This contains functions that can be used to estimate a smoothed and a non-smoothed (empirical) time-dependent receiver operating characteristic curve and the corresponding area under the receiver operating characteristic curve for correlated right-censored time-to-event data. See Beyene and Chen (2024) <doi:10.1177/09622802231220496>.

Estimates Filtered Monotonic Polynomial IRT Models as described by Liang and Browne (2015) <DOI:10.3102/1076998614556816>.

This package provides a function composition operator to chain a series of calls into a single function, mimicking the math notion of (f o g o h)(x) = h(g(f(x))). Inspired by pipeOp ('|>') since R4.1 and magrittr pipe ('%>%'), the operator build a pipe without putting data through, which is best for anonymous function accepted by utilities such as apply() and lapply().

This package provides a financial calculator that provides very fast implementations of common financial indicators using Rust code. It includes functions for bond-related indicators, such as yield to maturity ('YTM'), modified duration, and Macaulay duration, as well as functions for calculating time-weighted and money-weighted rates of return (using Modified Dietz method) for multiple portfolios, given their market values and profit and loss ('PnL') data. fcl is designed to be efficient and accurate for financial analysis and computation. The methods used in this package are based on the following references: <https://en.wikipedia.org/wiki/Modified_Dietz_method>, <https://en.wikipedia.org/wiki/Time-weighted_return>.

This package provides a model for leaf fluorescence, reflectance and transmittance spectra. It implements the model introduced by Vilfan et al. (2016) <DOI:10.1016/j.rse.2016.09.017>. Fluspect-B calculates the emission of ChlF on both the illuminated and shaded side of the leaf. Other input parameters are chlorophyll and carotenoid concentrations, leaf water, dry matter and senescent material (brown pigments) content, leaf mesophyll structure parameter and ChlF quantum efficiency for the two photosystems, PS-I and PS-II.

FamSKAT-RC is a family-based association kernel test for both rare and common variants. This test is general and several special cases are known as other methods: famSKAT, which only focuses on rare variants in family-based data, SKAT, which focuses on rare variants in population-based data (unrelated individuals), and SKAT-RC, which focuses on both rare and common variants in population-based data. When one applies famSKAT-RC and sets the value of phi to 1, famSKAT-RC becomes famSKAT. When one applies famSKAT-RC and set the value of phi to 1 and the kinship matrix to the identity matrix, famSKAT-RC becomes SKAT. When one applies famSKAT-RC and set the kinship matrix (fullkins) to the identity matrix (and phi is not equal to 1), famSKAT-RC becomes SKAT-RC. We also include a small sample synthetic pedigree to demonstrate the method with. For more details see Saad M and Wijsman EM (2014) <doi:10.1002/gepi.21844>.

This package provides a collection of functions to optimize portfolios and to analyze them from different points of view.

This package provides a simple and efficient wrapper around the fastest Fourier transform in the west (FFTW) library <http://www.fftw.org/>.

Extensive global and small-area estimation procedures for multiphase forest inventories under the design-based Monte-Carlo approach are provided. The implementation has been published in the Journal of Statistical Software (<doi:10.18637/jss.v097.i04>) and includes estimators for simple and cluster sampling published by Daniel Mandallaz in 2007 (<doi:10.1201/9781584889779>), 2013 (<doi:10.1139/cjfr-2012-0381>, <doi:10.1139/cjfr-2013-0181>, <doi:10.1139/cjfr-2013-0449>, <doi:10.3929/ethz-a-009990020>) and 2016 (<doi:10.3929/ethz-a-010579388>). It provides point estimates, their external- and design-based variances and confidence intervals, as well as a set of functions to analyze and visualize the produced estimates. The procedures have also been optimized for the use of remote sensing data as auxiliary information, as demonstrated in 2018 by Hill et al. (<doi:10.3390/rs10071052>).

Support for fuzzy spatial objects, their operations, and fuzzy spatial inference models based on Spatial Plateau Algebra. It employs fuzzy set theory and fuzzy logic as foundation to deal with spatial fuzziness. It mainly implements underlying concepts defined in the following research papers: (i) "Spatial Plateau Algebra: An Executable Type System for Fuzzy Spatial Data Types" <doi:10.1109/FUZZ-IEEE.2018.8491565>; (ii) "A Systematic Approach to Creating Fuzzy Region Objects from Real Spatial Data Sets" <doi:10.1109/FUZZ-IEEE.2019.8858878>; (iii) "Spatial Data Types for Heterogeneously Structured Fuzzy Spatial Collections and Compositions" <doi:10.1109/FUZZ48607.2020.9177620>; (iv) "Fuzzy Inference on Fuzzy Spatial Objects (FIFUS) for Spatial Decision Support Systems" <doi:10.1109/FUZZ-IEEE.2017.8015707>; (v) "Evaluating Region Inference Methods by Using Fuzzy Spatial Inference Models" <doi:10.1109/FUZZ-IEEE55066.2022.9882658>.

This package provides a set of functions that facilitate basic data manipulation and cleaning for statistical analysis including functions for finding and fixing duplicate rows and columns, missing values, outliers, and special characters in column and row names and functions for checking data consistency, distribution, quality, reliability, and structure.

