This package provides a comprehensive framework in R for modeling and forecasting economic scenarios based on multi-level dynamic factor model. The package enables users to: (i) extract global and group-specific factors using a flexible multi-level factor structure; (ii) compute asymptotically valid confidence regions for the estimated factors, accounting for uncertainty in the factor loadings; (iii) obtain estimates of the parameters of the factor-augmented quantile regressions together with their standard deviations; (iv) recover full predictive conditional densities from estimated quantiles; (v) obtain risk measures based on extreme quantiles of the conditional densities; (vi) estimate the conditional density and the corresponding extreme quantiles when the factors are stressed.

This package implements a Fellegi-Sunter probabilistic record linkage model that allows for missing data and the inclusion of auxiliary information. This includes functionalities to conduct a merge of two datasets under the Fellegi-Sunter model using the Expectation-Maximization algorithm. In addition, tools for preparing, adjusting, and summarizing data merges are included. The package implements methods described in Enamorado, Fifield, and Imai (2019) Using a Probabilistic Model to Assist Merging of Large-scale Administrative Records <doi:10.1017/S0003055418000783> and is available at <https://imai.fas.harvard.edu/research/linkage.html>.

This package provides a joint model for large-scale, competing risks time-to-event data with singular or multiple longitudinal biomarkers, implemented with the efficient algorithms developed by Li and colleagues (2022) <doi:10.1155/2022/1362913> and <doi:10.48550/arXiv.2506.12741>. The time-to-event data is modelled using a (cause-specific) Cox proportional hazards regression model with time-fixed covariates. The longitudinal biomarkers are modelled using a linear mixed effects model. The association between the longitudinal submodel and the survival submodel is captured through shared random effects. It allows researchers to analyze large-scale data to model biomarker trajectories, estimate their effects on event outcomes, and dynamically predict future events from patientsâ past histories. A function for simulating survival and longitudinal data for multiple biomarkers is also included alongside built-in datasets.

This package provides tools for generating an informative type of line graph, the frequency profile, which allows single behaviors, multiple behaviors, or the specific behavioral patterns of individual subjects to be graphed from occurrence/nonoccurrence behavioral data.

This package provides functions for range estimation in birds based on Pennycuick (2008) and Pennycuick (1975), Flight program which compliments Pennycuick (2008) requires manual entry of birds which can be tedious when there are hundreds of birds to estimate. Implemented are two ODE methods discussed in Pennycuick (1975) and time-marching computation methods as in Pennycuick (1998) and Pennycuick (2008). See Pennycuick (1975, ISBN:978-0-12-249405-5), Pennycuick (1998) <doi:10.1006/jtbi.1997.0572>, and Pennycuick (2008, ISBN:9780080557816).

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.

The goal of forstringr is to enable complex string manipulation in R especially to those more familiar with LEFT(), RIGHT(), and MID() functions in Microsoft Excel. The package combines the power of stringr with other manipulation packages such as dplyr and tidyr'.

Transformations that allow obtaining a flat table from reports in text or Excel format that contain data in the form of pivot tables. They can be defined for a single report and applied to a set of reports.

The free group in R; juxtaposition is represented by a plus. Includes inversion, multiplication by a scalar, group-theoretic power operation, and Tietze forms. To cite the package in publications please use Hankin (2022) <doi:10.48550/ARXIV.2212.05883>.

This package provides a small utility which wraps Rscript and provides access to all R functions from the shell.

Classical (bottom-up and top-down), optimal combination and heuristic point (Di Fonzo and Girolimetto, 2023 <doi:10.1016/j.ijforecast.2021.08.004>) and probabilistic (Girolimetto et al. 2024 <doi:10.1016/j.ijforecast.2023.10.003>) forecast reconciliation procedures for linearly constrained time series (e.g., hierarchical or grouped time series) in cross-sectional, temporal, or cross-temporal frameworks.

