Multidimensional scaling (MDS) functions for various tasks that are beyond the beta stage and way past the alpha stage. Currently, options are available for weights, restrictions, classical scaling or principal coordinate analysis, transformations (linear, power, Box-Cox, spline, ordinal), outlier mitigation (rdop), out-of-sample estimation (predict), negative dissimilarities, fast and faster executions with low memory footprints, penalized restrictions, cross-validation-based penalty selection, supplementary variable estimation (explain), additive constant estimation, mixed measurement level distance calculation, restricted classical scaling, etc. More will come in the future. References. Busing (2024) "A Simple Population Size Estimator for Local Minima Applied to Multidimensional Scaling". Manuscript submitted for publication. Busing (2025) "Node Localization by Multidimensional Scaling with Iterative Majorization". Manuscript submitted for publication. Busing (2025) "Faster Multidimensional Scaling". Manuscript in preparation. Barroso and Busing (2025) "e-RDOP, Relative Density-Based Outlier Probabilities, Extended to Proximity Mapping". Manuscript submitted for publication.

Flipbooks present code step-by-step and side-by-side with its output. flipbookr helps creators build flipbooks efficiently because code pipelines are automatically parsed and prepped for presentation as flipbooks.

This package provides methods for matrix factorization based on Wang and Stephens (2021) <https://jmlr.org/papers/v22/20-589.html>.

Efficient approximation of first passage time densities for diffusion processes based on the First Passage Time Location (FPTL) function.

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/>.

This Rcpp'-based package implements highly efficient functions for the calculation of the Jonckheere-Terpstra statistic. It can be used for a variety of applications, including feature selection in machine learning problems, or to conduct genome-wide association studies (GWAS) with multiple quantitative phenotypes. The code leverages OpenMP directives for multi-core computing to reduce overall processing time.

This package implements fractional differencing with Autoregressive Moving Average models to analyse long-memory time series data. Traditional ARIMA models typically use integer values for differencing, which are suitable for time series with short memory or anti-persistent behaviour. In contrast, the Fractional ARIMA model allows fractional differencing, enabling it to effectively capture long memory characteristics in time series data. The âfracARMAâ package is user-friendly and allows users to manually input the fractional differencing parameter, which can be obtained using various estimators such as the GPH estimator, Sperio method, or Wavelet method and many. Additionally, the package enables users to directly feed the time series data, AR order, MA order, fractional differencing parameter, and the proportion of training data as a split ratio, all in a single command. The package is based on the reference from the paper of Irshad and others (2024, <doi:10.22271/maths.2024.v9.i6b.1906>).

The FAS package implements the bootstrap method for the tuning parameter selection and tuning-free inference on sparse regression coefficient vectors. Currently, the test could be applied to linear and factor-augmented sparse regressions, see Lederer & Vogt (2021, JMLR) <https://www.jmlr.org/papers/volume22/20-539/20-539.pdf> and Beyhum & Striaukas (2023) <arXiv:2307.13364>.

Developed for the following tasks. 1 ) Computing the probability density function, cumulative distribution function, random generation, and estimating the parameters of the eleven mixture models. 2 ) Point estimation of the parameters of two - parameter Weibull distribution using twelve methods and three - parameter Weibull distribution using nine methods. 3 ) The Bayesian inference for the three - parameter Weibull distribution. 4 ) Estimating parameters of the three - parameter Birnbaum - Saunders, generalized exponential, and Weibull distributions fitted to grouped data using three methods including approximated maximum likelihood, expectation maximization, and maximum likelihood. 5 ) Estimating the parameters of the gamma, log-normal, and Weibull mixture models fitted to the grouped data through the EM algorithm, 6 ) Estimating parameters of the nonlinear height curve fitted to the height - diameter observation, 7 ) Estimating parameters, computing probability density function, cumulative distribution function, and generating realizations from gamma shape mixture model introduced by Venturini et al. (2008) <doi:10.1214/07-AOAS156> , 8 ) The Bayesian inference, computing probability density function, cumulative distribution function, and generating realizations from univariate and bivariate Johnson SB distribution, 9 ) Robust multiple linear regression analysis when error term follows skewed t distribution, 10 ) Estimating parameters of a given distribution fitted to grouped data using method of maximum likelihood, and 11 ) Estimating parameters of the Johnson SB distribution through the Bayesian, method of moment, conditional maximum likelihood, and two - percentile method.

Download geospatial data available from several federated data sources (mainly sources maintained by the US Federal government). Currently, the package enables extraction from nine datasets: The National Elevation Dataset digital elevation models (<https://www.usgs.gov/3d-elevation-program> 1 and 1/3 arc-second; USGS); The National Hydrography Dataset (<https://www.usgs.gov/national-hydrography/national-hydrography-dataset>; USGS); The Soil Survey Geographic (SSURGO) database from the National Cooperative Soil Survey (<https://websoilsurvey.sc.egov.usda.gov/>; NCSS), which is led by the Natural Resources Conservation Service (NRCS) under the USDA; the Global Historical Climatology Network (<https://www.ncei.noaa.gov/products/land-based-station/global-historical-climatology-network-daily>; GHCN), coordinated by National Climatic Data Center at NOAA; the Daymet gridded estimates of daily weather parameters for North America, version 4, available from the Oak Ridge National Laboratory's Distributed Active Archive Center (<https://daymet.ornl.gov/>; DAAC); the International Tree Ring Data Bank; the National Land Cover Database (<https://www.mrlc.gov/>; NLCD); the Cropland Data Layer from the National Agricultural Statistics Service (<https://www.nass.usda.gov/Research_and_Science/Cropland/SARS1a.php>; NASS); and the PAD-US dataset of protected area boundaries (<https://www.usgs.gov/programs/gap-analysis-project/science/pad-us-data-overview>; USGS).