This package provides a collection of user-friendly functions for assessing and visualizing fragility of individual studies (Walsh et al., 2014 <doi:10.1016/j.jclinepi.2013.10.019>; Lin, 2021 <doi:10.1111/jep.13428>), conventional pairwise meta-analyses (Atal et al., 2019 <doi:10.1016/j.jclinepi.2019.03.012>), and network meta-analyses of multiple treatments with binary outcomes (Xing et al., 2020 <doi:10.1016/j.jclinepi.2020.07.003>). The included functions are designed to: 1) calculate the fragility index (i.e., the minimal event status modifications that can alter the significance or non-significance of the original result) and fragility quotient (i.e., fragility index divided by sample size) at a specific significance level; 2) give the cases of event status modifications for altering the result's significance or non-significance and visualize these cases; 3) visualize the trend of statistical significance as event status is modified; 4) efficiently derive fragility indexes and fragility quotients at multiple significance levels, and visualize the relationship between these fragility measures against the significance levels; and 5) calculate fragility indexes and fragility quotients of multiple datasets (e.g., a collection of clinical trials or meta-analyses) and produce plots of their overall distributions. The outputs from these functions may inform the robustness of clinical results in terms of statistical significance and aid the interpretation of fragility measures. The usage of this package is illustrated in Lin et al. (2023 <doi:10.1016/j.ajog.2022.08.053>) and detailed in Lin and Chu (2022 <doi:10.1371/journal.pone.0268754>).

Enables filtering datasets by a prior specified identifiers which correspond to saved filter expressions.

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

The FastRCS algorithm of Vakili and Schmitt (2014) for robust fit of the multivariable linear regression model and outliers detection.

This package provides a wrapper for the python module FIORA as well as a shiny'-App to facilitate data processing and visualization. FIORA allows to predict Mass-Spectra based on the SMILES code of chemical compounds. It is described in the Nature Communications article by Nowatzky (2025) <doi:10.1038/s41467-025-57422-4>.

This package provides a variety of original and flexible user-friendly statistical latent variable models for the simultaneous clustering and segmentation of heterogeneous functional data (i.e time series, or more generally longitudinal data, fitted by unsupervised algorithms, including EM algorithms. Functional Latent Data Models for Clustering heterogeneous curves ('FLaMingos') are originally introduced and written in Matlab by Faicel Chamroukhi <https://github.com/fchamroukhi?utf8=?&tab=repositories&q=mix&type=public&language=matlab>. The references are mainly the following ones. Chamroukhi F. (2010) <https://chamroukhi.com/FChamroukhi-PhD.pdf>. Chamroukhi F., Same A., Govaert, G. and Aknin P. (2010) <doi:10.1016/j.neucom.2009.12.023>. Chamroukhi F., Same A., Aknin P. and Govaert G. (2011). <doi:10.1109/IJCNN.2011.6033590>. Same A., Chamroukhi F., Govaert G. and Aknin, P. (2011) <doi:10.1007/s11634-011-0096-5>. Chamroukhi F., and Glotin H. (2012) <doi:10.1109/IJCNN.2012.6252818>. Chamroukhi F., Glotin H. and Same A. (2013) <doi:10.1016/j.neucom.2012.10.030>. Chamroukhi F. (2015) <https://chamroukhi.com/FChamroukhi-HDR.pdf>. Chamroukhi F. and Nguyen H-D. (2019) <doi:10.1002/widm.1298>.

Efficient estimation of maximum likelihood models with multiple fixed-effects. Standard-errors can easily and flexibly be clustered and estimations exported.

Perform optimal transport based tests in factorial designs as introduced in Groppe et al. (2025) <doi:10.48550/arXiv.2509.13970> via the FDOTT() function. These tests are inspired by ANOVA and its nonparametric counterparts. They allow for testing linear relationships in factorial designs between finitely supported probability measures on a metric space. Such relationships include equality of all measures (no treatment effect), interaction effects between a number of factors, as well as main and simple factor effects.

High-order functions for data manipulation : sort or group data, given one or more auxiliary functions. Functions are inspired by other pure functional programming languages ('Haskell mainly). The package also provides built-in function operators for creating compact anonymous functions, as well as the possibility to use the purrr package syntax.

Generate cost effective minimally changed run sequences for symmetrical as well as asymmetrical factorial designs.

This package provides functions for the calculation of greenhouse gas flux rates from closed chamber concentration measurements. The package follows a modular concept: Fluxes can be calculated in just two simple steps or in several steps if more control in details is wanted. Additionally plot and preparation functions as well as functions for modelling gpp and reco are provided.

It implements an improved and computationally faster version of the original Stepwise Gaussian Graphical Algorithm for estimating the Omega precision matrix from high-dimensional data. Zamar, R., Ruiz, M., Lafit, G. and Nogales, J. (2021) <doi:10.52933/jdssv.v1i2.11>.

Samples generalized random product graphs, a generalization of a broad class of network models. Given matrices X, S, and Y with with non-negative entries, samples a matrix with expectation X S Y^T and independent Poisson or Bernoulli entries using the fastRG algorithm of Rohe et al. (2017) <https://www.jmlr.org/papers/v19/17-128.html>. The algorithm first samples the number of edges and then puts them down one-by-one. As a result it is O(m) where m is the number of edges, a dramatic improvement over element-wise algorithms that which require O(n^2) operations to sample a random graph, where n is the number of nodes.