This package implements a very fast C++ algorithm to quickly bootstrap receiver operating characteristics (ROC) curves and derived performance metrics, including the area under the curve (AUC) and the partial area under the curve as well as the true and false positive rate. The analysis of paired receiver operating curves is supported as well, so that a comparison of two predictors is possible. You can also plot the results and calculate confidence intervals. On a typical desktop computer the time needed for the calculation of 100000 bootstrap replicates given 500 observations requires time on the order of magnitude of one second.

An implementation of multiple regression models for count data. These include various forms of the negative binomial (NB-1, NB-2, NB-P, generalized negative binomial, etc.), Poisson-Lognormal, other compound Poisson distributions, the Generalized Waring model, etc. Information on the different forms of the negative binomial are described by Greene (2008) <doi:10.1016/j.econlet.2007.10.015>. For treatises on count models, see Cameron and Trivedi (2013) <doi:10.1017/CBO9781139013567> and Hilbe (2012) <doi:10.1017/CBO9780511973420>. For the implementation of under-reporting in count models, see Wood et al. (2016) <doi:10.1016/j.aap.2016.06.013>. For prediction methods in random parameter models, see Wood and Gayah (2025) <doi:10.1016/j.aap.2025.108147>. For estimating random parameters using maximum simulated likelihood, see Greene and Hill (2010) <doi:10.1108/S0731-9053(2010)26>; Gourieroux and Monfort (1996) <doi:10.1093/0198774753.001.0001>; or Hensher et al. (2015) <doi:10.1017/CBO9781316136232>.

Allows the user to implement easily canvas elements within a shiny app or an RMarkdown document. The user can create shapes, images and text elements within the canvas which can also be used as a drawing tool for taking notes. The package relies on the fabricjs JavaScript library. See <http://fabricjs.com/>.

An implementation of various learning algorithms based on fuzzy rule-based systems (FRBSs) for dealing with classification and regression tasks. Moreover, it allows to construct an FRBS model defined by human experts. FRBSs are based on the concept of fuzzy sets, proposed by Zadeh in 1965, which aims at representing the reasoning of human experts in a set of IF-THEN rules, to handle real-life problems in, e.g., control, prediction and inference, data mining, bioinformatics data processing, and robotics. FRBSs are also known as fuzzy inference systems and fuzzy models. During the modeling of an FRBS, there are two important steps that need to be conducted: structure identification and parameter estimation. Nowadays, there exists a wide variety of algorithms to generate fuzzy IF-THEN rules automatically from numerical data, covering both steps. Approaches that have been used in the past are, e.g., heuristic procedures, neuro-fuzzy techniques, clustering methods, genetic algorithms, squares methods, etc. Furthermore, in this version we provide a universal framework named frbsPMML', which is adopted from the Predictive Model Markup Language (PMML), for representing FRBS models. PMML is an XML-based language to provide a standard for describing models produced by data mining and machine learning algorithms. Therefore, we are allowed to export and import an FRBS model to/from frbsPMML'. Finally, this package aims to implement the most widely used standard procedures, thus offering a standard package for FRBS modeling to the R community.

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.

This package contains the methods proposed by Geyer and Meeden (2005)<doi:10.1214/088342305000000340> and Trigo et al. (2025) <doi:10.47749/T/UNICAMP.2025.1500297> to construct fuzzy confidence intervals. Compute and plot the fuzzy membership functions of the methods, and the expected length compared with the infimum.

Original idea was presented in the reference paper. Varghese et al. (2020, 74(1):35-42) "Bayesian State-space Implementation of Schaefer Production Model for Assessment of Stock Status for Multi-gear Fishery". Marine fisheries governance and management practices are very essential to ensure the sustainability of the marine resources. A widely accepted resource management strategy towards this is to derive sustainable fish harvest levels based on the status of marine fish stock. Various fish stock assessment models that describe the biomass dynamics using time series data on fish catch and fishing effort are generally used for this purpose. In the scenario of complex multi-species marine fishery in which different species are caught by a number of fishing gears and each gear harvests a number of species make it difficult to obtain the fishing effort corresponding to each fish species. Since the capacity of the gears varies, the effort made to catch a resource cannot be considered as the sum of efforts expended by different fishing gears. This necessitates standardisation of fishing effort in unit base.

An interface to the fastText library <https://github.com/facebookresearch/fastText>. The package can be used for text classification and to learn word vectors. An example how to use fastTextR can be found in the README file.

This package provides generic data structures and algorithms for use with forest mensuration data in a consistent framework. The functions and objects included are a collection of broadly applicable tools. More specialized applications should be implemented in separate packages that build on this foundation. Documentation about ForestElementsR is provided by three vignettes included in this package. For an introduction to the field of forest mensuration, refer to the textbooks by Kershaw et al. (2017) <doi:10.1002/9781118902028>, and van Laar and Akca (2007) <doi:10.1007/978-1-4020-5991-9>.

