Factor models have been widely applied in areas such as economics and finance, and the well-known heavy-tailedness of macroeconomic/financial data should be taken into account when conducting factor analysis. We propose two algorithms to do robust factor analysis by considering the Huber loss. One is based on minimizing the Huber loss of the idiosyncratic error's L2 norm, which turns out to do Principal Component Analysis (PCA) on the weighted sample covariance matrix and thereby named as Huber PCA. The other one is based on minimizing the element-wise Huber loss, which can be solved by an iterative Huber regression algorithm. In this package we also provide the code for traditional PCA, the Robust Two Step (RTS) method by He et al. (2022) and the Quantile Factor Analysis (QFA) method by Chen et al. (2021) and He et al. (2023).

Surface Protein abundance Estimation using CKmeans-based clustered thresholding ('SPECK') is an unsupervised learning-based method that performs receptor abundance estimation for single cell RNA-sequencing data based on reduced rank reconstruction (RRR) and a clustered thresholding mechanism. Seurat's normalization method is described in: Hao et al., (2021) <doi:10.1016/j.cell.2021.04.048>, Stuart et al., (2019) <doi:10.1016/j.cell.2019.05.031>, Butler et al., (2018) <doi:10.1038/nbt.4096> and Satija et al., (2015) <doi:10.1038/nbt.3192>. Method for the RRR is further detailed in: Erichson et al., (2019) <doi:10.18637/jss.v089.i11> and Halko et al., (2009) <doi:10.48550/arXiv.0909.4061>. Clustering method is outlined in: Song et al., (2020) <doi:10.1093/bioinformatics/btaa613> and Wang et al., (2011) <doi:10.32614/RJ-2011-015>.

An update to the Joint Location-Scale (JLS) testing framework that identifies associated SNPs, gene-sets and pathways with main and/or interaction effects on quantitative traits (Soave et al., 2015; <doi:10.1016/j.ajhg.2015.05.015>). The JLS method simultaneously tests the null hypothesis of equal mean and equal variance across genotypes, by aggregating association evidence from the individual location/mean-only and scale/variance-only tests using Fisher's method. The generalized joint location-scale (gJLS) framework has been developed to deal specifically with sample correlation and group uncertainty (Soave and Sun, 2017; <doi:10.1111/biom.12651>). The current release: gJLS2, include additional functionalities that enable analyses of X-chromosome genotype data through novel methods for location (Chen et al., 2021; <doi:10.1002/gepi.22422>) and scale (Deng et al., 2019; <doi:10.1002/gepi.22247>).

Turns assessment and survey sample data (demographics, scores, sampling weights, ability estimates with individual item response theory standard errors, replicate weights and plausible values) into fully customizable pivot-style statistical tables in which every cell carries a design-appropriate standard error. Provides weighted means, proportions above cut scores, proficiency-level percentages and quantiles; sampling variance via linearization, Woodruff (1952) <doi:10.1080/01621459.1952.10483443> intervals for quantiles, or balanced repeated replication and jackknife replicate weights including Fay's method; measurement variance via delta-method propagation of individual standard errors or Rubin (1987) <doi:10.1002/9780470316696> combination across plausible values. Imports data from CSV', Excel', SPSS', SAS', Stata and item response theory software person files ('Winsteps', ConQuest'); a configuration-file interface serves non-programmers and automation; results export to formatted Excel', JSON and standalone HTML reports with rule-based plain-language interpretation.

Population ratio estimator (calibrated) under two-phase random sampling design has gained enormous popularity in the recent time. This package provides functions for estimation population ratio (calibrated) under two phase sampling design, including the approximate variance of the ratio estimator. The improved ratio estimator can be applicable for both the case, when auxiliary data is available at unit level or aggregate level (eg., mean or total) for first phase sampled. Calibration weight of each unit of the second phase sample was calculated. Single and combined inclusion probabilities were also estimated for both phases under two phase random [simple random sampling without replacement (SRSWOR)] sampling. The improved ratio estimator's percentage coefficient of variation was also determined as a measure of accuracy. This package has been developed based on the theoretical development of Islam et al. (2021) and Ozgul (2020) <doi:10.1080/00949655.2020.1844702>.

