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.

Fast, numerically robust computation of weighted moments via Rcpp'. Supports computation on vectors and matrices, and Monoidal append of moments. Moments and cumulants over running fixed length windows can be computed, as well as over time-based windows. Moment computations are via a generalization of Welford's method, as described by Bennett et. (2009) <doi:10.1109/CLUSTR.2009.5289161>.

The CRAN check results and where your package stands in the CRAN submission queue in your R terminal.

Unified regularized estimating equation solver. Currently the package includes one solver with the l1 penalty only. More solvers and penalties are under development. Reference: Yi Yang, Yuwen Gu, Yue Zhao, Jun Fan (2021) <doi:10.48550/arXiv.2110.11074>.

Feature Ordering by Integrated R square Dependence (FORD) is a variable selection algorithm based on the new measure of dependence: Integrated R2 Dependence Coefficient (IRDC). For more information, see the paper: Azadkia and Roudaki (2025),"A New Measure Of Dependence: Integrated R2" <doi:10.48550/arXiv.2505.18146>.

Use R to access to the FMP Cloud API <https://fmpcloud.io/> and Financial Modeling Prep API <https://financialmodelingprep.com/developer/docs/>. Data available includes stock prices, market indexes, company fundamentals, 13F holdings data, and much more. A valid API token must be set to enable functions.

Books are "Linear Models with R" published 1st Ed. August 2004, 2nd Ed. July 2014, 3rd Ed. February 2025 by CRC press, ISBN 9781439887332, and "Extending the Linear Model with R" published by CRC press in 1st Ed. December 2005 and 2nd Ed. March 2016, ISBN 9781584884248 and "Practical Regression and ANOVA in R" contributed documentation on CRAN (now very dated).

Read and write Frictionless Data Packages. A Data Package (<https://specs.frictionlessdata.io/data-package/>) is a simple container format and standard to describe and package a collection of (tabular) data. It is typically used to publish FAIR (<https://www.go-fair.org/fair-principles/>) and open datasets.

Implementation of a simple algorithm designed for online multivariate changepoint detection of a mean in sparse changepoint settings. The algorithm is based on a modified cusum statistic and guarantees control of the type I error on any false discoveries, while featuring O(1) time and O(1) memory updates per series as well as a proven detection delay.

This package provides a small utility which wraps Rscript and provides access to all R functions from the shell.

Probability distributions that are sometimes useful in hydrology.

FLR algorithm for classification.

Estimates heterogeneous effects in factorial (and conjoint) models. The methodology employs a Bayesian finite mixture of regularized logistic regressions, where moderators can affect each observation's probability of group membership and a sparsity-inducing prior fuses together levels of each factor while respecting ANOVA-style sum-to-zero constraints. Goplerud, Imai, and Pashley (2024) <doi:10.48550/ARXIV.2201.01357> provide further details.

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).

Data from various catalogs of astrophysical gamma-ray sources detected by NASA's Large Area Telescope (The Astrophysical Journal, 697, 1071, 2009 June 1), on board the Fermi gamma-ray satellite. More information on Fermi and its data products is available from the Fermi Science Support Center (http://fermi.gsfc.nasa.gov/ssc/).

This package provides functions for visualizing, modeling, forecasting and hypothesis testing of functional time series.

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.

In the Cramérâ Lundberg risk process perturbed by a Wiener process, this packages provides approximations to the probability of ruin within a finite time horizon. Currently, there are three methods implemented: The first one uses saddlepoint approximation (two variants are provided), the second one uses importance sampling and the third one is based on the simulation of a dual process. This last method is not very accurate and only given here for completeness.

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).

Extracts features from biological sequences. It contains most features which are presented in related work and also includes features which have never been introduced before. It extracts numerous features from nucleotide and peptide sequences. Each feature converts the input sequences to discrete numbers in order to use them as predictors in machine learning models. There are many features and information which are hidden inside a sequence. Utilizing the package, users can convert biological sequences to discrete models based on chosen properties. References: iLearn Z. Chen et al. (2019) <DOI:10.1093/bib/bbz041>. iFeature Z. Chen et al. (2018) <DOI:10.1093/bioinformatics/bty140>. <https://CRAN.R-project.org/package=rDNAse>. PseKRAAC Y. Zuo et al. PseKRAAC: a flexible web server for generating pseudo K-tuple reduced amino acids composition (2017) <DOI:10.1093/bioinformatics/btw564>. iDNA6mA-PseKNC P. Feng et al. iDNA6mA-PseKNC: Identifying DNA N6-methyladenosine sites by incorporating nucleotide physicochemical properties into PseKNC (2019) <DOI:10.1016/j.ygeno.2018.01.005>. I. Dubchak et al. Prediction of protein folding class using global description of amino acid sequence (1995) <DOI:10.1073/pnas.92.19.8700>. W. Chen et al. Identification and analysis of the N6-methyladenosine in the Saccharomyces cerevisiae transcriptome (2015) <DOI:10.1038/srep13859>.

Tests for Kaiser-Meyer-Olkin (KMO) and communalities in a dataset. It provides a final sample by removing variables in a iterable manner while keeping account of the variables that were removed in each step. It follows the best practices and assumptions according to Hair, Black, Babin & Anderson (2018, ISBN:9781473756540).

This package creates a full rank matrix out of a given matrix. The intended use is for one-hot encoded design matrices that should be used in linear models to ensure that significant associations can be correctly interpreted. However, fullRankMatrix can be applied to any matrix to make it full rank. It removes columns with only 0's, merges duplicated columns and discovers linearly dependent columns and replaces them with linearly independent columns that span the space of the original columns. Columns are renamed to reflect those modifications. This results in a full rank matrix that can be used as a design matrix in linear models. The algorithm and some functions are inspired by Kuhn, M. (2008) <doi:10.18637/jss.v028.i05>.

This package provides a novel forward stepwise discriminant analysis framework that integrates Pillai's trace with Uncorrelated Linear Discriminant Analysis (ULDA), providing an improvement over traditional stepwise LDA methods that rely on Wilks Lambda. A stand-alone ULDA implementation is also provided, offering a more general solution than the one available in the MASS package. It automatically handles missing values and provides visualization tools. For more details, see Wang (2024) <doi:10.48550/arXiv.2409.03136>.

Four fertility models are fitted using non-linear least squares. These are the Hadwiger, the Gamma, the Model1 and Model2, following the terminology of the following paper: Peristera P. and Kostaki A. (2007). "Modeling fertility in modern populations". Demographic Research, 16(6): 141--194. <doi:10.4054/DemRes.2007.16.6>. Model based averaging is also supported.