This package provides a shiny application based on FossilSim'. Used for simulating tree, taxonomic and fossil data under mechanistic models of speciation, preservation and sampling.

All data sets from "Forecasting: methods and applications" by Makridakis, Wheelwright & Hyndman (Wiley, 3rd ed., 1998) <https://robjhyndman.com/forecasting/>.

Flexible parametric mixture and non-mixture cure models for time-to-event data.

Feature Ordering by Conditional Independence (FOCI) is a variable selection algorithm based on the measure of conditional dependence. For more information, see the paper: Azadkia and Chatterjee (2019),"A simple measure of conditional dependence" <arXiv:1910.12327>.

Enhance R help system by fuzzy search and preview interface, pseudo-postfix operators, and more. The `?.` pseudo-postfix operator and the `?` prefix operator displays documents and contents (source or structure) of objects simultaneously to help understanding the objects. The `?p` pseudo-postfix operator displays package documents, and is shorter than help(package = foo).

This package provides an opinionated project scaffold for R and Quarto analysis work, enforcing a consistent directory layout with scripts in R/, .qmd files in pages/, and assets in www/. The primary entry point, init(), downloads the latest template from a companion GitHub repository so that project structure evolves independently of package releases. Supports persistent author metadata and Quarto brand configuration that carry across projects automatically.

Fuzzy forests, a new algorithm based on random forests, is designed to reduce the bias seen in random forest feature selection caused by the presence of correlated features. Fuzzy forests uses recursive feature elimination random forests to select features from separate blocks of correlated features where the correlation within each block of features is high and the correlation between blocks of features is low. One final random forest is fit using the surviving features. This package fits random forests using the randomForest package and allows for easy use of WGCNA to split features into distinct blocks. See D. Conn, Ngun, T., C. Ramirez, and G. Li (2019) <doi:10.18637/jss.v091.i09> for further details.

Fast and flexible Kalman filtering and smoothing implementation utilizing sequential processing, designed for efficient parameter estimation through maximum likelihood estimation. Sequential processing is a univariate treatment of a multivariate series of observations and can benefit from computational efficiency over traditional Kalman filtering when independence is assumed in the variance of the disturbances of the measurement equation. Sequential processing is described in the textbook of Durbin and Koopman (2001, ISBN:978-0-19-964117-8). FKF.SP was built upon the existing FKF package and is, in general, a faster Kalman filter/smoother.

Automated feature engineering functions tailored for credit scoring. It includes utilities for extracting structured features from timestamps, IP addresses, and email addresses, enabling enhanced predictive modeling for financial risk assessment.

Tabacchi et al. (2011) published a very detailed study producing a uniform system of functions to estimate tree volume and phytomass components (stem, branches, stool). The estimates of the 2005 Italian forest inventory (<https://www.inventarioforestale.org/it/>) are based on these functions. The study documents the domain of applicability of each function and the equations to quantify estimates accuracies for individual estimates as well as for aggregated estimates. This package makes the functions available in the R environment. Version 2 exposes two distinct functions for individual and summary estimates. To facilitate access to the functions, tree species identification is now based on EPPO species codes (<https://data.eppo.int/>).

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.

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

This package provides a "tabular-data-resource" (<https://specs.frictionlessdata.io/tabular-data-resource/>) is a simple format to describe a singular tabular data resource such as a CSV file. It includes support both for metadata such as author and title and a schema to describe the data, for example the types of the fields/columns in the data. Create a tabular-data-resource by providing a data.frame and specifying metadata. Write and read tabular-data-resources to and from disk.

