This package provides a replacement for dplyr::na_if(). Allows you to specify multiple values to be replaced with NA using a single function.

Developed by CDC/ATSDR (Centers for Disease Control and Prevention/ Agency for Toxic Substances and Disease Registry), Social Vulnerability Index (SVI) serves as a tool to assess the resilience of communities by taking into account socioeconomic and demographic factors. Provided with year(s), region(s) and a geographic level of interest, findSVI retrieves required variables from US census data and calculates SVI for communities in the specified area based on CDC/ATSDR SVI documentation. Reference for the calculation methods: Flanagan BE, Gregory EW, Hallisey EJ, Heitgerd JL, Lewis B (2011) <doi:10.2202/1547-7355.1792>.

Constructs optimal policy trees which provide a rule-based treatment prescription policy. Input is covariate and reward data, where, typically, the rewards will be doubly robust reward estimates. This package aims to construct optimal policy trees more quickly than the existing policytree package and is intended to be used alongside that package. For more details see Cussens, Hatamyar, Shah and Kreif (2025) <doi:10.48550/arXiv.2506.15435>.

Flexible framework for specifying survival distributions through their hazard (failure rate) functions. Define arbitrary time-varying hazard functions to model complex failure patterns including bathtub curves, proportional hazards with covariates, and other non-standard hazard behaviors. Provides automatic computation of survival, CDF, PDF, quantiles, and sampling. Implements the likelihood model interface for maximum likelihood estimation with right-censored and left-censored survival data.

Efficient implementations of the algorithms in the Almost-Matching-Exactly framework for interpretable matching in causal inference. These algorithms match units via a learned, weighted Hamming distance that determines which covariates are more important to match on. For more information and examples, see the Almost-Matching-Exactly website.

An R interface to FLINT <https://flintlib.org/>, a C library for number theory. FLINT extends GNU MPFR <https://www.mpfr.org/> and GNU MP <https://gmplib.org/> with support for operations on standard rings (the integers, the integers modulo n, finite fields, the rational, p-adic, real, and complex numbers) as well as matrices and polynomials over rings. FLINT implements midpoint-radius interval arithmetic, also known as ball arithmetic, in the real and complex numbers, enabling computation in arbitrary precision with rigorous propagation of rounding and other errors; see Johansson (2017) <doi:10.1109/TC.2017.2690633>. Finally, FLINT provides ball arithmetic implementations of many special mathematical functions, with high coverage of reference works such as the NIST Digital Library of Mathematical Functions <https://dlmf.nist.gov/>. The R interface defines S4 classes, generic functions, and methods for representation and basic operations as well as plain R functions mirroring and vectorizing entry points in the C library.

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.

Read and write PNG images with arrays, rasters, native rasters, numeric arrays, integer arrays, raw vectors and indexed values. This PNG encoder exposes configurable internal options enabling the user to select a speed-size tradeoff. For example, disabling compression can speed up writing PNG by a factor of 50. Multiple image formats are supported including raster, native rasters, and integer and numeric arrays at color depths of 1, 2, 3 or 4. 16-bit images are also supported. This implementation uses the libspng C library which is available from <https://github.com/randy408/libspng/>.

Accompanying package of the book Financial Risk Modelling and Portfolio Optimisation with R', second edition. The data sets used in the book are contained in this package.

Single unified interface for end-to-end modelling of regression, categorical and time-to-event (survival) outcomes. Models created using familiar are self-containing, and their use does not require additional information such as baseline survival, feature clustering, or feature transformation and normalisation parameters. Model performance, calibration, risk group stratification, (permutation) variable importance, individual conditional expectation, partial dependence, and more, are assessed automatically as part of the evaluation process and exported in tabular format and plotted, and may also be computed manually using export and plot functions. Where possible, metrics and values obtained during the evaluation process come with confidence intervals.

This package provides an opinionated project scaffold for R and Quarto analysis work, enforcing a consistent directory layout with scripts in R/, .qmd files in pages/, and 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.

Spatio-temporal Fixation Pattern Analysis (FPA) is a new method of analyzing eye movement data, developed by Mr. Jinlu Cao under the supervision of Prof. Chen Hsuan-Chih at The Chinese University of Hong Kong, and Prof. Wang Suiping at the South China Normal Univeristy. The package "fpa" is a R implementation which makes FPA analysis much easier. There are four major functions in the package: ft2fp(), get_pattern(), plot_pattern(), and lineplot(). The function ft2fp() is the core function, which can complete all the preprocessing within moments. The other three functions are supportive functions which visualize the eye fixation patterns.

