This package provides an interface to the FORCIS database (Chaabane et al. (2024) <doi:10.5281/zenodo.7390791>) on global foraminifera distribution. This package allows to download and to handle FORCIS data. It is part of the FRB-CESAB working group FORCIS. <https://www.fondationbiodiversite.fr/en/the-frb-in-action/programs-and-projects/le-cesab/forcis/>.

This package provides a selection of 3 different inference rules (including additionally the clamped types of the referred inference rules) and 4 threshold functions in order to obtain the inference of the FCM (Fuzzy Cognitive Map). Moreover, the fcm package returns a data frame of the concepts values of each state after the inference procedure. Fuzzy cognitive maps were introduced by Kosko (1986) <doi:10.1002/int.4550010405> providing ideal causal cognition tools for modeling and simulating dynamic systems.

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.

This package provides a general estimation framework for multi-state Markov processes with flexible specification of the transition intensities. The log-transition intensities can be specified through Generalised Additive Models which allow for virtually any type of covariate effect. Elementary specifications such as time-homogeneous processes and simple parametric forms are also supported. There are no limitations on the type of process one can assume, with both forward and backward transitions allowed and virtually any number of states.

Perform variable selection in settings with possibly missing data based on extrinsic (algorithm-specific) and intrinsic (population-level) variable importance. Uses a Super Learner ensemble to estimate the underlying prediction functions that give rise to estimates of variable importance. For more information about the methods, please see Williamson and Huang (2024) <doi:10.1515/ijb-2023-0059>.

This package contains financial math functions and introductory derivative functions included in the Society of Actuaries and Casualty Actuarial Society Financial Mathematics exam, and some topics in the Models for Financial Economics exam.

Fit a fractional binomial regression model and extended zero-inflated negative binomial regression model to count data with excess zeros using maximum likelihood estimation. Compare zero-inflated regression models via Vuong closeness test.

This package provides a convenient and user-friendly interface to interact with the Firebase Authentication REST API': <https://firebase.google.com/docs/reference/rest/auth>. It enables R developers to integrate Firebase Authentication services seamlessly into their projects, allowing for user authentication, account management, and other authentication-related tasks.

This package provides functions that calculates common types of splitting criteria used in random forests for classification problems, as well as functions that make predictions based on a single tree or a Forest-R.K. model; the package also provides functions to generate importance plot for a Forest-R.K. model, as well as the 2D multidimensional-scaling plot of data points that are colour coded by their predicted class types by the Forest-R.K. model. This package is based on: Bernard, S., Heutte, L., Adam, S., (2008, ISBN:978-3-540-85983-3) "Forest-R.K.: A New Random Forest Induction Method", Fourth International Conference on Intelligent Computing, September 2008, Shanghai, China, pp.430-437.

Generate SPSS'/'SAS styled frequency tables. Frequency tables are generated with variable and value label attributes where applicable with optional html output to quickly examine datasets.

This package provides an alternative to facilitate the construction of a phylogeny for fish species from a list of species or a community matrix using as a backbone the phylogenetic tree proposed by Rabosky et al. (2018) <doi:10.1038/s41586-018-0273-1>.

Process raw force-plate data (txt-files) by segmenting them into trials and, if needed, calculating (user-defined) descriptive statistics of variables for user-defined time bins (relative to trigger onsets) for each trial. When segmenting the data a baseline correction, a filter, and a data imputation can be applied if needed. Experimental data can also be processed and combined with the segmented force-plate data. This procedure is suggested by Johannsen et al. (2023) <doi:10.6084/m9.figshare.22190155> and some of the options (e.g., choice of low-pass filter) are also suggested by Winter (2009) <doi:10.1002/9780470549148>.

Helps access various Fantasy Football APIs by handling authentication and rate-limiting, forming appropriate calls, and returning tidy dataframes which can be easily connected to other data sources.

Statistical hypothesis testing methods for inferring model-free functional dependency using asymptotic chi-squared or exact distributions. Functional test statistics are asymmetric and functionally optimal, unique from other related statistics. Tests in this package reveal evidence for causality based on the causality-by- functionality principle. They include asymptotic functional chi-squared tests (Zhang & Song 2013) <doi:10.48550/arXiv.1311.2707>, an adapted functional chi-squared test (Kumar & Song 2022) <doi:10.1093/bioinformatics/btac206>, and an exact functional test (Zhong & Song 2019) <doi:10.1109/TCBB.2018.2809743> (Nguyen et al. 2020) <doi:10.24963/ijcai.2020/372>. The normalized functional chi-squared test was used by Best Performer NMSUSongLab in HPN-DREAM (DREAM8) Breast Cancer Network Inference Challenges (Hill et al. 2016) <doi:10.1038/nmeth.3773>. A function index (Zhong & Song 2019) <doi:10.1186/s12920-019-0565-9> (Kumar et al. 2018) <doi:10.1109/BIBM.2018.8621502> derived from the functional test statistic offers a new effect size measure for the strength of functional dependency, a better alternative to conditional entropy in many aspects. For continuous data, these tests offer an advantage over regression analysis when a parametric functional form cannot be assumed; for categorical data, they provide a novel means to assess directional dependency not possible with symmetrical Pearson's chi-squared or Fisher's exact tests.