At Novartis, we aimed at standardizing the set of diagnostic plots used for modeling activities in order to reduce the overall effort required for generating such plots. For this, we developed a guidance that proposes an adequate set of diagnostics and a toolbox, called ggPMX to execute them. ggPMX is a toolbox that can generate all diagnostic plots at a quality sufficient for publication and submissions using few lines of code. This package focuses on plots recommended by ISoP <doi:10.1002/psp4.12161>. While not required, you can get/install the R lixoftConnectors package in the Monolix installation, as described at the following url <https://monolixsuite.slp-software.com/r-functions/2024R1/installation-and-initialization>. When lixoftConnectors is available, R can use Monolix directly to create the required Chart Data instead of exporting it from the Monolix gui.

This package provides Bayesian estimation of Item Response Theory models that decompose item difficulty into cognitive operations or rules. Implements the Linear Logistic Test Model (LLTM; Fischer (1973) <doi:10.1016/0001-6918(73)90003-6>), the Multicomponent Latent Trait Model for Diagnosis (MLTM-D; Embretson and Yang (2013) <doi:10.1007/s11336-012-9296-y>), and the Generalized Multicomponent Latent Trait Model for Diagnosis (GMLTM-D; Ramirez et al. (2024) <doi:10.3390/jintelligence12070067>), including a variant with correlated latent components. All models are estimated via Hamiltonian Monte Carlo using Stan through the rstan interface. Includes tools for prior predictive checks (Gelman et al., 2020), model validation, conditional reliability estimation, examinee mastery classification following Embretson (2019) <doi:10.1007/978-3-030-05584-4_9>, and individual diagnostic reports at the rule and component level. Supports user-defined prior distributions for all model parameters.

This package provides a modular and computationally efficient R package for parameterizing, simulating, and analyzing health economic simulation models. The package supports cohort discrete time state transition models (Briggs et al. 1998) <doi:10.2165/00019053-199813040-00003>, N-state partitioned survival models (Glasziou et al. 1990) <doi:10.1002/sim.4780091106>, and individual-level continuous time state transition models (Siebert et al. 2012) <doi:10.1016/j.jval.2012.06.014>, encompassing both Markov (time-homogeneous and time-inhomogeneous) and semi-Markov processes. Decision uncertainty from a cost-effectiveness analysis is quantified with standard graphical and tabular summaries of a probabilistic sensitivity analysis (Claxton et al. 2005, Barton et al. 2008) <doi:10.1002/hec.985>, <doi:10.1111/j.1524-4733.2008.00358.x>. Use of C++ and data.table make individual-patient simulation, probabilistic sensitivity analysis, and incorporation of patient heterogeneity fast.

This package provides a bunch of algorithms based on linear programming for estimating, under the homogeneity hypothesis, RxC ecological contingency tables (or vote transition matrices) using mainly aggregate data (from voting units). References: Pavà a and Romero (2024) <doi:10.1177/00491241221092725>. Pavà a and Romero (2024) <doi:10.1093/jrsssa/qnae013>. Pavà a (2023) <doi:10.1007/s43545-023-00658-y>. Pavà a (2024) <doi:10.1080/0022250X.2024.2423943>. Pavà a (2024) <doi:10.1177/07591063241277064>. Pavà a and Penadés (2024). A bottom-up approach for ecological inference. Romero, Pavà a, Martà n and Romero (2020) <doi:10.1080/02664763.2020.1804842>. Acknowledgements: The authors wish to thank Consellerà a de Educación, Cultura, Universidades y Empleo, Generalitat Valenciana (grants AICO/2021/257, CIAICO/2023/031) and MICIU/AEI/10.13039/501100011033/FEDER, UE (grant PID2021-128228NB-I00) for supporting this research.

