Computes and plots prediction intervals for numerical data or prediction sets for categorical data using prior information. Empirical Bayes procedures to estimate the prior information from multi-group data are included. See, e.g.,Bersson and Hoff (2022) <arXiv:2204.08122> "Optimal Conformal Prediction for Small Areas".

This package creates a scatter plot after residualizing using a set of covariates. The residuals are calculated using the fixest package which allows very fast estimation that scales. Details of the (Yule-)Frisch-Waugh-Lovell theorem is given in Basu (2023) <doi:10.48550/arXiv.2307.00369>.

Defines a collection of functions to compute average power and sample size for studies that use the false discovery rate as the final measure of statistical significance. A three-rectangle approximation method of a p-value histogram is proposed to derive a formula to compute the statistical power for analyses that involve the FDR. The methodology paper of this package is under review.

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.

Creates, manipulates, and evaluates hemodynamic response functions and event-related regressors for functional magnetic resonance imaging data analysis. Supports multiple basis sets including Canonical, Gamma, Gaussian, B-spline, and Fourier bases. Features decorators for time-shifting and blocking, and efficient convolution algorithms for regressor construction. Methods are based on standard fMRI analysis techniques as described in Jezzard et al. (2001, ISBN:9780192630711).

Perform frequency distribution tables, associated histograms and polygons from vector, data.frame and matrix objects for numerical and categorical variables.

Fast estimation algorithms to implement the Quantile Regression with Selection estimator and the multiplicative Bootstrap for inference. This estimator can be used to estimate models that feature sample selection and heterogeneous effects in cross-sectional data. For more details, see Arellano and Bonhomme (2017) <doi:10.3982/ECTA14030> and Pereda-Fernández (2024) <doi:10.48550/arXiv.2402.16693>.

This package creates participant flow diagrams directly from a dataframe. Representing the flow of participants through each stage of a study, especially in clinical trials, is essential to assess the generalisability and validity of the results. This package provides a set of functions that can be combined with a pipe operator to create all kinds of flowcharts from a data frame in an easy way.

Computes the power and sample size (PASS) required to test for the difference in the mean function between two groups under a repeatedly measured longitudinal or sparse functional design. See the manuscript by Koner and Luo (2023) <https://salilkoner.github.io/assets/PASS_manuscript.pdf> for details of the PASS formula and computational details. The details of the testing procedure for univariate and multivariate response are presented in Wang (2021) <doi:10.1214/21-EJS1802> and Koner and Luo (2023) <arXiv:2302.05612> respectively.

Flow of funds are financial accounts that are provided by Federal Reserve quarterly. The package contains all datasets <https://www.federalreserve.gov/datadownload/Choose.aspx?rel=z1>, tables <https://www.federalreserve.gov/apps/fof/FOFTables.aspx> and descriptions <https://www.federalreserve.gov/apps/fof/Guide/z1_tables_description.pdf> with functions to understand series <https://www.federalreserve.gov/apps/fof/SeriesStructure.aspx> and explore them.

This package provides functions for importing, creating, editing and exporting FSK files <https://foodrisklabs.bfr.bund.de/fskx-food-safety-knowledge-exchange-format/> using the R programming environment. Furthermore, it enables users to run simulations contained in the FSK files and visualize the results.

Functions, S4 classes/methods and a graphical user interface (GUI) to design surveys to substantiate freedom from disease using a modified hypergeometric function (see Cameron and Baldock, 1997, <doi:10.1016/s0167-5877(97)00081-0>). Herd sensitivities are computed according to sampling strategies "individual sampling" or "limited sampling" (see M. Ziller, T. Selhorst, J. Teuffert, M. Kramer and H. Schlueter, 2002, <doi:10.1016/S0167-5877(01)00245-8>). Methods to compute the a-posteriori alpha-error are implemented. Risk-based targeted sampling is supported.

Code for fitting and assessing models for the growth of trees. In particular for the Bayesian neighborhood competition linear regression model of Allen (2020): methods for model fitting and generating fitted/predicted values, evaluating the effect of competitor species identity using permutation tests, and evaluating model performance using spatial cross-validation.

It is known that current false discovery rate (FDR) procedures can be very conservative when applied to multiple testing in the discrete paradigm where p-values (and test statistics) have discrete and heterogeneous null distributions. This package implements more powerful weighted or adaptive FDR procedures for FDR control and estimation in the discrete paradigm. The package takes in the original data set rather than just the p-values in order to carry out the adjustments for discreteness and heterogeneity of p-value distributions. The package implements methods for two types of test statistics and their p-values: (a) binomial test on if two independent Poisson distributions have the same means, (b) Fisher's exact test on if the conditional distribution is the same as the marginal distribution for two binomial distributions, or on if two independent binomial distributions have the same probabilities of success.