Convenient functions for ensemble forecasts in R combining approaches from the forecast package. Forecasts generated from auto.arima(), ets(), thetaf(), nnetar(), stlm(), tbats(), snaive() and arfima() can be combined with equal weights, weights based on in-sample errors (introduced by Bates & Granger (1969) <doi:10.1057/jors.1969.103>), or cross-validated weights. Cross validation for time series data with user-supplied models and forecasting functions is also supported to evaluate model accuracy.

Fire behavior prediction models, including the Scott & Reinhardt's (2001) Rothermel Wildland Fire Modelling System <DOI:10.2737/RMRS-RP-29> and Alexander et al.'s (2006) Crown Fire Initiation & Spread model <DOI:10.1016/j.foreco.2006.08.174>. Also contains sample datasets, estimation of fire behavior prediction model inputs (e.g., fuel moisture, canopy characteristics, wind adjustment factor), results visualization, and methods to estimate fire weather hazard.

Interface to Palantir Foundry', including reading and writing structured or unstructured datasets, and more <https://www.palantir.com/platforms/foundry/>.

Implementation of the Future API <doi:10.32614/RJ-2021-048> on top of the mirai package <doi:10.5281/zenodo.7912722>. By using this package, you get to take advantage of the benefits of mirai plus everything else that future and the Futureverse adds on top of it. It allows you to process futures, as defined by the future package, in parallel out of the box, on your local machine or across remote machines. Contrary to back-ends relying on the parallel package (e.g. multisession') and socket connections, mirai_cluster and mirai_multisession', provided here, can run more than 125 parallel R processes. As a reminder, regardless which future backend is used by the user, the code does not have to change, it gives identical results, and behaves exactly the same.

It contains a function designed to the joint segmentation in the mean of several correlated series. The method is described in the paper X. Collilieux, E. Lebarbier and S. Robin. A factor model approach for the joint segmentation with between-series correlation (2015) <arXiv:1505.05660>.

In Australia, a financial year (or fiscal year) is the period from 1 July to 30 June of the following calendar year. As such, many databases need to represent and validate financial years efficiently. While the use of integer years with a convention that they represent the year ending is common, it may lead to ambiguity with calendar years. On the other hand, string representations may be too inefficient and do not easily admit arithmetic operations. This package tries to make validation of financial years quicker while retaining clarity.

This package provides optimized C++ code for computing the partial Receiver Operating Characteristic (ROC) test used in niche and species distribution modeling. The implementation follows Peterson et al. (2008) <doi:10.1016/j.ecolmodel.2007.11.008>. Parallelization via OpenMP was implemented with assistance from the DeepSeek Artificial Intelligence Assistant (<https://www.deepseek.com/>).

Implementation of a simple algorithm designed for online multivariate changepoint detection of a mean in sparse changepoint settings. The algorithm is based on a modified cusum statistic and guarantees control of the type I error on any false discoveries, while featuring O(1) time and O(1) memory updates per series as well as a proven detection delay.

This package provides functions for finding smooth interpolating curves connecting a series of points in the plane. Curves may be open or closed, that is, with the first and last point of the curve at the initial point.

This package provides a flexible permutation framework for making inference such as point estimation, confidence intervals or hypothesis testing, on any kind of data, be it univariate, multivariate, or more complex such as network-valued data, topological data, functional data or density-valued data.

Supervised, multivariate, and non-parametric discretization algorithm based on tree ensembles learning and moment matching optimization. This version of the algorithm relies on random forest algorithm to learn a large set of split points that conserves the relationship between attributes and the target class, and on moment matching optimization to transform this set into a reduced number of cut points matching as well as possible statistical properties of the initial set of split points. For each attribute to be discretized, the set S of its related split points extracted through random forest is mapped to a reduced set C of cut points of size k. This mapping relies on minimizing, for each continuous attribute to be discretized, the distance between the four first moments of S and the four first moments of C subject to some constraints. This non-linear optimization problem is performed using k values ranging from 2 to max_splits', and the best solution returned correspond to the value k which optimum solution is the lowest one over the different realizations. ForestDisc is a generalization of RFDisc discretization method initially proposed by Berrado and Runger (2009) <doi:10.1109/AICCSA.2009.5069327>, and improved by Berrado et al. in 2012 by adopting the idea of moment matching optimization related by Hoyland and Wallace (2001) <doi: 10.1287/mnsc.47.2.295.9834>.

FastGit <https://doc.fastgit.org/> works like a mirror of GitHub to make significant acceleration. fgitR is a package to do git operation with FastGit automatically.