This package implements the h-likelihood estimation procedures for general frailty models including competing-risk models and joint models.

Enable researchers to adjust identification rates using the 1/(lineup size) method, generate the full receiver operating characteristic (ROC) curves, and statistically compare the area under the curves (AUC). References: Yueran Yang & Andrew Smith. (2020). "fullROC: An R package for generating and analyzing eyewitness-lineup ROC curves". <doi:10.13140/RG.2.2.20415.94885/1> , Andrew Smith, Yueran Yang, & Gary Wells. (2020). "Distinguishing between investigator discriminability and eyewitness discriminability: A method for creating full receiver operating characteristic curves of lineup identification performance". Perspectives on Psychological Science, 15(3), 589-607. <doi:10.1177/1745691620902426>.

Data-driven fMRI denoising with projection scrubbing (Pham et al (2022) <doi:10.1016/j.neuroimage.2023.119972>). Also includes routines for DVARS (Derivatives VARianceS) (Afyouni and Nichols (2018) <doi:10.1016/j.neuroimage.2017.12.098>), motion scrubbing (Power et al (2012) <doi:10.1016/j.neuroimage.2011.10.018>), aCompCor (anatomical Components Correction) (Muschelli et al (2014) <doi:10.1016/j.neuroimage.2014.03.028>), detrending, and nuisance regression. Projection scrubbing is also applicable to other outlier detection tasks involving high-dimensional data.

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.

An R interface to FLINT <https://flintlib.org/>, a C library for number theory. FLINT extends GNU MPFR <https://www.mpfr.org/> and GNU MP <https://gmplib.org/> with support for operations on standard rings (the integers, the integers modulo n, finite fields, the rational, p-adic, real, and complex numbers) as well as matrices and polynomials over rings. FLINT implements midpoint-radius interval arithmetic, also known as ball arithmetic, in the real and complex numbers, enabling computation in arbitrary precision with rigorous propagation of rounding and other errors; see Johansson (2017) <doi:10.1109/TC.2017.2690633>. Finally, FLINT provides ball arithmetic implementations of many special mathematical functions, with high coverage of reference works such as the NIST Digital Library of Mathematical Functions <https://dlmf.nist.gov/>. The R interface defines S4 classes, generic functions, and methods for representation and basic operations as well as plain R functions mirroring and vectorizing entry points in the C library.

This package provides methods and tools designed to improve the forecast accuracy for a linearly constrained multiple time series, while fulfilling the linear/aggregation relationships linking the components (Girolimetto and Di Fonzo, 2024 <doi:10.48550/arXiv.2412.03429>). FoCo2 offers multi-task forecast combination and reconciliation approaches leveraging input from multiple forecasting models or experts and ensuring that the resulting forecasts satisfy specified linear constraints. In addition, linear inequality constraints (e.g., non-negativity of the forecasts) can be imposed, if needed.

Maximum Likelihood Estimation of Stochastic Frontier Production and Cost Functions. Two specifications are available: the error components specification with time-varying efficiencies (Battese and Coelli, 1992, <doi:10.1007/BF00158774>) and a model specification in which the firm effects are directly influenced by a number of variables (Battese and Coelli, 1995, <doi:10.1007/BF01205442>).

This package provides a collection of functions for computing fairness metrics for machine learning and statistical models, including confidence intervals for each metric. The package supports the evaluation of group-level fairness criterion commonly used in fairness research, particularly in healthcare for binary protected attributes. It is based on the overview of fairness in machine learning written by Gao et al (2024) <doi:10.48550/arXiv.2406.09307>.

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

An implementation of regression models with partial differential regularizations, making use of the Finite Element Method. The models efficiently handle data distributed over irregularly shaped domains and can comply with various conditions at the boundaries of the domain. A priori information about the spatial structure of the phenomenon under study can be incorporated in the model via the differential regularization. See Sangalli, L. M. (2021) <doi:10.1111/insr.12444> "Spatial Regression With Partial Differential Equation Regularisation" for an overview. The release 1.1-9 requires R (>= 4.2.0) to be installed on windows machines.

Takes a distance matrix and plots it as an interactive graph. One point is focused at the center of the graph, around which all other points are plotted in their exact distances as given in the distance matrix. All other non-focus points are plotted as best as possible in relation to one another. Double click on any point to choose a new focus point, and hover over points to see their ID labels. If color label categories are given, hover over colors in the legend to highlight only those points and click on colors to highlight multiple groups. For more information on the rationale and mathematical background, as well as an interactive introduction, see <https://lea-urpa.github.io/focusedMDS.html>.

Quantify the serial correlation across lags of a given functional time series using the autocorrelation function and a partial autocorrelation function for functional time series proposed in Mestre et al. (2021) <doi:10.1016/j.csda.2020.107108>. The autocorrelation functions are based on the L2 norm of the lagged covariance operators of the series. Functions are available for estimating the distribution of the autocorrelation functions under the assumption of strong functional white noise.

Supervised, multivariate, and non-parametric discretization algorithm based on tree ensembles learning and moment matching optimization. This version of the algorithm relies on random forest algorithm to learn a large set of split points that conserves the relationship between attributes and the target class, and on moment matching optimization to transform this set into a reduced number of cut points matching as well as possible statistical properties of the initial set of split points. For each attribute to be discretized, the set S of its related split points extracted through random forest is mapped to a reduced set C of cut points of size k. This mapping relies on minimizing, for each continuous attribute to be discretized, the distance between the four first moments of S and the four first moments of C subject to some constraints. This non-linear optimization problem is performed using k values ranging from 2 to max_splits', and the best solution returned correspond to the value k which optimum solution is the lowest one over the different realizations. ForestDisc is a generalization of RFDisc discretization method initially proposed by Berrado and Runger (2009) <doi:10.1109/AICCSA.2009.5069327>, and improved by Berrado et al. in 2012 by adopting the idea of moment matching optimization related by Hoyland and Wallace (2001) <doi: 10.1287/mnsc.47.2.295.9834>.