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/>.

Computes six functional diversity indices. These are namely, Functional Divergence (FDiv), Function Evenness (FEve), Functional Richness (FRic), Functional Richness intersections (FRic_intersect), Functional Dispersion (FDis), and Rao's entropy (Q) (reviewed in Villéger et al. 2008 <doi:10.1890/07-1206.1>). Provides efficient, modular, and parallel functions to compute functional diversity indices (preprint: <doi:10.32942/osf.io/dg7hw>).

This package provides functions for plotting probability density functions, distribution functions, survival functions, hazard functions and computing distribution moments. The implementation is inspired by Delignette-Muller and Dutang (2015) <doi:10.18637/jss.v064.i04>.

Catalogues of resolution IV regular fractional factorial designs in 128 runs are provided for up to 33 2-level factors. The catalogues are complete, excluding resolution IV designs without 5-letter words, because these do not add value for a search for unblocked clear designs. The previous package version 1.0 with complete catalogues up to 24 runs (24 runs and a namespace added later) can be downloaded from the authors website.

This package provides a bundle of analytics tools for fisheries scientists. A shiny R App is included for a no-code solution for retrieval, analysis, and visualization.

This package implements a path algorithm for the Fused Lasso Signal Approximator. For more details see the help files or the article by Hoefling (2009) <arXiv:0910.0526>.

This package provides a faster implementation of Bayesian Causal Forests (BCF; Hahn et al. (2020) <doi:10.1214/19-BA1195>), which uses regression tree ensembles to estimate the conditional average treatment effect of a binary treatment on a scalar output as a function of many covariates. This implementation avoids many redundant computations and memory allocations present in the original BCF implementation, allowing the model to be fit to larger datasets. The implementation was originally developed for the 2022 American Causal Inference Conference's Data Challenge. See Kokandakar et al. (2023) <doi:10.1353/obs.2023.0024> for more details.

This is a fast and flexible implementation of the Kalman filter and smoother, which can deal with NAs. It is entirely written in C and relies fully on linear algebra subroutines contained in BLAS and LAPACK. Due to the speed of the filter, the fitting of high-dimensional linear state space models to large datasets becomes possible. This package also contains a plot function for the visualization of the state vector and graphical diagnostics of the residuals.

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.

Offers a set of tools for visualizing and analyzing size and power properties of the test for equal predictive accuracy, the Diebold-Mariano test that is based on heteroskedasticity and autocorrelation-robust (HAR) inference. A typical HAR inference is involved with non-parametric estimation of the long-run variance, and one of its tuning parameters, the truncation parameter, trades off a size and power. Lazarus, Lewis, and Stock (2021)<doi:10.3982/ECTA15404> theoretically characterize the size-power frontier for the Gaussian multivariate location model. ForeComp computes and visualizes the finite-sample size-power frontier of the Diebold-Mariano test based on fixed-b asymptotics together with the Bartlett kernel. To compute the finite-sample size and power, it works with the best approximating ARMA process to the given dataset. It informs the user how their choice of the truncation parameter performs and how robust the testing outcomes are.

Construction and smart selection of Gaussian process models for analysis of computer experiments with emphasis on treatment of functional inputs that are regularly sampled. This package offers: (i) flexible modeling of functional-input regression problems through the fairly general Gaussian process model; (ii) built-in dimension reduction for functional inputs; (iii) heuristic optimization of the structural parameters of the model (e.g., active inputs, kernel function, type of distance). An in-depth tutorial in the use of funGp is provided in Betancourt et al. (2024) <doi:10.18637/jss.v109.i05> and Metamodeling background is provided in Betancourt et al. (2020) <doi:10.1016/j.ress.2020.106870>. The algorithm for structural parameter optimization is described in <https://hal.science/hal-02532713>.

Spatio-temporal locations of an animal are computed from annotated data with a hidden Markov model via particle filter algorithm. The package is relatively robust to varying degrees of shading. The hidden Markov model is described in Movement Ecology - Rakhimberdiev et al. (2015) <doi:10.1186/s40462-015-0062-5>, general package description is in the Methods in Ecology and Evolution - Rakhimberdiev et al. (2017) <doi:10.1111/2041-210X.12765> and package accuracy assessed in the Journal of Avian Biology - Rakhimberdiev et al. (2016) <doi:10.1111/jav.00891>.

This package provides a programmatic interface to the Finnish Biodiversity Information Facility ('FinBIF') API (<https://api.laji.fi>). FinBIF aggregates Finnish biodiversity data from multiple sources in a single open access portal for researchers, citizen scientists, industry and government. FinBIF allows users of biodiversity information to find, access, combine and visualise data on Finnish plants, animals and microorganisms. The finbif package makes the publicly available data in FinBIF easily accessible to programmers. Biodiversity information is available on taxonomy and taxon occurrence. Occurrence data can be filtered by taxon, time, location and other variables. The data accessed are conveniently preformatted for subsequent analyses.

Parse and create Darwin Core (<http://rs.tdwg.org/dwc/>) Simple and Archives. Functionality includes reading and parsing all the files in a Darwin Core Archive, including the datasets and metadata; read and parse simple Darwin Core files; and validation of Darwin Core Archives.