Computes fungible coefficients and Monte Carlo data. Underlying theory for these functions is described in the following publications: Waller, N. (2008). Fungible Weights in Multiple Regression. Psychometrika, 73(4), 691-703, <DOI:10.1007/s11336-008-9066-z>. Waller, N. & Jones, J. (2009). Locating the Extrema of Fungible Regression Weights. Psychometrika, 74(4), 589-602, <DOI:10.1007/s11336-008-9087-7>. Waller, N. G. (2016). Fungible Correlation Matrices: A Method for Generating Nonsingular, Singular, and Improper Correlation Matrices for Monte Carlo Research. Multivariate Behavioral Research, 51(4), 554-568. Jones, J. A. & Waller, N. G. (2015). The normal-theory and asymptotic distribution-free (ADF) covariance matrix of standardized regression coefficients: theoretical extensions and finite sample behavior. Psychometrika, 80, 365-378, <DOI:10.1007/s11336-013-9380-y>. Waller, N. G. (2018). Direct Schmid-Leiman transformations and rank-deficient loadings matrices. Psychometrika, 83, 858-870. <DOI:10.1007/s11336-017-9599-0>.

This package contains Probability Mass Functions, Cumulative Mass Functions, Negative Log Likelihood value, parameter estimation and modeling data using Binomial Mixture Distributions (BMD) (Manoj et al (2013) <doi:10.5539/ijsp.v2n2p24>) and Alternate Binomial Distributions (ABD) (Paul (1985) <doi:10.1080/03610928508828990>), also Journal article to use the package(<doi:10.21105/joss.01505>).

This package provides tools to support systematic and reproducible workflows for both stationary and nonstationary flood frequency analysis, with applications extending to other hydroclimate extremes, such as precipitation frequency analysis. This package implements the FFA framework proposed by Vidrio- Sahagún et al. (2024) <doi:10.1016/j.envsoft.2024.105940>, originally developed in MATLAB', now adapted for the R environment. This work was funded by the Flood Hazard Identification and Mapping Program of Environment and Climate Change Canada, as well as the Canada Research Chair (Tier 1) awarded to Dr. Pietroniro.

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>.

Given a set of parameters describing model dynamics and a corresponding cost function, FAMoS performs a dynamic forward-backward model selection on a specified selection criterion. It also applies a non-local swap search method. Works on any cost function. For detailed information see Gabel et al. (2019) <doi:10.1371/journal.pcbi.1007230>.

Create, visualize, and test fast-and-frugal decision trees (FFTs) using the algorithms and methods described by Phillips, Neth, Woike & Gaissmaier (2017), <doi:10.1017/S1930297500006239>. FFTs are simple and transparent decision trees for solving binary classification problems. FFTs can be preferable to more complex algorithms because they require very little information, are easy to understand and communicate, and are robust against overfitting.

Real capture frequencies will be fitted to various distributions which provide the basis of estimating population sizes, their standard error, and symmetric as well as asymmetric confidence intervalls.

An easy way to conduct flexible scan. Monte-Carlo method is used to test the spatial clusters given the cases, population, and shapefile. A table with formal style and a map with clusters are included in the result report. The method can be referenced at: Toshiro Tango and Kunihiko Takahashi (2005) <doi:10.1186/1476-072X-4-11>.

With the functions in this package you can check the validity of the following financial instrument identifiers: FIGI (Financial Instrument Global Identifier <https://www.openfigi.com/about/figi>), CUSIP (Committee on Uniform Security Identification Procedures <https://www.cusip.com/identifiers.html#/CUSIP>), ISIN (International Securities Identification Number <https://www.cusip.com/identifiers.html#/ISIN>), SEDOL (Stock Exchange Daily Official List <https://www2.lseg.com/SEDOL-masterfile-service-tech-guide-v8.6>). You can also calculate the FIGI checksum of 11-character strings, which can be useful if you want to create your own FIGI identifiers.

This package provides a fast and flexible implementation of Callaway and Sant'Anna's (2021)<doi:10.1016/j.jeconom.2020.12.001> staggered Difference-in-Differences (DiD) estimators, fastdid reduces the computation time from hours to seconds, and incorporates extensions such as time-varying covariates and multiple events.

Automatically suggests a correction when a typo occurs.