Support for fuzzy spatial objects, their operations, and fuzzy spatial inference models based on Spatial Plateau Algebra. It employs fuzzy set theory and fuzzy logic as foundation to deal with spatial fuzziness. It mainly implements underlying concepts defined in the following research papers: (i) "Spatial Plateau Algebra: An Executable Type System for Fuzzy Spatial Data Types" <doi:10.1109/FUZZ-IEEE.2018.8491565>; (ii) "A Systematic Approach to Creating Fuzzy Region Objects from Real Spatial Data Sets" <doi:10.1109/FUZZ-IEEE.2019.8858878>; (iii) "Spatial Data Types for Heterogeneously Structured Fuzzy Spatial Collections and Compositions" <doi:10.1109/FUZZ48607.2020.9177620>; (iv) "Fuzzy Inference on Fuzzy Spatial Objects (FIFUS) for Spatial Decision Support Systems" <doi:10.1109/FUZZ-IEEE.2017.8015707>; (v) "Evaluating Region Inference Methods by Using Fuzzy Spatial Inference Models" <doi:10.1109/FUZZ-IEEE55066.2022.9882658>.

The Clutter model is a significant forest growth simulation tool. Grounded on individual trees and comprehensively considering factors such as competition among trees and the impact of environmental elements on growth, it can accurately reflect the growth process of forest stands. It can be applied in areas like forest resource management, harvesting planning, and ecological research. With the help of the Clutter model, people can better understand the dynamic changes of forests and provide a scientific basis for rational forest management and protecting the ecological environment. This R package can effectively realize the construction of forest growth and harvest models based on the Clutter model and achieve optimized forest management.References: Farias A, Soares C, Leite H et al(2021)<doi:10.1007/s10342-021-01380-1>. Guera O, Silva J, Ferreira R, et al(2019)<doi:10.1590/2179-8087.038117>.

Quantitatively analyse depth time-series data from pop-up satellite archival tags (PSATs) through the application of continuous wavelet transformation (CWT) combined with Principal Component Analysis (PCA), and k-means clustering. Import, crop, and plot depth time-depth records (TDRs). Using CWT to detect important signals within the non-stationary data, we create daily wavelet statistics to summarise vertical movements on different wavelet periods and combine with daily and diel depth statistics. Classify depth time-series with unsupervised k-means clustering into 24-hour periods of vertical movement behaviour with distinct patterns of vertical movement. Plot example days from each behaviour cluster, and plot the TDR coloured by cluster. Based on principals of combining CWT with k-means first developed by Sakamoto (2009) <doi:10.1371/journal.pone.0005379> and redeveloped by Beale (2026) <doi:10.21203/rs.3.rs-6907076/v1>.

Implementation of the FASSTER (Forecasting with Additive Switching of Seasonality, Trend, and Exogenous Regressors) model for forecasting time series with multiple seasonal patterns. The model combines state space methodology with a switching component in the observation equation to allow flexible modeling of complex seasonal patterns, including time-varying effects and multiple seasonalities.

Feature subset selection algorithms modularized in search algorithms and measure utilities.

Nonparametric estimators and tests for time series analysis. The functions use bootstrap techniques and robust nonparametric difference-based estimators to test for the presence of possibly non-monotonic trends and for synchronicity of trends in multiple time series.

We present an implementation of the algorithms required to simulate large-scale social networks and retrieve their most relevant metrics. Details can be found in the accompanying scientific paper on the Journal of Statistical Software, <doi:10.18637/jss.v096.i07>.

Identifies potential data outliers and their impact on estimates and analyses. Tool for evaluation of study credibility. Uses the forward search approach of Atkinson and Riani, "Robust Diagnostic Regression Analysis", 2000,<ISBN: o-387-95017-6> to prepare descriptive statistics of a dataset that is to be analyzed by functions lm stats, glm stats, nls stats, lme nlme, or coxph survival, or their equivalent in another language. Includes graphics functions to display the descriptive statistics.

Forest data quality is a package containing nine methods of analysis for forest databases, from databases containing inventory data and growth models, the focus of the analyzes is related to the quality of the data present in the database with a focus on consistency , punctuality and completeness of data.

Exports flextable objects to xlsx files, utilizing functionalities provided by flextable and openxlsx2'.