This package provides tools for generating dynamic and standardized economic narratives in R Markdown documents. The package is primarily designed for French-language statistical and economic publications. It includes functions to describe changes in levels, percentages, trends, accelerations and short-term economic developments using consistent linguistic rules. The package supports automated reporting workflows and reproducible economic writing. Fournit des outils permettant de générer des textes économiques dynamiques et standardisés dans des documents R Markdown. Le package est principalement conçu pour les publications statistiques et économiques en français. Il propose des fonctions permettant de décrire les évolutions de niveaux, de pourcentages, de tendances, d'accélérations et les évolutions conjoncturelles à l'aide de règles linguistiques homogènes. Le package facilite l'automatisation de la rédaction et la reproductibilité des publications économiques.

This package provides the hyphenation algorithm used for TeX'/'LaTeX and similar software, as proposed by Liang (1983, <https://tug.org/docs/liang/>). Mainly contains the function hyphen() to be used for hyphenation/syllable counting of text objects. It was originally developed for and part of the koRpus package, but later released as a separate package so it's lighter to have this particular functionality available for other packages. Support for various languages needs be added on-the-fly or by plugin packages (<https://undocumeantit.github.io/repos/>); this package does not include any language specific data. Due to some restrictions on CRAN, the full package sources are only available from the project homepage. To ask for help, report bugs, request features, or discuss the development of the package, please subscribe to the koRpus-dev mailing list (<http://korpusml.reaktanz.de>).

This package contains various functions to be used for simulation education, including simple Monte Carlo simulation functions, queueing simulation functions, variate generation functions capable of producing independent streams and antithetic variates, functions for illustrating random variate generation for various discrete and continuous distributions, and functions to compute time-persistent statistics. Also contains functions for visualizing: event-driven details of a single-server queue model; a Lehmer random number generator; variate generation via acceptance-rejection; and of generating a non-homogeneous Poisson process via thinning. Also contains two queueing data sets (one fabricated, one real-world) to facilitate input modeling. More details on the use of these functions can be found in Lawson and Leemis (2015) <doi:10.1109/WSC.2017.8248124>, in Kudlay, Lawson, and Leemis (2020) <doi:10.1109/WSC48552.2020.9384010>, and in Lawson and Leemis (2021) <doi:10.1109/WSC52266.2021.9715299>.

mitch is an R package for multi-contrast enrichment analysis. At it’s heart, it uses a rank-MANOVA based statistical approach to detect sets of genes that exhibit enrichment in the multidimensional space as compared to the background. The rank-MANOVA concept dates to work by Cox and Mann (https://doi.org/10.1186/1471-2105-13-S16-S12). mitch is useful for pathway analysis of profiling studies with one, two or more contrasts, or in studies with multiple omics profiling, for example proteomic, transcriptomic, epigenomic analysis of the same samples. mitch is perfectly suited for pathway level differential analysis of scRNA-seq data. We have an established routine for pathway enrichment of Infinium Methylation Array data (see vignette). The main strengths of mitch are that it can import datasets easily from many upstream tools and has advanced plotting features to visualise these enrichments.

Analyses of Proportions can be performed on the Anscombe (arcsine-related) transformed data. The ANOPA package can analyze proportions obtained from up to four factors. The factors can be within-subject or between-subject or a mix of within- and between-subject. The main, omnibus analysis can be followed by additive decompositions into interaction effects, main effects, simple effects, contrast effects, etc., mimicking precisely the logic of ANOVA. For that reason, we call this set of tools ANOPA (Analysis of Proportion using Anscombe transform) to highlight its similarities with ANOVA. The ANOPA framework also allows plots of proportions easy to obtain along with confidence intervals. Finally, effect sizes and planning statistical power are easily done under this framework. Only particularity, the ANOPA computes F statistics which have an infinite degree of freedom on the denominator. See Laurencelle and Cousineau (2023) <doi:10.3389/fpsyg.2022.1045436>.

