Impute general multivariate missing data with the fractional hot deck imputation based on Jaekwang Kim (2011) <doi:10.1093/biomet/asq073>.

This package implements various methods for estimating fractal dimension of time series and 2-dimensional data <doi:10.1214/11-STS370>.

Transform output files of some tools to the microtable object of microtable class in microeco package. The microtable class is the basic class in microeco package and is necessary for the downstream microbial community data analysis.

Cluster functional data using phase and amplitude components of each function. By weighting phase and amplitude variation differently, clustering results can be obtained from multiple perspectives. Routines for synchronization, functional k-means clustering, and functional k-medians clustering are provided.

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).

This is a method for Allele-specific DNA Copy Number Profiling using Next-Generation Sequencing. Given the allele-specific coverage at the variant loci, this program segments the genome into regions of homogeneous allele-specific copy number. It requires, as input, the read counts for each variant allele in a pair of case and control samples. For detection of somatic mutations, the case and control samples can be the tumor and normal sample from the same individual.

The functions provided in the FADA (Factor Adjusted Discriminant Analysis) package aim at performing supervised classification of high-dimensional and correlated profiles. The procedure combines a decorrelation step based on a factor modeling of the dependence among covariates and a classification method. The available methods are Lasso regularized logistic model (see Friedman et al. (2010)), sparse linear discriminant analysis (see Clemmensen et al. (2011)), shrinkage linear and diagonal discriminant analysis (see M. Ahdesmaki et al. (2010)). More methods of classification can be used on the decorrelated data provided by the package FADA.

Fast and numerically stable estimation of a covariance matrix by banding the Cholesky factor using a modified Gram-Schmidt algorithm implemented in RcppArmadilo. See <http://stat.umn.edu/~molst029> for details on the algorithm.

Over 30 years of FreeBSD commit activity and contributor growth. Includes daily commit counts and new committer data extracted from the cloned git repository (1993-2026), plus Phabricator signup statistics (2013-2026). Contains no personal data -- only aggregated counts. Useful for time series analysis, growth modeling, and studying open source community dynamics.

The FAS package implements the bootstrap method for the tuning parameter selection and tuning-free inference on sparse regression coefficient vectors. Currently, the test could be applied to linear and factor-augmented sparse regressions, see Lederer & Vogt (2021, JMLR) <https://www.jmlr.org/papers/volume22/20-539/20-539.pdf> and Beyhum & Striaukas (2023) <arXiv:2307.13364>.

This package contains four main functions (i.e., four pieces of furniture): table1() which produces a well-formatted table of descriptive statistics common as Table 1 in research articles, tableC() which produces a well-formatted table of correlations, tableF() which provides frequency counts, and washer() which is helpful in cleaning up the data. These furniture-themed functions are designed to simplify common tasks in quantitative analysis. Other data summary and cleaning tools are also available.

Analyze and model heteroskedastic behavior in financial time series.

The parameters p and q are estimated with the aid of a randomized Sierpinski Carpet which is built on a [p-p-p-q]-model. Thereby, for three times a simulation with a p-value and once with a q-value is assumed. Hence, these parameters are estimated and displayed. Moreover, functions for simulating random Sierpinski-Carpets with constant and variable probabilities are included. For more details on the method please see Hermann et al. (2015) <doi:10.1002/sim.6497>.

This package provides the Big Merge Tracker and COSCI algorithms for convex clustering and feature screening using L1 fusion penalty. See Radchenko, P. and Mukherjee, G. (2017) <doi:10.1111/rssb.12226> and T.Banerjee et al. (2017) <doi:10.1016/j.jmva.2017.08.001> for more details.

This package provides functions to produce ggplot2'-based plots of objects produced by functions in the vegan package. Provides fortify()', autoplot()', and tidy() methods for many of vegan''s functions. The aim of ggvegan is to make it easier to work within the tidyverse with vegan'.

This package implements a generalized coordinate descent (GCD) algorithm for computing the solution paths of the hybrid Huberized support vector machine (HHSVM) and its generalizations. Supported models include the (adaptive) LASSO and elastic net penalized least squares, logistic regression, HHSVM, squared hinge loss SVM and expectile regression.

This package provides ggsnap(), which saves a ggplot2 plot to disk in a + chain, acting as a thin, chainable wrapper around ggplot2::ggsave(). Can be called multiple times in a chain to save different snapshots.

