Compare variables of interest between (potentially large numbers of) spatial interactions and meta-variables. Spatial variables are summarized using K, or other, functions, and projected for use in a modified random forest model. The model allows comparison of functional and non-functional variables to each other and to noise, giving statistical significance to the results. Included are preparation, modeling, and interpreting tools along with example datasets, as described in VanderDoes et al., (2023) <doi:10.1101/2023.07.18.549619>.

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>.

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.

Routines for exploratory and descriptive analysis of functional data such as depth measurements, atypical curves detection, regression models, supervised classification, unsupervised classification and functional analysis of variance.

Recent years have seen significant interest in neighborhood-based structural parameters that effectively represent the spatial characteristics of tree populations and forest communities, and possess strong applicability for guiding forestry practices. This package provides valuable information that enhances our understanding and analysis of the fine-scale spatial structure of tree populations and forest stands. Reference: Yan L, Tan W, Chai Z, et al (2019) <doi:10.13323/j.cnki.j.fafu(nat.sci.).2019.03.007>.

Fits, simulates, and evaluates forced-choice and traditional item response theory (IRT) models for noncognitive assessment. Eight model families are supported, spanning dominance (multidimensional IRT (MIRT) 1PL--4PL; multidimensional generalized partial credit model (MGPCM)), ideal-point unfolding (multidimensional generalized graded unfolding model (MGGUM)), and forced-choice designs (forced-choice multidimensional IRT (FCMIRT), forced-choice generalized graded unfolding model (FCGGUM), Thurstonian IRT (TIRT), forced-choice diagnostic classification model (FCDCM), forced-choice generalized deterministic inputs, noisy "and" gate model (FCGDINA)) that mitigate response biases such as acquiescence and social desirability. Core estimation backends include full Bayesian inference via Hamiltonian Monte Carlo (Stan) and a fast improved stochastic expectation-maximization (iStEM) algorithm suitable for large-scale data; FCGDINA also provides a deterministic expectation-maximization (EM) estimator. Comprehensive model evaluation uses the limited-information M2 family of goodness-of-fit statistics (Maydeu-Olivares and Joe, 2005 <doi:10.1198/016214504000002069>; 2006 <doi:10.1007/s11336-005-1295-9>) together with root mean square error of approximation (RMSEA), comparative fit index (CFI), Tucker-Lewis index (TLI), and standardized root mean square residual (SRMSR).

For ordinal rating data, consider the accelerated EM algorithm to estimate and test models within the family of CUB models (where CUB stands for Combination of a discrete Uniform and a shifted Binomial distributions). The procedure is built upon Louis identity for the observed information matrix. Best-subset variable selection is then implemented since it becomes more feasible from the computational point of view.

This package contains a set of functions that can be used to apply formats to data frames or vectors. The package aims to provide functionality similar to that of SAS® formats. Formats are assigned to the format attribute on data frame columns. Then when the fdata() function is called, a new data frame is created with the column data formatted as specified. The package also contains a value() function to create a user-defined format, similar to a SAS® user-defined format.

Utilities to read and write files in the FITS (Flexible Image Transport System) format, a standard format in astronomy (see e.g. <https://en.wikipedia.org/wiki/FITS> for more information). Present low-level routines allow: reading, parsing, and modifying FITS headers; reading FITS images (multi-dimensional arrays); reading FITS binary and ASCII tables; and writing FITS images (multi-dimensional arrays). Higher-level functions allow: reading files composed of one or more headers and a single (perhaps multidimensional) image or single table; reading tables into data frames; generating vectors for image array axes; scaling and writing images as 16-bit integers. Known incompletenesses are reading random group extensions, as well as complex and array descriptor data types in binary tables.

This package provides a simple method to select the best model or best subset of variables using different types of data (binary, Gaussian or Poisson) and applying it in different contexts (parametric or non-parametric). Implemented methodology described in: M. Sestelo, N. M. Villanueva, L. Meira-Machado and J. Roca-Pardiñas (2016). FWDselect: an R package for variable selection in regression models. The R Journal, 8 (1), 132-148. <doi:10.32614/RJ-2016-009>.

Simulation and analysis of Fully-Latent Principal Stratification (FLPS) with measurement models. Lee, Adam, Kang, & Whittaker (2023). <doi:10.1007/978-3-031-27781-8_25>. This package is supported by the Institute of Education Sciences, U.S. Department of Education, through Grant R305D210036.