Query data hosted in Microsoft Fabric'. Provides helpers to open DBI connections to SQL endpoints of Lakehouse and Data Warehouse items; submit Data Analysis Expressions ('DAX') queries to semantic model datasets in Microsoft Fabric and Power BI'; read Delta Lake tables stored in OneLake ('Azure Data Lake Storage Gen2'); and execute Spark code via the Livy API'.

The Forecast Linear Augmented Projection (flap) method reduces forecast variance by adjusting the forecasts of multivariate time series to be consistent with the forecasts of linear combinations (components) of the series by projecting all forecasts onto the space where the linear constraints are satisfied. The forecast variance can be reduced monotonically by including more components. For a given number of components, the flap method achieves maximum forecast variance reduction among linear projections.

This method is a new class of model selection strategies, for mixed model selection, which includes linear and generalized linear mixed models. The idea involves a procedure to isolate a subgroup of what are known as correct models (of which the optimal model is a member). This is accomplished by constructing a statistical fence, or barrier, to carefully eliminate incorrect models. Once the fence is constructed, the optimal model is selected from among those within the fence according to a criterion which can be made flexible. References: 1. Jiang J., Rao J.S., Gu Z., Nguyen T. (2008), Fence Methods for Mixed Model Selection. The Annals of Statistics, 36(4): 1669-1692. <DOI:10.1214/07-AOS517> <https://projecteuclid.org/euclid.aos/1216237296>. 2. Jiang J., Nguyen T., Rao J.S. (2009), A Simplified Adaptive Fence Procedure. Statistics and Probability Letters, 79, 625-629. <DOI:10.1016/j.spl.2008.10.014> <https://www.researchgate.net/publication/23991417_A_simplified_adaptive_fence_procedure> 3. Jiang J., Nguyen T., Rao J.S. (2010), Fence Method for Nonparametric Small Area Estimation. Survey Methodology, 36(1), 3-11. <http://publications.gc.ca/collections/collection_2010/statcan/12-001-X/12-001-x2010001-eng.pdf>. 4. Jiming Jiang, Thuan Nguyen and J. Sunil Rao (2011), Invisible fence methods and the identification of differentially expressed gene sets. Statistics and Its Interface, Volume 4, 403-415. <http://www.intlpress.com/site/pub/files/_fulltext/journals/sii/2011/0004/0003/SII-2011-0004-0003-a014.pdf>. 5. Thuan Nguyen & Jiming Jiang (2012), Restricted fence method for covariate selection in longitudinal data analysis. Biostatistics, 13(2), 303-314. <DOI:10.1093/biostatistics/kxr046> <https://academic.oup.com/biostatistics/article/13/2/303/263903/Restricted-fence-method-for-covariate-selection-in>. 6. Thuan Nguyen, Jie Peng, Jiming Jiang (2014), Fence Methods for Backcross Experiments. Statistical Computation and Simulation, 84(3), 644-662. <DOI:10.1080/00949655.2012.721885> <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3891925/>. 7. Jiang, J. (2014), The fence methods, in Advances in Statistics, Hindawi Publishing Corp., Cairo. <DOI:10.1155/2014/830821>. 8. Jiming Jiang and Thuan Nguyen (2015), The Fence Methods, World Scientific, Singapore. <https://www.abebooks.com/9789814596060/Fence-Methods-Jiming-Jiang-981459606X/plp>.

Compute inbreeding coefficients using the method of Meuwissen and Luo (1992) <doi:10.1186/1297-9686-24-4-305>, and numerator relationship coefficients between individuals using the method of Van Vleck (2007) <https://pubmed.ncbi.nlm.nih.gov/18050089/>.

Convenient classes to model fitness landscapes and fitness seascapes. A low-level package with which most users will not interact but upon which other packages modeling fitness landscapes and fitness seascapes will depend.

This package performs family-based association tests with a polytomous outcome under 2-locus and 1-locus models defined by some design matrix.

This package provides a C++ API for routinely used numerical tools such as integration, root-finding, and optimization, where function arguments are given as lambdas. This facilitates Rcpp programming, enabling the development of R'-like code in C++ where functions can be defined on the fly and use variables in the surrounding environment.

The main functions in this package are with_cache() and cached_read(). The former is a simple way to cache an R object into a file on disk, using cachem'. The latter is a wrapper around any standard read function, but caches both the output and the file list info. If the input file list info hasn't changed, the cache is used; otherwise, the original files are re-read. This can save time if the original operation requires reading from many files, and/or involves lots of processing.

Calculation of AHP (Analytic Hierarchy Process - <http://en.wikipedia.org/wiki/Analytic_hierarchy_process>) with classic and fuzzy weights based on Saaty's pairwise comparison method for determination of weights.

Feature flags allow developers to turn features of their software on and off in form of configuration. This package provides functions for creating feature flags in code. It exposes an interface for defining own feature flags which are enabled based on custom criteria.

This package provides a series of utility functions to help with reshaping hierarchy of data tree, and reform the structure of data tree.