.

Convenient classes to model fitness landscapes and fitness seascapes. A low-level package with which most users will not interact but upon which other packages modeling fitness landscapes and fitness seascapes will depend.

This package provides a Bayesian Nonparametric model for the study of time-evolving frequencies, which has become renowned in the study of population genetics. The model consists of a Hidden Markov Model (HMM) in which the latent signal is a distribution-valued stochastic process that takes the form of a finite mixture of Dirichlet Processes, indexed by vectors that count how many times each value is observed in the population. The package implements methodologies presented in Ascolani, Lijoi and Ruggiero (2021) <doi:10.1214/20-BA1206> and Ascolani, Lijoi and Ruggiero (2023) <doi:10.3150/22-BEJ1504> that make it possible to study the process at the time of data collection or to predict its evolution in future or in the past.

FusionCharts provides awesome and minimalist functions to make beautiful interactive charts <https://www.fusioncharts.com/>.

Calculate the final size of a susceptible-infectious-recovered epidemic in a population with demographic variation in contact patterns and susceptibility to disease, as discussed in Miller (2012) <doi:10.1007/s11538-012-9749-6>.

Enables the construction of flexible urban delineations that can be tailored to specific applications or research questions, see Van Migerode et al. (2024) <DOI:10.1177/23998083241262545> and Van Migerode et al. (2025) <DOI:10.5281/zenodo.15173220>. Originally developed to flexibly reconstruct the Degree of Urbanisation classification of cities, towns and rural areas developed by Dijkstra et al. (2021) <DOI:10.1016/j.jue.2020.103312>. Now it also support a broader range of delineation approaches, using multiple datasets â including population, built-up area, and night-time light grids â and different thresholding methods.

This package implements the Fixed Effect Jackknife Instrumental Variables ('FEJIV') estimator of Chao, Swanson, and Woutersen (2023) <doi:10.1016/j.jeconom.2022.12.011>, allowing consistent IV estimation with many (possibly weak) instruments, cluster fixed effects, heteroskedastic errors, and many exogenous covariates. The estimator is recommended by SÅ oczyÅ ski (2024) <doi:10.48550/arXiv.2011.06695> as an alternative to two-stage least squares when estimating the interacted specification of Angrist and Imbens (1995) <doi:10.1080/01621459.1995.10476535>.

This package provides three S7 classes â streamline, bundle, and bundle_set â for representing diffusion MRI tractography data in R, together with a concise set of methods for computing shape descriptors (arc-length, curvature, torsion, sinuosity), the Hausdorff distance between streamlines, arc-length reparametrization of streamlines and bundles onto uniform grids, combination of streamlines or bundles into a single bundle, combination of bundles from multiple subjects or sessions into a bundle_set, and coercion to and from the dwiFiber S4 class of the dti package. See Dell'Acqua, F., Descoteaux, M. and Leemans, A. (2024) "Handbook of Diffusion MR Tractography" <doi:10.1016/C2018-0-02520-7> for more about the mathematical and computational underpinnings of diffusion MRI tractography.

Computes factorial A-, D- and E-optimal designs for two-colour cDNA microarray experiments.

Enables filtering datasets by a prior specified identifiers which correspond to saved filter expressions.

Easy installation, loading and management, of high-performance packages for statistical computing and data manipulation in R. The core fastverse consists of 4 packages: data.table', collapse', kit and magrittr', that jointly only depend on Rcpp'. The fastverse can be freely and permanently extended with additional packages, both globally or for individual projects. Separate package verses can also be created. Fast packages for many common tasks such as time series, dates and times, strings, spatial data, statistics, data serialization, larger-than-memory processing, and compilation of R code are listed in the README file: <https://github.com/fastverse/fastverse#suggested-extensions>.

The funFEM algorithm (Bouveyron et al., 2014) allows to cluster functional data by modeling the curves within a common and discriminative functional subspace.

Estimates the first-exposure effect (FEE) using a one-inflated positive Poisson model, or a one-inflated zero-truncated negative binomial model. In addition, estimates the marginal FEE, and standard errors for the FEE and marginal FEE.

Time-based joins to analyze sequence of events, both in memory and out of memory. after_join() joins two tables of events, while funnel_start() and funnel_step() join events in the same table. With the type argument, you can switch between different funnel types, like first-first and last-firstafter.

Integrate Item Response Theory (IRT) and Federated Learning to estimate traditional IRT models, including the 2-Parameter Logistic (2PL) and the Graded Response Models, with enhanced privacy. It allows for the estimation in a distributed manner without compromising accuracy. A user-friendly shiny application is included.

This package provides a suite of methods for detecting influential subjects in longitudinal datasets, particularly when observations occur at irregular time points. The methods identify individuals whose response trajectories deviate significantly from the population pattern, enabling detection of anomalies or subjects exerting undue influence on model outcomes.

Full Consistency Method (FUCOM) for multi-criteria decision-making (MCDM), developed by Dragam Pamucar in 2018 (<doi:10.3390/sym10090393>). The goal of the method is to determine the weights of criteria such that the deviation from full consistency is minimized. Users provide a character vector specifying the ranking of each criterion according to its significance, starting from the criterion expected to have the highest weight to the least significant one. Additionally, users provide a numeric vector specifying the priority values for each criterion. The comparison is made with respect to the first-ranked (most significant) criterion. The function returns the optimized weights for each criterion (summing to 1), the comparative priority (Phi) values, the mathematical transitivity condition (w) value, and the minimum deviation from full consistency (DFC).