The following several classes of frailty models using a penalized likelihood estimation on the hazard function but also a parametric estimation can be fit using this R package: 1) A shared frailty model (with gamma or log-normal frailty distribution) and Cox proportional hazard model. Clustered and recurrent survival times can be studied. 2) Additive frailty models for proportional hazard models with two correlated random effects (intercept random effect with random slope). 3) Nested frailty models for hierarchically clustered data (with 2 levels of clustering) by including two iid gamma random effects. 4) Joint frailty models in the context of the joint modelling for recurrent events with terminal event for clustered data or not. A joint frailty model for two semi-competing risks and clustered data is also proposed. 5) Joint general frailty models in the context of the joint modelling for recurrent events with terminal event data with two independent frailty terms. 6) Joint Nested frailty models in the context of the joint modelling for recurrent events with terminal event, for hierarchically clustered data (with two levels of clustering) by including two iid gamma random effects. 7) Multivariate joint frailty models for two types of recurrent events and a terminal event. 8) Joint models for longitudinal data and a terminal event. 9) Trivariate joint models for longitudinal data, recurrent events and a terminal event. 10) Joint frailty models for the validation of surrogate endpoints in multiple randomized clinical trials with failure-time and/or longitudinal endpoints with the possibility to use a mediation analysis model. 11) Conditional and Marginal two-part joint models for longitudinal semicontinuous data and a terminal event. 12) Joint frailty-copula models for the validation of surrogate endpoints in multiple randomized clinical trials with failure-time endpoints. 13) Generalized shared and joint frailty models for recurrent and terminal events. Proportional hazards (PH), additive hazard (AH), proportional odds (PO) and probit models are available in a fully parametric framework. For PH and AH models, it is possible to consider type-varying coefficients and flexible semiparametric hazard function. Prediction values are available (for a terminal event or for a new recurrent event). Left-truncated (not for Joint model), right-censored data, interval-censored data (only for Cox proportional hazard and shared frailty model) and strata are allowed. In each model, the random effects have the gamma or normal distribution. Now, you can also consider time-varying covariates effects in Cox, shared and joint frailty models (1-5). The package includes concordance measures for Cox proportional hazards models and for shared frailty models. 14) Competing Joint Frailty Model: A single type of recurrent event and two terminal events. 15) functions to compute power and sample size for four Gamma-frailty-based designs: Shared Frailty Models, Nested Frailty Models, Joint Frailty Models, and General Joint Frailty Models. Each design includes two primary functions: a power function, which computes power given a specified sample size; and a sample size function, which computes the required sample size to achieve a specified power. 16) Weibull Illness-Death model with or without shared frailty between transitions. Left-truncated and right-censored data are allowed. 17) Weibull Competing risks model with or without shared frailty between the transitions. Left-truncated and right-censored data are allowed. Moreover, the package can be used with its shiny application, in a local mode or by following the link below.

This package implements the Fixed Effect Jackknife Instrumental Variables ('FEJIV') estimator of Chao, Swanson, and Woutersen (2023) <doi:10.1016/j.jeconom.2022.12.011>, allowing consistent IV estimation with many (possibly weak) instruments, cluster fixed effects, heteroskedastic errors, and many exogenous covariates. The estimator is recommended by SÅ oczyÅ ski (2024) <doi:10.48550/arXiv.2011.06695> as an alternative to two-stage least squares when estimating the interacted specification of Angrist and Imbens (1995) <doi:10.1080/01621459.1995.10476535>.

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.

Model-based clustering of multivariate continuous data using Bayesian mixtures of factor analyzers (Papastamoulis (2019) <DOI:10.1007/s11222-019-09891-z> (2018) <DOI:10.1016/j.csda.2018.03.007>). The number of clusters is estimated using overfitting mixture models (Rousseau and Mengersen (2011) <DOI:10.1111/j.1467-9868.2011.00781.x>): suitable prior assumptions ensure that asymptotically the extra components will have zero posterior weight, therefore, the inference is based on the ``alive components. A Gibbs sampler is implemented in order to (approximately) sample from the posterior distribution of the overfitting mixture. A prior parallel tempering scheme is also available, which allows to run multiple parallel chains with different prior distributions on the mixture weights. These chains run in parallel and can swap states using a Metropolis-Hastings move. Eight different parameterizations give rise to parsimonious representations of the covariance per cluster (following Mc Nicholas and Murphy (2008) <DOI:10.1007/s11222-008-9056-0>). The model parameterization and number of factors is selected according to the Bayesian Information Criterion. Identifiability issues related to label switching are dealt by post-processing the simulated output with the Equivalence Classes Representatives algorithm (Papastamoulis and Iliopoulos (2010) <DOI:10.1198/jcgs.2010.09008>, Papastamoulis (2016) <DOI:10.18637/jss.v069.c01>).

