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 thousands of birds to estimate. Implemented are two ODE methods discussed in Pennycuick (1975) and time-marching computation method "constant muscle mass" as in Pennycuick (1998). See Pennycuick (1975, ISBN:978-0-12-249405-5), Pennycuick (1998) <doi:10.1006/jtbi.1997.0572>, and Pennycuick (2008, ISBN:9780080557816).

This package provides robust tests for testing in GLMs, by sign-flipping score contributions. The tests are robust against overdispersion, heteroscedasticity and, in some cases, ignored nuisance variables. See Hemerik, Goeman and Finos (2020) <doi:10.1111/rssb.12369>.

This package provides a set of tools for data wrangling, spatial data analysis, statistical modeling (including direct, model-assisted, photo-based, and small area tools), and USDA Forest Service data base tools. These tools are aimed to help Foresters, Analysts, and Scientists extract and perform analyses on USDA Forest Service data.

Around 10% of almost any predictive modeling project is spent in predictive modeling, funModeling and the book Data Science Live Book (<https://livebook.datascienceheroes.com/>) are intended to cover remaining 90%: data preparation, profiling, selecting best variables dataViz', assessing model performance and other functions.

This package provides a flexible set of tools for matching two un-linked data sets. fedmatch allows for three ways to match data: exact matches, fuzzy matches, and multi-variable matches. It also allows an easy combination of these three matches via the tier matching function.

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.

Growth models and forest production require existing data manipulation and the creation of new data, structured from basic forest inventory data. The purpose of this package is provide functions to support these activities.

Perform mathematical operations on R formula (add, subtract, multiply, etc.) and substitute parts of formula.

Function factories are functions that make functions. They can be confusing to construct. Straightforward techniques can produce functions that are fragile or hard to understand. While more robust techniques exist to construct function factories, those techniques can be confusing. This package is designed to make it easier to construct function factories.

Build display tables easily by extending the functionality of the flextable package. Features include spanning header, grouping rows, parsing markdown and so on.

This is a package for implementation of Flury-Gautschi algorithms.

This package performs functional regression, and some related approaches, for intensive longitudinal data (see the book by Walls & Schafer, 2006, Models for Intensive Longitudinal Data, Oxford) when such data is not necessarily observed on an equally spaced grid of times. The approach generally follows the ideas of Goldsmith, Bobb, Crainiceanu, Caffo, and Reich (2011)<DOI:10.1198/jcgs.2010.10007> and the approach taken in their sample code, but with some modifications to make it more feasible to use with long rather than wide, non-rectangular longitudinal datasets with unequal and potentially random measurement times. It also allows easy plotting of the correlation between the smoothed covariate and the outcome as a function of time, which can add additional insights on how to interpret a functional regression. Additionally, it also provides several permutation tests for the significance of the functional predictor. The heuristic interpretation of ``time is used to describe the index of the functional predictor, but the same methods can equally be used for another unidimensional continuous index, such as space along a north-south axis. Note that most of the functionality of this package has been superseded by added features after 2016 in the pfr function by Jonathan Gellar, Mathew W. McLean, Jeff Goldsmith, and Fabian Scheipl, in the refund package built by Jeff Goldsmith and co-authors and maintained by Julia Wrobel. The development of the funreg package in 2015 and 2016 was part of a research project supported by Award R03 CA171809-01 from the National Cancer Institute and Award P50 DA010075 from the National Institute on Drug Abuse. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute on Drug Abuse, the National Cancer Institute, or the National Institutes of Health.

Stores large arrays in files to avoid occupying large memories. Implemented with super fast gigabyte-level multi-threaded reading/writing via OpenMP'. Supports multiple non-character data types (double, float, complex, integer, logical, and raw).

An API for automatic data queries to the fedstat <https://www.fedstat.ru>, using a small set of functions with a common interface.

This package provides tools for detecting and summarize influential cases that can affect exploratory and confirmatory factor analysis models as well as structural equation models more generally (Chalmers, 2015, <doi:10.1177/0146621615597894>; Flora, D. B., LaBrish, C. & Chalmers, R. P., 2012, <doi:10.3389/fpsyg.2012.00055>).

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

Allows maximum likelihood fitting of cluster-weighted models, a class of mixtures of regression models with random covariates. Methods are described in Angelo Mazza, Antonio Punzo, Salvatore Ingrassia (2018) <doi:10.18637/jss.v086.i02>.

This package provides a collection of functions to optimize portfolios and to analyze them from different points of view.

This package provides a collection of four datasets based around the population dynamics of migratory fish. Datasets contain both basic size information on a per fish basis, as well as otolith data that contains a per day record of fish growth history. All data in this package was collected by the author, from 2015-2016, in the Wellington region of New Zealand.

Helpers for parsing out the R functions and packages used in R scripts and notebooks.

This package provides functions for fitting data to a quiescent growth model, i.e. a growth process that involves members of the population who stop dividing or propagating.

This package provides a simple way to unload none-base packages and remove all global variables.

This package implements methods for analyzing competing risks data in multi-center survival studies using frailty models. The approach relies on a mixed proportional hazards model for the sub-distribution, allowing for cluster-specific random effects. The package provides tools for model estimation with or without frailty using Maximum Likelihood (ML) and Restricted Maximum Likelihood (REML). It supports flexible modeling of between-center heterogeneity and is particularly suited for multi-center clinical trials or registries. Core features include data simulation, likelihood computation, cluster-dependent censoring options, and testing of frailty effects. For methodological details, see Katsahian et al. (2006) <doi:10.1002/sim.2684>.

This package provides methods for fitting and inspection of Bayesian Multinomial Logistic Normal Models using MAP estimation and Laplace Approximation as developed in Silverman et. Al. (2022) <https://www.jmlr.org/papers/v23/19-882.html>. Key functionality is implemented in C++ for scalability. fido replaces the previous package stray'.