Regular and non-regular Fractional Factorial 2-level designs can be created. Furthermore, analysis tools for Fractional Factorial designs with 2-level factors are offered (main effects and interaction plots for all factors simultaneously, cube plot for looking at the simultaneous effects of three factors, full or half normal plot, alias structure in a more readable format than with the built-in function alias).

This package provides a utility to scrape and load play-by-play data and statistics from the Professional Women's Hockey League <https://www.thepwhl.com/>, formerly known as the Premier Hockey Federation (PHF) or National Women's Hockey League (NWHL). Additionally, allows access to the National Hockey League's stats API <https://www.nhl.com/>.

This package provides doubly robust one-step and targeted maximum likelihood (TMLE) estimators for average causal effects in acyclic directed mixed graphs (ADMGs) with unmeasured variables. Automatically determines whether the treatment effect is identified via backdoor adjustment or the extended front-door functional, and dispatches to the appropriate estimator. Supports incorporation of machine learning algorithms via SuperLearner and cross-fitting for nuisance estimation. Methods are described in Guo and Nabi (2024) <doi:10.48550/arXiv.2409.03962>.

Discretely-sampled function is first smoothed. Features of the smoothed function are then extracted. Some of the key features include mean value, first and second derivatives, critical points (i.e. local maxima and minima), curvature of cunction at critical points, wiggliness of the function, noise in data, and outliers in data.

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.

Maximum Likelihood Estimation of Stochastic Frontier Production and Cost Functions. Two specifications are available: the error components specification with time-varying efficiencies (Battese and Coelli, 1992, <doi:10.1007/BF00158774>) and a model specification in which the firm effects are directly influenced by a number of variables (Battese and Coelli, 1995, <doi:10.1007/BF01205442>).

Get spatial vector data from the Atlas du Patrimoine (<http://atlas.patrimoines.culture.fr/atlas/trunk/>), the official national platform of the French Ministry of Culture, and facilitate its use within R geospatial workflows. The package provides functions to list available heritage datasets, query and retrieve heritage data using spatial queries based on user-provided sf objects, perform spatial filtering operations, and return results as sf objects suitable for spatial analysis, mapping, and integration into heritage management and landscape studies.

Full-subsets information-theoretic approaches are increasingly used to explore predictive power and variable importance when a wide range of candidate predictors are being considered. This package provides functions that can be used to construct, fit, and compare a complete model set of possible ecological or environmental predictors for a given response variable of interest. Models are based on Generalized Additive Models (GAMs) and build on the MuMIn package. Advantages include the capacity to fit more predictors than there are replicates, automatic removal of models with correlated predictors, and support for model sets that include interactions between factors and smooth predictors, as well as smooth-by-smooth interactions via te(). Methods are described in Fisher et al. (2018) <doi:10.1002/ece3.4134>.

Converts large Danish register files ('sas7bdat') into Parquet format with year-based Hive partitioning and chunked reading for larger-than-memory files. Supports parallel conversion with a targets pipeline and reading those registers into DuckDB tables for faster querying and analyses.

This package provides a high-performance implementation of redundancy analysis (RDA) in C++ using Armadillo and OpenMP'. Supports standard and partial RDA, centering, scaling, overall and axis-wise permutation tests, biplot visualization, score extraction, and prediction. Designed for large ecological, genomic, and other multivariate data sets where computational speed and memory efficiency are required.

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>.

This package provides a neighborhood-based, greedy search algorithm is performed to estimate a feature allocation by minimizing the expected loss based on posterior samples from the feature allocation distribution. The method is described in Dahl, Johnson, and Andros (2023) "Comparison and Bayesian Estimation of Feature Allocations" <doi:10.1080/10618600.2023.2204136>.

Inference methods for factor copula models for continuous data in Krupskii and Joe (2013) <doi:10.1016/j.jmva.2013.05.001>, Krupskii and Joe (2015) <doi:10.1016/j.jmva.2014.11.002>, Fan and Joe (2024) <doi:10.1016/j.jmva.2023.105263>, one factor truncated vine models in Joe (2018) <doi:10.1002/cjs.11481>, and Gaussian oblique factor models. Functions for computing tail-weighted dependence measures in Lee, Joe and Krupskii (2018) <doi:10.1080/10485252.2017.1407414> and estimating tail dependence parameter.