Fits Weibull or sigmoidal models to percent loss conductivity (plc) curves as a function of plant water potential, computes confidence intervals of parameter estimates and predictions with bootstrap or parametric methods, and provides convenient plotting methods.

This package provides quick and easy access to official spatial data from Germanyâ s Federal Agency for Cartography and Geodesy (BKG) <https://gdz.bkg.bund.de/>. Interfaces various web feature services (WFS) and download servers. Allows retrieval, caching and filtering with a wide range of open geodata products, including administrative or non-administrative boundaries, land cover, elevation models, geographic names, and points of interest covering Germany. Can be particularly useful for linking regional statistics to their spatial representations and streamlining workflows that involve spatial data of Germany.

Interval estimation of the population allele frequency from qPCR analysis based on the restriction enzyme digestion (RED)-DeltaDeltaCq method (Osakabe et al. 2017, <doi:10.1016/j.pestbp.2017.04.003>), as well as general DeltaDeltaCq analysis. Compatible with the Cq measurement of DNA extracted from multiple individuals at once, so called "group-testing", this model assumes that the quantity of DNA extracted from an individual organism follows a gamma distribution. Therefore, the point estimate is robust regarding the uncertainty of the DNA yield.

An implementation of the methodologies described in Xi Liu, Afshin A. Divani, and Alexander Petersen (2022) <doi:10.1016/j.csda.2022.107421>, including truncated functional linear and truncated functional logistic regression models.

Implementation of the Interval Testing Procedure for functional data in different frameworks (i.e., one or two-population frameworks, functional linear models) by means of different basis expansions (i.e., B-spline, Fourier, and phase-amplitude Fourier). The current version of the package requires functional data evaluated on a uniform grid; it automatically projects each function on a chosen functional basis; it performs the entire family of multivariate tests; and, finally, it provides the matrix of the p-values of the previous tests and the vector of the corrected p-values. The functional basis, the coupled or uncoupled scenario, and the kind of test can be chosen by the user. The package provides also a plotting function creating a graphical output of the procedure: the p-value heat-map, the plot of the corrected p-values, and the plot of the functional data.

This package provides a collection of functions for computing fairness metrics for machine learning and statistical models, including confidence intervals for each metric. The package supports the evaluation of group-level fairness criterion commonly used in fairness research, particularly in healthcare for binary protected attributes. It is based on the overview of fairness in machine learning written by Gao et al (2024) <doi:10.48550/arXiv.2406.09307>.

Datasets for teaching quantitative approaches and modeling in archaeology and paleontology. This package provides several types of data related to broad topics (cultural evolution, radiocarbon dating, paleoenvironments, etc.), which can be used to illustrate statistical methods in the classroom (multivariate data analysis, compositional data analysis, diversity measurement, etc.).

This package implements the new algorithm for fast computation of M-scatter matrices using a partial Newton-Raphson procedure for several estimators. The algorithm is described in Duembgen, Nordhausen and Schuhmacher (2016) <doi:10.1016/j.jmva.2015.11.009>.

Probability distributions that are sometimes useful in hydrology.

Access small example datasets from Luquillo, a ForestGEO site in Puerto Rico (<https://forestgeo.si.edu/sites/north-america/luquillo>).

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.

Standard generalized additive models assume a response function, which induces an assumption on the shape of the distribution of the response. However, miss-specifying the response function results in biased estimates. Therefore in Spiegel et al. (2017) <doi:10.1007/s11222-017-9799-6> we propose to estimate the response function jointly with the covariate effects. This package provides the underlying functions to estimate these generalized additive models with flexible response functions. The estimation is based on an iterative algorithm. In the outer loop the response function is estimated, while in the inner loop the covariate effects are determined. For the response function a strictly monotone P-spline is used while the covariate effects are estimated based on a modified Fisher-Scoring algorithm. Overall the estimation relies on the mgcv'-package.

Provide functions for forest inventory calculations. Common volumetric equations (Smalian, Newton and Huber) as well stacking factor and form.