Functions, methods, and datasets for fitting dimension reduction regression, using slicing (methods SAVE and SIR), Principal Hessian Directions (phd, using residuals and the response), and an iterative IRE. Partial methods, that condition on categorical predictors are also available. A variety of tests, and stepwise deletion of predictors, is also included. Also included is code for computing permutation tests of dimension. Adding additional methods of estimating dimension is straightforward. For documentation, see the vignette in the package. With version 3.0.4, the arguments for dr.step have been modified.

This package provides tools to compute directly age-standardised rates using the 2013 European Standard Population. Includes variance estimation and 95% confidence intervals for population health applications. Functions are flexible to handle any grouping variable and age bands, allowing reproducible and automated analyses.

Implementation of frequency tables and bar charts for qualitative variables and checkbox fields. This package implements tables and charts used in reports at Funarte (National Arts Foundation) and OBEC (Culture and Creative Economy Observatory) in Brazil, and its main purpose is to simplify the use of R for people with a background in the humanities and arts. Examples and details can be viewed in this presentation from 2026: <https://formacao2026.netlify.app/assets/modulo_3/modulo3#/title-slide>.

This package provides functions are provided that facilitate the import and analysis of SNP (single nucleotide polymorphism) and silicodart (presence/absence) data. The main focus is on data generated by DarT (Diversity Arrays Technology), however, data from other sequencing platforms can be used once SNP or related fragment presence/absence data from any source is imported. Genetic datasets are stored in a derived genlight format (package adegenet'), that allows for a very compact storage of data and metadata. Functions are available for importing and exporting of SNP and silicodart data, for reporting on and filtering on various criteria (e.g. CallRate', heterozygosity, reproducibility, maximum allele frequency). Additional functions are available for visualization (e.g. Principle Coordinate Analysis) and creating a spatial representation using maps. dartR supports also the analysis of 3rd party software package such as newhybrid', structure', NeEstimator and blast'. Since version 2.0.3 we also implemented simulation functions, that allow to forward simulate SNP dynamics under different population and evolutionary dynamics. Comprehensive tutorials and support can be found at our github repository: github.com/green-striped-gecko/dartR/. If you want to cite dartR', you find the information by typing citation('dartR') in the console.

This package provides time series regression models with one predictor using finite distributed lag models, polynomial (Almon) distributed lag models, geometric distributed lag models with Koyck transformation, and autoregressive distributed lag models. It also consists of functions for computation of h-step ahead forecasts from these models. See Demirhan (2020)(<doi:10.1371/journal.pone.0228812>) and Baltagi (2011)(<doi:10.1007/978-3-642-20059-5>) for more information.

The hybrid model is a highly effective forecasting approach that integrates decomposition techniques with machine learning to enhance time series prediction accuracy. Each decomposition technique breaks down a time series into multiple intrinsic mode functions (IMFs), which are then individually modeled and forecasted using machine learning algorithms. The final forecast is obtained by aggregating the predictions of all IMFs, producing an ensemble output for the time series. The performance of the developed models is evaluated using international monthly maize price data, assessed through metrics such as root mean squared error (RMSE), mean absolute percentage error (MAPE), and mean absolute error (MAE). For method details see Choudhary, K. et al. (2023). <https://ssca.org.in/media/14_SA44052022_R3_SA_21032023_Girish_Jha_FINAL_Finally.pdf>.

This package provides a collection of functions to estimate parameters of a diffusion model via a D*M analysis. Build in models are: the Ratcliff diffusion model, the RWiener diffusion model, and Linear Ballistic Accumulator models. Custom models functions can be specified as long as they have a density function.

This package provides a HTML widget that shows differences between files (text, images, and data frames).

Uses species occupancy at coarse grain sizes to predict species occupancy at fine grain sizes. Ten models are provided to fit and extrapolate the occupancy-area relationship, as well as methods for preparing atlas data for modelling. See Marsh et. al. (2018) <doi:10.18637/jss.v086.c03>.

The goal of dataspice is to make it easier for researchers to create basic, lightweight, and concise metadata files for their datasets. These basic files can then be used to make useful information available during analysis, create a helpful dataset "README" webpage, and produce more complex metadata formats to aid dataset discovery. Metadata fields are based on the Schema.org and Ecological Metadata Language standards.

This is the companion package to the Data Visualization Geometries Encyclopedia, providing seamless access to the associated data.

Statistical methods for retrospectively detecting changes in location and/or dispersion of univariate and multivariate variables. Data values are assumed to be independent, can be individual (one observation at each instant of time) or subgrouped (more than one observation at each instant of time). Control limits are computed, often using a permutation approach, so that a prescribed false alarm probability is guaranteed without making any parametric assumptions on the stable (in-control) distribution. See G. Capizzi and G. Masarotto (2018) <doi:10.1007/978-3-319-75295-2_1> for an introduction to the package.

This package provides a collection of functions to preprocess data and organize them in a format amenable to use by chevron.

This package provides a function for plotting maps of agricultural field experiments that are laid out in grids. See Ryder (1981) <doi:10.1017/S0014479700011601>.