This package provides a toolbox for estimating vector fields from intensive longitudinal data, and construct potential landscapes thereafter. The vector fields can be estimated with two nonparametric methods: the Multivariate Vector Field Kernel Estimator (MVKE) by Bandi & Moloche (2018) <doi:10.1017/S0266466617000305> and the Sparse Vector Field Consensus (SparseVFC) algorithm by Ma et al. (2013) <doi:10.1016/j.patcog.2013.05.017>. The potential landscapes can be constructed with a simulation-based approach with the simlandr package (Cui et al., 2021) <doi:10.31234/osf.io/pzva3>, or the Bhattacharya et al. (2011) method for path integration <doi:10.1186/1752-0509-5-85>.

Many statistical models and analyses in R are implemented through formula objects. The formulaic package creates a unified approach for programmatically and dynamically generating formula objects. Users may specify the outcome and inputs of a model directly, search for variables to include based upon naming patterns, incorporate interactions, and identify variables to exclude. A wide range of quality checks are implemented to identify issues such as misspecified variables, duplication, a lack of contrast in the inputs, and a large number of levels in categorical data. Variables that do not meet these quality checks can be automatically excluded from the model. These issues are documented and reported in a manner that provides greater accountability and useful information to guide an investigation of the data.

Supports teaching methods of estimating and testing time series factor models for use in robust portfolio construction and analysis. Unique in providing not only classical least squares, but also modern robust model fitting methods which are not much influenced by outliers. Includes returns and risk decompositions, with user choice of standard deviation, value-at-risk, and expected shortfall risk measures. "Robust Statistics Theory and Methods (with R)", R. A. Maronna, R. D. Martin, V. J. Yohai, M. Salibian-Barrera (2019) <doi:10.1002/9781119214656>.

Designed to streamline the process of analyzing genotyping data from Fluidigm machines, this package offers a suite of tools for data handling and analysis. It includes functions for converting Fluidigm data to format used by PLINK', estimating errors, calculating pairwise similarities, determining pairwise similarity loci, and generating a similarity matrix.

This package provides a research estimation tool for analysts that work with sample-based inventory data from the U.S. Department of Agriculture, Forest Service, Forest Inventory and Analysis (FIA) Program.

This package provides tools for estimating causal effects in panel data using counterfactual methods, as well as other modern DID estimators. It is designed for causal panel analysis with binary treatments under the parallel trends assumption. The package supports scenarios where treatments can switch on and off and allows for limited carryover effects. It includes several imputation estimators, such as Gsynth (Xu 2017), linear factor models, and the matrix completion method. Detailed methodology is described in Liu, Wang, and Xu (2024) <doi:10.48550/arXiv.2107.00856> and Chiu et al. (2025) <doi:10.48550/arXiv.2309.15983>. Optionally integrates with the "HonestDiDFEct" package for sensitivity analyses compatible with imputation estimators. "HonestDiDFEct" is not on CRAN but can be obtained from <https://github.com/lzy318/HonestDiDFEct>.

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

Package for time value of money calculation, time series analysis and computational finance.

Fresh biomass determination is the key to evaluating crop genotypes response to diverse input and stress conditions and forms the basis for calculating net primary production. However, as conventional phenotyping approaches for measuring fresh biomass is time-consuming, laborious and destructive, image-based phenotyping methods are being widely used now. In the image-based approach, the fresh weight of the above-ground part of the plant depends on the projected area. For determining the projected area, the visual image of the plant is converted into the grayscale image by simply averaging the Red(R), Green (G) and Blue (B) pixel values. Grayscale image is then converted into a binary image using Otsuâ s thresholding method Otsu, N. (1979) <doi:10.1109/TSMC.1979.4310076> to separate plant area from the background (image segmentation). The segmentation process was accomplished by selecting the pixels with values over the threshold value belonging to the plant region and other pixels to the background region. The resulting binary image consists of white and black pixels representing the plant and background regions. Finally, the number of pixels inside the plant region was counted and converted to square centimetres (cm2) using the reference object (any object whose actual area is known previously) to get the projected area. After that, the projected area is used as input to the machine learning model (Linear Model, Artificial Neural Network, and Support Vector Regression) to determine the plant's fresh weight.

This package provides functions to compute fuzzy versions of species occurrence patterns based on presence-absence data (including inverse distance interpolation, trend surface analysis, and prevalence-independent favourability obtained from probability of presence), as well as pair-wise fuzzy similarity (based on fuzzy logic versions of commonly used similarity indices) among those occurrence patterns. Includes also functions for model consensus and comparison (overlap and fuzzy similarity, fuzzy loss, fuzzy gain), and for data preparation, such as obtaining unique abbreviations of species names, defining the background region, cleaning and gridding (thinning) point occurrence data onto raster maps, selecting among (pseudo)absences to address survey bias, converting species lists (long format) to presence-absence tables (wide format), transposing part of a data frame, selecting relevant variables for models, assessing the false discovery rate, or analysing and dealing with multicollinearity. Initially described in Barbosa (2015) <doi:10.1111/2041-210X.12372>.

This package provides a data package that hosts all models for the nflfastR package.