Parses financial condition and performance data (Call Reports) for institutions in the United States Farm Credit System. Contains functions for downloading files from the Farm Credit Administration (FCA) Call Report archive website and reading the files into tidy data frame format. The archive website can be found at <https://www.fca.gov/bank-oversight/call-report-data-for-download>.

Given vectors of family sizes and number of affecteds per family, calculates the risk of disease recurrence in an unaffected person, conditional on a family having at least k affected members. Methods also model heterogeneity of disease risk across families by fitting a mixture model, allowing for high and low risk families.

Project Customer Retention based on Beta Geometric, Beta Discrete Weibull and Latent Class Discrete Weibull Models.This package is based on Fader and Hardie (2007) <doi:10.1002/dir.20074> and Fader and Hardie et al. (2018) <doi:10.1016/j.intmar.2018.01.002>.

This package provides four addons for analyzing trends and unit roots in financial time series: (i) functions for the density and probability of the augmented Dickey-Fuller Test, (ii) functions for the density and probability of MacKinnon's unit root test statistics, (iii) reimplementations for the ADF and MacKinnon Test, and (iv) an urca Unit Root Test Interface for Pfaff's unit root test suite.

Analysis of Bayesian adaptive enrichment clinical trial using Free-Knot Bayesian Model Averaging (FK-BMA) method of Maleyeff et al. (2024) for Gaussian data. Maleyeff, L., Golchi, S., Moodie, E. E. M., & Hudson, M. (2024) "An adaptive enrichment design using Bayesian model averaging for selection and threshold-identification of predictive variables" <doi:10.1093/biomtc/ujae141>.

This package provides high-level access to neuroimaging data from standard software packages like FreeSurfer <http://freesurfer.net/> on the level of subjects and groups. Load morphometry data, surfaces and brain parcellations based on atlases. Mask data using labels, load data for specific atlas regions only, and visualize data and statistical results directly in R'.

This package performs fragment analysis using genetic data coming from capillary electrophoresis machines. These are files with FSA extension which stands for FASTA-type file, and .txt files from Beckman CEQ 8000 system, both contain DNA fragment intensities read by machinery. In addition to visualization, it performs automatic scoring of SSRs (Sample Sequence Repeats; a type of genetic marker very common across the genome) and other type of PCR markers (standing for Polymerase Chain Reaction) in biparental populations such as F1, F2, BC (backcross), and diversity panels (collection of genetic diversity).

Some functions of ade4 and stats are combined in order to obtain a partition of the rows of a data table, with columns representing variables of scales: quantitative, qualitative or frequency. First, a principal axes method is performed and then, a combination of Ward agglomerative hierarchical classification and K-means is performed, using some of the first coordinates obtained from the previous principal axes method. In order to permit different weights of the elements to be clustered, the function kmeansW', programmed in C++, is included. It is a modification of kmeans'. Some graphical functions include the option: gg=FALSE'. When gg=TRUE', they use the ggplot2 and ggrepel packages to avoid the super-position of the labels.

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.

For cleaning and analysis of graphs, such as animal closing force measurements. forceR was initially written and optimized to deal with insect bite force measurements, but can be used for any time series. Includes a full workflow to load, plot and crop data, correct amplifier and baseline drifts, identify individual peak shapes (bites), rescale (normalize) peak curves, and find best polynomial fits to describe and analyze force curve shapes.

The penalized and non-penalized Minorize-Maximization (MM) method for frailty models to fit the clustered data, multi-event data and recurrent data. Least absolute shrinkage and selection operator (LASSO), minimax concave penalty (MCP) and smoothly clipped absolute deviation (SCAD) penalized functions are implemented. All the methods are computationally efficient. These general methods are proposed based on the following papers, Huang, Xu and Zhou (2022) <doi:10.3390/math10040538>, Huang, Xu and Zhou (2023) <doi:10.1177/09622802221133554>.

An application to calculate the daily environmental costs of river flow regulation by dams based on Garcà a de Jalon et al. 2017 <doi:10.1007/s11269-017-1663-0>.

The aim of the package is to provide some basic functions for doing statistics with trapezoidal fuzzy numbers. In particular, the package contains several functions for simulating trapezoidal fuzzy numbers, as well as for calculating some central tendency measures (mean and two types of median), some scale measures (variance, ADD, MDD, Sn, Qn, Tn and some M-estimators) and one diversity index and one inequality index. Moreover, functions for calculating the 1-norm distance, the mid/spr distance and the (phi,theta)-wabl/ldev/rdev distance between fuzzy numbers are included, and a function to calculate the value phi-wabl given a sample of trapezoidal fuzzy numbers.

Access data from the Federal Register API <https://www.federalregister.gov/developers/api/v1>.

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.

This package provides a compositional statistical framework for absolute proportion estimation between fractions in RNA sequencing data. FracFixR addresses the fundamental challenge in fractionated RNA-seq experiments where library preparation and sequencing depth obscure the original proportions of RNA fractions. It reconstructs original fraction proportions using non-negative linear regression, estimates the "lost" unrecoverable fraction, corrects individual transcript frequencies, and performs differential proportion testing between conditions. Supports any RNA fractionation protocol including polysome profiling, sub-cellular localization, and RNA-protein complex isolation.

This package implements a fast, flexible method for simulating continuous variables with specified rank correlations using the Imanâ Conover transformation (Iman & Conover, 1982 <doi:10.1080/03610918208812265>) and back-ranking. Includes plotting tools and error-diagnostics.

This package provides a high-performance framework for deriving bioclimatic and custom summary variables from large-scale climate raster data. The package features a dual-backend architecture that intelligently switches between fast in-memory processing for smaller datasets (via the terra package) and a memory-safe tiled approach for massive datasets that do not fit in RAM (via exactextractr and Rfast'). The main functions, derive_bioclim() and derive_statistics(), offer a unified interface with advanced options for custom time periods and static indices, making it suitable for a wide range of ecological and environmental modeling applications. A software note is in preparation. In the meantime, you can visit the package website <https://gepinillab.github.io/fastbioclim/> to find tutorials in English and Spanish.

This package implements the h-likelihood estimation procedures for general frailty models including competing-risk models and joint models.

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.

Convenient classes to model fitness landscapes and fitness seascapes. A low-level package with which most users will not interact but upon which other packages modeling fitness landscapes and fitness seascapes will depend.

Fast reverse complement of DNA and RNA sequences using a C++ lookup table for O(1) per-base complement mapping. Supports full IUPAC ambiguity codes, DNA and RNA modes, case preservation, and NA handling. Much faster than other packages for computing reverse complements of many short sequences such as primers, probes, and, k-mers.

Probability distributions that are sometimes useful in hydrology.

Access and retrieve vocabulary data Finto API <https://api.finto.fi/>, which is a centralized service for interoperable thesauri, ontology and classification schemes for different subject areas.