The FisherEM algorithm, proposed by Bouveyron & Brunet (2012) <doi:10.1007/s11222-011-9249-9>, is an efficient method for the clustering of high-dimensional data. FisherEM models and clusters the data in a discriminative and low-dimensional latent subspace. It also provides a low-dimensional representation of the clustered data. A sparse version of Fisher-EM algorithm is also provided.

The Food and Agriculture Organization of the United Nations (FAO) FishStat database is the leading source of global fishery and aquaculture statistics and provides unique information for sector analysis and monitoring. This package provides the global production data from all fisheries and aquaculture in R format, ready for analysis.

Statistical tool set for population genetics. The package provides following functions: 1) estimators of genetic differentiation (FST), 2) regression analysis of environmental effects on genetic differentiation using generalized least squares (GLS) method, 3) interfaces to read and manipulate GENEPOP format data files). For more information, see Kitada, Nakamichi and Kishino (2020) <doi:10.1101/2020.01.30.927186>.

This package provides a small subset of plots throughout the U.S. are sampled and assessed "on-the-ground" as forested or non-forested by the U.S. Department of Agriculture, Forest Service, Forest Inventory and Analysis (FIA) Program, but the FIA also has access to remotely sensed data for all land in the country. The forested package contains data frames intended for use in predictive modeling applications where the more easily-accessible remotely sensed data can be used to predict whether a plot is forested or non-forested. Currently, the package provides data for Washington and Georgia.

The funFEM algorithm (Bouveyron et al., 2014) allows to cluster functional data by modeling the curves within a common and discriminative functional subspace.

Allows prophet models from the prophet package to be used in a tidy workflow with the modelling interface of fabletools'. This extends prophet to provide enhanced model specification and management, performance evaluation methods, and model combination tools.

The main goal of this package is to present various fuzzy statistical tools. It intends to provide an implementation of the theoretical and empirical approaches presented in the book entitled "The signed distance measure in fuzzy statistical analysis. Some theoretical, empirical and programming advances" <doi: 10.1007/978-3-030-76916-1>. For the theoretical approaches, see Berkachy R. and Donze L. (2019) <doi:10.1007/978-3-030-03368-2_1>. For the empirical approaches, see Berkachy R. and Donze L. (2016) <ISBN: 978-989-758-201-1>). Important (non-exhaustive) implementation highlights of this package are as follows: (1) a numerical procedure to estimate the fuzzy difference and the fuzzy square. (2) two numerical methods of fuzzification. (3) a function performing different possibilities of distances, including the signed distance and the generalized signed distance for instance with all its properties. (4) numerical estimations of fuzzy statistical measures such as the variance, the moment, etc. (5) two methods of estimation of the bootstrap distribution of the likelihood ratio in the fuzzy context. (6) an estimation of a fuzzy confidence interval by the likelihood ratio method. (7) testing fuzzy hypotheses and/or fuzzy data by fuzzy confidence intervals in the Kwakernaak - Kruse and Meyer sense. (8) a general method to estimate the fuzzy p-value with fuzzy hypotheses and/or fuzzy data. (9) a method of estimation of global and individual evaluations of linguistic questionnaires. (10) numerical estimations of multi-ways analysis of variance models in the fuzzy context. The unbalance in the considered designs are also foreseen.

An interface to the core Familias functions which are programmed in C++. The implementation is described in Egeland, Mostad and Olaisen (1997) <doi:10.1016/S1355-0306(97)72202-0> and Simonsson and Mostad (2016) <doi:10.1016/j.fsigen.2016.04.005>.

Use Rmarkdown First method to build your package. Start your package with documentation, functions, examples and tests in the same unique file. Everything can be set from the Rmarkdown template file provided in your project, then inflated as a package. Inflating the template copies the relevant chunks and sections in the appropriate files required for package development.

This package provides a parametrization framework for finite mixture distribution using S4 objects. Density, cumulative density, quantile and simulation functions are defined. Currently normal, Tukey g-&-h, skew-normal and skew-t distributions are well tested. The gamma, negative binomial distributions are being tested.