Estimation of Bayesian Global Vector Autoregressions (BGVAR) with different prior setups and the possibility to introduce stochastic volatility. Built-in priors include the Minnesota, the stochastic search variable selection and Normal-Gamma (NG) prior. For a reference see also Crespo Cuaresma, J., Feldkircher, M. and F. Huber (2016) "Forecasting with Global Vector Autoregressive Models: a Bayesian Approach", Journal of Applied Econometrics, Vol. 31(7), pp. 1371-1391 <doi:10.1002/jae.2504>. Post-processing functions allow for doing predictions, structurally identify the model with short-run or sign-restrictions and compute impulse response functions, historical decompositions and forecast error variance decompositions. Plotting functions are also available. The package has a companion paper: Boeck, M., Feldkircher, M. and F. Huber (2022) "BGVAR: Bayesian Global Vector Autoregressions with Shrinkage Priors in R", Journal of Statistical Software, Vol. 104(9), pp. 1-28 <doi:10.18637/jss.v104.i09>.

This software does Multi-Reader, Multi-Case (MRMC) analyses of data from imaging studies where clinicians (readers) evaluate patient images (cases). What does this mean? ... Many imaging studies are designed so that every reader reads every case in all modalities, a fully-crossed study. In this case, the data is cross-correlated, and we consider the readers and cases to be cross-correlated random effects. An MRMC analysis accounts for the variability and correlations from the readers and cases when estimating variances, confidence intervals, and p-values. The functions in this package can treat arbitrary study designs and studies with missing data, not just fully-crossed study designs. An overview of this software, including references presenting details on the methods, can be found here: <https://www.fda.gov/medical-devices/science-and-research-medical-devices/imrmc-software-do-multi-reader-multi-case-statistical-analysis-reader-studies>.

Optimal Subset Cardinality Regression (OSCAR) models offer regularized linear regression using the L0-pseudonorm, conventionally known as the number of non-zero coefficients. The package estimates an optimal subset of features using the L0-penalization via cross-validation, bootstrapping and visual diagnostics. Effective Fortran implementations are offered along the package for finding optima for the DC-decomposition, which is used for transforming the discrete L0-regularized optimization problem into a continuous non-convex optimization task. These optimization modules include DBDC ('Double Bundle method for nonsmooth DC optimization as described in Joki et al. (2018) <doi:10.1137/16M1115733>) and LMBM ('Limited Memory Bundle Method for large-scale nonsmooth optimization as in Haarala et al. (2004) <doi:10.1080/10556780410001689225>). The OSCAR models are comprehensively exemplified in Halkola et al. (2023) <doi:10.1371/journal.pcbi.1010333>). Multiple regression model families are supported: Cox, logistic, and Gaussian.

Forms a query to submit for US Treasury yield curve data, posting this query to the US Treasury web site's data feed service. By default the download includes data yield data for 12 products from January 1, 1990, some of which are NA during this span. The caller can pass parameters to limit the query to a certain year or year and month, but the full download is not especially large. The download data from the service is in XML format. The package's main function transforms that XML data into a numeric data frame with treasury product items (constant maturity yields for 12 kinds of bills, notes, and bonds) as columns and dates as row names. The function returns a list which includes an item for this data frame as well as query-related values for reference and the update date from the service.

Perform association tests using generalized linear mixed models (GLMMs) in genome-wide association studies (GWAS) and sequencing association studies. First, GMMAT fits a GLMM with covariate adjustment and random effects to account for population structure and familial or cryptic relatedness. For GWAS, GMMAT performs score tests for each genetic variant as proposed in Chen et al. (2016) <DOI:10.1016/j.ajhg.2016.02.012>. For candidate gene studies, GMMAT can also perform Wald tests to get the effect size estimate for each genetic variant. For rare variant analysis from sequencing association studies, GMMAT performs the variant Set Mixed Model Association Tests (SMMAT) as proposed in Chen et al. (2019) <DOI:10.1016/j.ajhg.2018.12.012>, including the burden test, the sequence kernel association test (SKAT), SKAT-O and an efficient hybrid test of the burden test and SKAT, based on user-defined variant sets.