Analysis, visualisation and simulation of digital polymerase chain reaction (dPCR) (Burdukiewicz et al. (2016) <doi:10.1016/j.bdq.2016.06.004>). Supports data formats of commercial systems (Bio-Rad QX100 and QX200; Fluidigm BioMark) and other systems.

Supports propensity score-based methodsâ including matching, stratification, and weightingâ for estimating causal treatment effects. It also implements calibration using negative control outcomes to enhance robustness. debiasedTrialEmulation facilitates effect estimation for both binary and time-to-event outcomes, supporting risk ratio (RR), odds ratio (OR), and hazard ratio (HR) as effect measures. It integrates statistical modeling and visualization tools to assess covariate balance, equipoise, and bias calibration. Additional methodsâ including approaches to address immortal time bias, information bias, selection bias, and informative censoringâ are under development. Users interested in these extended features are encouraged to contact the package authors.

This package provides a R driver for Apache Drill<https://drill.apache.org>, which could connect to the Apache Drill cluster<https://drill.apache.org/docs/installing-drill-on-the-cluster> or drillbit<https://drill.apache.org/docs/embedded-mode-prerequisites> and get result(in data frame) from the SQL query and check the current configuration status. This link <https://drill.apache.org/docs> contains more information about Apache Drill.

This package provides a framework for creating production outputs. Users can frame a table, listing, or figure with headers and footers and save to an output file. Stores an intermediate docorator object for reproducibility and rendering to multiple output types.

Helpers functions to process, analyse, and visualize the output of single locus species delimitation methods. For full functionality, please install suggested software at <https://legallab.github.io/delimtools/articles/install.html>.

Demonstration code showing how (univariate) kernel density estimates are computed, at least conceptually, and allowing users to experiment with different kernels, should they so wish. The method used follows directly the definition, but gains efficiency by replacing the observations by frequencies in a very fine grid covering the sample range. A canonical reference is B. W. Silverman, (1998) <doi: 10.1201/9781315140919>. NOTE: the density function in the stats package uses a more sophisticated method based on the fast Fourier transform and that function should be used if computational efficiency is a prime consideration.

Data Analysis using Bootstrap-Coupled ESTimation. Estimation statistics is a simple framework that avoids the pitfalls of significance testing. It uses familiar statistical concepts: means, mean differences, and error bars. More importantly, it focuses on the effect size of one's experiment/intervention, as opposed to a false dichotomy engendered by P values. An estimation plot has two key features: 1. It presents all datapoints as a swarmplot, which orders each point to display the underlying distribution. 2. It presents the effect size as a bootstrap 95% confidence interval on a separate but aligned axes. Estimation plots are introduced in Ho et al., Nature Methods 2019, 1548-7105. <doi:10.1038/s41592-019-0470-3>. The free-to-view PDF is located at <https://www.nature.com/articles/s41592-019-0470-3.epdf?author_access_token=Euy6APITxsYA3huBKOFBvNRgN0jAjWel9jnR3ZoTv0Pr6zJiJ3AA5aH4989gOJS_dajtNr1Wt17D0fh-t4GFcvqwMYN03qb8C33na_UrCUcGrt-Z0J9aPL6TPSbOxIC-pbHWKUDo2XsUOr3hQmlRew%3D%3D>.

Consider ambiguity in probabilistic descriptions by replacing a parametric probabilistic description of uncertainty by a non-parametric set of probability distributions in the form of a Density Ratio Class. This is of particular interest in Bayesian inference. The Density Ratio Class is particularly suited for this purpose as it is invariant under Bayesian inference, marginalization, and propagation through a deterministic model. Here, invariant means that the result of the operation applied to a Density Ratio Class is again a Density Ratio Class. In particular the invariance under Bayesian inference thus enables iterative learning within the same framework of Density Ratio Classes. The use of imprecise probabilities in general, and Density Ratio Classes in particular, lead to intervals of characteristics of probability distributions, such as cumulative distribution functions, quantiles, and means. The package is based on a sample of the distribution proportional to the upper bound of the class. Typically this will be a sample from the posterior in Bayesian inference. Based on such a sample, the package provides functions to calculate lower and upper class boundaries and lower and upper bounds of cumulative distribution functions, and quantiles. Rinderknecht, S.L., Albert, C., Borsuk, M.E., Schuwirth, N., Kuensch, H.R. and Reichert, P. (2014) "The effect of ambiguous prior knowledge on Bayesian model parameter inference and prediction." Environmental Modelling & Software. 62, 300-315, 2014. <doi:10.1016/j.envsoft.2014.08.020>. Sriwastava, A. and Reichert, P. "Robust Bayesian Estimation of Value Function Parameters using Imprecise Priors." Submitted. <https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4973574>.

Construction and analysis of matrix population models in R.

Seasonal- and calendar adjustment of time series with daily frequency using the DSA approach developed by Ollech, Daniel (2018): Seasonal adjustment of daily time series. Bundesbank Discussion Paper 41/2018.