Ranked Set Sampling (RSS) is a stratified sampling method known for its efficiency compared to Simple Random Sampling (SRS). When sample allocation is equal across strata, it is referred to as balanced RSS (BRSS) whereas unequal allocation is called unbalanced RSS (URSS), which is particularly effective for asymmetric or skewed distributions. This package offers practical statistical tools and sampling methods for both BRSS and URSS, emphasizing flexible sampling designs and inference for population means, medians, proportions, and Area Under the Curve (AUC). It incorporates parametric and nonparametric tests, including empirical likelihood ratio (LR) methods. The package provides ranked set sampling methods from a given population, including sampling with imperfect ranking using auxiliary variables. Furthermore, it provides tools for efficient sample allocation in URSS, ensuring greater efficiency than SRS and BRSS. For more details, refer e.g. to Chen et al. (2003) <doi:10.1007/978-0-387-21664-5>, Ahn et al. (2022) <doi:10.1007/978-3-031-14525-4_3>, and Ahn et al. (2024) <doi:10.1111/insr.12589>.

Fits linear regression, logistic and multinomial regression models, Poisson regression, Cox model via Global Adaptive Generative Adjustment Algorithm. For more detailed information, see Bin Wang, Xiaofei Wang and Jianhua Guo (2022) <arXiv:1911.00658>. This paper provides the theoretical properties of Gaga linear model when the load matrix is orthogonal. Further study is going on for the nonorthogonal cases and generalized linear models. These works are in part supported by the National Natural Foundation of China (No.12171076).

This package implements the GALAHAD algorithm (Geometry-Adaptive Lyapunov-Assured Hybrid Optimizer), updated in version 2 to replace the hard-clamp positivity constraint of v1 with a numerically smooth softplus reparameterization, add rho-based trust-region adaptation (actual vs. predicted objective reduction), extend convergence detection to include both absolute and relative function-stall criteria, and enrich the per-iteration history with Armijo backtrack counts and trust-region quality ratios. Parameters constrained to be positive (rates, concentrations, scale parameters) are handled in a transformed z-space via the softplus map so that gradients remain well-defined at the constraint boundary. A two-partition API (positive / euclidean) replaces the three-way T/P/E partition of v1; the legacy form is still accepted for backwards compatibility. Designed for biological modeling problems (germination, dose-response, prion RT-QuIC, survival) where rates, concentrations, and unconstrained coefficients coexist. Developed at the Minnesota Center for Prion Research and Outreach (MNPRO), University of Minnesota. Based on Conn et al. (2000) <doi:10.1137/1.9780898719857>, Barzilai and Borwein (1988) <doi:10.1093/imanum/8.1.141>, Xu and An (2024) <doi:10.48550/arXiv.2409.14383>, Polyak (1969) <doi:10.1016/0041-5553(69)90035-4>, Nocedal and Wright (2006, ISBN:978-0-387-30303-1), and Dugas et al. (2009) <https://www.jmlr.org/papers/v10/dugas09a.html>.

This package infers state-recorded gender categories from first names and dates of birth using historical datasets. By using these datasets instead of lists of male and female names, this package is able to more accurately infer the gender of a name, and it is able to report the probability that a name was male or female. GUIDELINES: This method must be used cautiously and responsibly. Please be sure to see the guidelines and warnings about usage in the README or the package documentation. See Blevins and Mullen (2015) <http://www.digitalhumanities.org/dhq/vol/9/3/000223/000223.html>.

Gaussian processes ('GPs') have been widely used to model spatial data, spatio'-temporal data, and computer experiments in diverse areas of statistics including spatial statistics, spatio'-temporal statistics, uncertainty quantification, and machine learning. This package creates basic tools for fitting and prediction based on GPs with spatial data, spatio'-temporal data, and computer experiments. Key characteristics for this GP tool include: (1) the comprehensive implementation of various covariance functions including the Matérn family and the Confluent Hypergeometric family with isotropic form, tensor form, and automatic relevance determination form, where the isotropic form is widely used in spatial statistics, the tensor form is widely used in design and analysis of computer experiments and uncertainty quantification, and the automatic relevance determination form is widely used in machine learning; (2) implementations via Markov chain Monte Carlo ('MCMC') algorithms and optimization algorithms for GP models with all the implemented covariance functions. The methods for fitting and prediction are mainly implemented in a Bayesian framework; (3) model evaluation via Fisher information and predictive metrics such as predictive scores; (4) built-in functionality for simulating GPs with all the implemented covariance functions; (5) unified implementation to allow easy specification of various GPs'.

This package provides a simple wrapper for Wikipedia data. Specifically, this package looks to fill a gap in retrieving text data in a tidy format that can be used for Natural Language Processing.

This package provides functions to identify European NUTS (Nomenclature of Territorial Units for Statistics) regions for geographic coordinates (latitude/longitude) using Eurostat geospatial boundaries. Includes map-based visualisation of the matched regions for validation and exploration. Designed for regional data analysis, reproducible workflows, and integration with common geospatial R packages.