Calculate posterior joint and conditional probabilities, probability distributions of population frequencies, information-theoretic measures, and expected utilities, by means of Bayesian nonparametrics. Data can be any combination of nominal, ordinal, continuous, censored, rounded types. Data imputation is automatic and done in a principled way. Markov-chain Monte Carlo calculations are automatically handled and do not require user supervision. Applications range from statistical estimation and probabilistic hypothesis testing to evidence-based inference and decision making, in a wide range of disciplines from astrophysics to medicine. For more details and examples see for instance Porta Mana & al. (2026) <doi:10.31219/osf.io/8nr56>, Dunson & Bhattacharya (2011) <doi:10.1093/acprof:oso/9780199694587.003.0005>, Lindley & Novick (1981) <doi:10.1214/aos/1176345331>, Bernardo & Smith (2000) <doi:10.1002/9780470316870>, Müller et al. (2015) <doi:10.1007/978-3-319-18968-0>. Data-training function requires the package Nimble'.

This package implements the discrete nonlinear filter (DNF) of Kitagawa (1987) <doi:10.1080/01621459.1987.10478534> to a wide class of stochastic volatility (SV) models with return and volatility jumps following the work of Bégin and Boudreault (2021) <doi:10.1080/10618600.2020.1840995> to obtain likelihood evaluations and maximum likelihood parameter estimates. Offers several built-in SV models and a flexible framework for users to create customized models by specifying drift and diffusion functions along with an arrival distribution for the return and volatility jumps. Allows for the estimation of factor models with stochastic volatility (e.g., heteroskedastic volatility CAPM) by incorporating expected return predictors. Also includes functions to compute filtering and prediction distribution estimates, to simulate data from built-in and custom SV models with jumps, and to forecast future returns and volatility values using Monte Carlo simulation from a given SV model.

Thinning (skeletonization) algorithms for binary raster images. Provides seven algorithms behind a single dispatching function: Zhang-Suen (Zhang and Suen 1984) <doi:10.1145/357994.358023>, Guo-Hall (Guo and Hall 1989) <doi:10.1145/62065.62074>, a 2-D adaptation of Lee (Lee, Kashyap, and Chu 1994) <doi:10.1006/cgip.1994.1042>, K3M (Saeed, Tabedzki, Rybnik, and Adamski 2010) <doi:10.2478/v10006-010-0024-4>, the parallel form commonly attributed to Hilditch (1969, in Machine Intelligence 4'), OPTA / SPTA (Naccache and Shinghal 1984), and Holt and colleagues (1987) <doi:10.1145/12527.12531>. Also provides the medial axis transform (Blum 1967) and a distance transform implementation following Felzenszwalb and Huttenlocher (2012) <doi:10.4086/toc.2012.v008a019>. The thin() API selects the algorithm by name, defaulting to Zhang-Suen. Complements the morphology in the EBImage package, which does not provide a thinning operator.

Univariate and multivariate temporal and spatial diversity indices, rank abundance curves, and community stability measures. The functions implement measures that are either explicitly temporal and include the option to calculate them over multiple replicates, or spatial and include the option to calculate them over multiple time points. Functions fall into five categories: static diversity indices, temporal diversity indices, spatial diversity indices, rank abundance curves, and community stability measures. The diversity indices are temporal and spatial analogs to traditional diversity indices. Specifically, the package includes functions to calculate community richness, evenness and diversity at a given point in space and time. In addition, it contains functions to calculate species turnover, mean rank shifts, and lags in community similarity between two time points. Details of the methods are available in Hallett et al. (2016) <doi:10.1111/2041-210X.12569> and Avolio et al. (2019) <doi:10.1002/ecs2.2881>.

Fast categorization of items based on external code data identified by regular expressions. A typical use case considers patient with medically coded data, such as codes from the International Classification of Diseases ('ICD') or the Anatomic Therapeutic Chemical ('ATC') classification system. Functions of the package relies on a triad of objects: (1) case data with unit id:s and possible dates of interest; (2) external code data for corresponding units in (1) and with optional dates of interest and; (3) a classification scheme ('classcodes object) with regular expressions to identify and categorize relevant codes from (2). It is easy to introduce new classification schemes ('classcodes objects) or to use default schemes included in the package. Use cases includes patient categorization based on comorbidity indices such as Charlson', Elixhauser', RxRisk V', or the comorbidity-polypharmacy score (CPS), as well as adverse events after hip and knee replacement surgery.