This package provides a population genetic simulator, which is able to generate synthetic datasets for single-nucleotide polymorphisms (SNP) for multiple populations. The genetic distances among populations can be set according to the Fixation Index (Fst) as explained in Balding and Nichols (1995) <doi:10.1007/BF01441146>. This tool is able to simulate outlying individuals and missing SNPs can be specified. For Genome-wide association study (GWAS), disease status can be set in desired level according risk ratio.

This is a fast and flexible implementation of the Kalman filter and smoother, which can deal with NAs. It is entirely written in C and relies fully on linear algebra subroutines contained in BLAS and LAPACK. Due to the speed of the filter, the fitting of high-dimensional linear state space models to large datasets becomes possible. This package also contains a plot function for the visualization of the state vector and graphical diagnostics of the residuals.

Efficient computation of the Liu regression coefficient paths, Liu-related statistics and information criteria for a grid of the regularization parameter. The computations are based on the C++ library Armadillo through the R package Rcpp'.

Define feature logic, compute only new or unprocessed rows, and persist the resulting flat feature table in a database. The package provides an explicit incremental pipeline for fetching source rows, computing feature definitions, and writing computed features to a database table.

This package provides functionality for testing familial hypotheses. Supports testing centers belonging to the Huber family. Testing is carried out using the Bayesian bootstrap. One- and two-sample tests are supported, as are directional tests. Methods for visualizing output are provided.

Brings a set of tools to help and automatically realise the description of principal component analyses (from FactoMineR functions). Detection of existing outliers, identification of the informative components, graphical views and dimensions description are performed threw dedicated functions. The Investigate() function performs all these functions in one, and returns the result as a report document (Word, PDF or HTML).

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.

This package provides a collection of functions for calculating Floristic Quality Assessment (FQA) metrics using regional FQA databases that have been approved or approved with reservations as ecological planning models by the U.S. Army Corps of Engineers (USACE). For information on FQA see Spyreas (2019) <doi:10.1002/ecs2.2825>. These databases are stored in a sister R package, fqadata'. Both packages were developed for the USACE by the U.S. Army Engineer Research and Development Centerâ s Environmental Laboratory.

This package implements the Fourier cumulative sum (CUSUM) cointegration test for detecting cointegration relationships in time series data with structural breaks. The test uses Fourier approximations to capture smooth structural changes and CUSUM statistics to test for cointegration stability. Based on methodology described in Zaghdoudi (2025) <doi:10.46557/001c.144076>. The corrected Akaike Information Criterion (AICc) is used for optimal frequency selection.

This package provides an implementation of two-dimensional functional principal component analysis (FPCA), Marginal FPCA, and Product FPCA for repeated functional data. Marginal and Product FPCA implementations are done for both dense and sparsely observed functional data. References: Chen, K., Delicado, P., & Müller, H. G. (2017) <doi:10.1111/rssb.12160>. Chen, K., & Müller, H. G. (2012) <doi:10.1080/01621459.2012.734196>. Hall, P., Müller, H.G. and Wang, J.L. (2006) <doi:10.1214/009053606000000272>. Yao, F., Müller, H. G., & Wang, J. L. (2005) <doi:10.1198/016214504000001745>.

The Flow Analysis Summary Statistics Tool for R, fasstr', provides various functions to tidy and screen daily stream discharge data, calculate and visualize various summary statistics and metrics, and compute annual trending and volume frequency analyses. It features useful function arguments for filtering of and handling dates, customizing data and metrics, and the ability to pull daily data directly from the Water Survey of Canada hydrometric database (<https://collaboration.cmc.ec.gc.ca/cmc/hydrometrics/www/>).

Similar to base's unique function, only optimized for working with data frames, especially those that contain date-time columns.

This package implements the fused lasso additive model as proposed in Petersen, A., Witten, D., and Simon, N. (2016). Fused Lasso Additive Model. Journal of Computational and Graphical Statistics, 25(4): 1005-1025.

Implementations of the k-means, hierarchical agglomerative and DBSCAN clustering methods for functional data which allows for jointly aligning and clustering curves. It supports functional data defined on one-dimensional domains but possibly evaluating in multivariate codomains. It supports functional data defined in arrays but also via the fd and funData classes for functional data defined in the fda and funData packages respectively. It currently supports shift, dilation and affine warping functions for functional data defined on the real line and uses the SRVF framework to handle boundary-preserving warping for functional data defined on a specific interval. Main reference for the k-means algorithm: Sangalli L.M., Secchi P., Vantini S., Vitelli V. (2010) "k-mean alignment for curve clustering" <doi:10.1016/j.csda.2009.12.008>. Main reference for the SRVF framework: Tucker, J. D., Wu, W., & Srivastava, A. (2013) "Generative models for functional data using phase and amplitude separation" <doi:10.1016/j.csda.2012.12.001>.

Find optimal decisions rules for guiding progression decisions following a pilot trial, assuming a hierarchical recruitment model. Estimate the time until the main trial recruits to target, given the recruitment data observed in the pilot.

Toolbox to process raw data from closed loop flux chamber (or tent) setups into ecosystem gas fluxes usable for analysis. It goes from a data frame of gas concentration over time (which can contain several measurements) and a meta data file indicating which measurement was done when, to a data frame of ecosystem gas fluxes including quality diagnostics. Organized with one function per step, maximizing user flexibility and backwards compatibility. Different models to estimate the fluxes from the raw data are available: exponential as described in Zhao et al (2018) <doi:10.1016/j.agrformet.2018.08.022>, exponential as described in Hutchinson and Mosier (1981) <doi:10.2136/sssaj1981.03615995004500020017x>, quadratic, and linear. Other functions include quality assessment, plotting for visual check, calculation of fluxes based on the setup specific parameters (chamber size, plot area, ...), gross primary production and transpiration rate calculation, and light response curves.

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.

Simple key-value database using SQLite as the backend.

Regular and non-regular Fractional Factorial 2-level designs can be created. Furthermore, analysis tools for Fractional Factorial designs with 2-level factors are offered (main effects and interaction plots for all factors simultaneously, cube plot for looking at the simultaneous effects of three factors, full or half normal plot, alias structure in a more readable format than with the built-in function alias).

Average rating and number of votes reported by IMDb for films and shorts with over 100 votes in 2022. The data are analysed in Chapter 3 of the Book Getting (more out of) Graphics (Antony Unwin, CRC Press 2024).

Lightweight utilities to estimate autoregressive (AR) and autoregressive moving average (ARMA) noise models from residuals and apply matched generalized least squares to whiten functional magnetic resonance imaging (fMRI) design and data matrices. The ARMA estimator follows a classic 1982 approach <doi:10.1093/biomet/69.1.81>, and a restricted AR family mirrors workflows described by Cox (2012) <doi:10.1016/j.neuroimage.2011.08.056>.

Generates a frequency distribution. The frequency distribution includes raw frequencies, percentages in each category, and cumulative frequencies. The frequency distribution can be stored as a data frame.

This package provides functions for sequencing studies allowing for multiple functional annotation scores. Score type tests and an efficient perturbation method are used for individual gene/large gene-set/genome wide analysis. Only summary statistics are needed.

Feature flags allow developers to turn features of their software on and off in form of configuration. This package provides functions for creating feature flags in code. It exposes an interface for defining own feature flags which are enabled based on custom criteria.