This package provides standardized access to a range of re-scaling methods for numerical vectors and time-series features calculated within the theft ecosystem.

This package provides tools for handling NetCDF metadata in data frames. The metadata is provided as relations in tabular form, to avoid having to scan printed header output or to navigate nested lists of raw metadata.

Fits sphere-sphere regression models by estimating locally weighted rotations. Simulation of sphere-sphere data according to non-rigid rotation models. Provides methods for bias reduction applying iterative procedures within a Newton-Raphson learning scheme. Cross-validation is exploited to select smoothing parameters. See Marco Di Marzio, Agnese Panzera & Charles C. Taylor (2018) <doi:10.1080/01621459.2017.1421542>.

The NetCoupler algorithm identifies potential direct effects of correlated, high-dimensional variables formed as a network with an external variable. The external variable may act as the dependent/response variable or as an independent/predictor variable to the network.

The n-vector framework uses the normal vector to the Earth ellipsoid (called n-vector) as a non-singular position representation that turns out to be very convenient for practical position calculations. The n-vector is simple to use and gives exact answers for all global positions, and all distances, for both ellipsoidal and spherical Earth models. This package is a translation of the Matlab library from FFI, the Norwegian Defence Research Establishment, as described in Gade (2010) <doi:10.1017/S0373463309990415>.

This R package provides a calculation of between-cases AUC estimate, corresponding covariance, and variance estimate in the nested data problem. Also, the package has the function to simulate the nested data. The calculated between-cases AUC estimate is used to evaluate the reader's diagnostic performance in clinical tasks with nested data. For more details on the above methods, please refer to the paper by H Du, S Wen, Y Guo, F Jin, BD Gallas (2022) <doi:10.1177/09622802221111539>.

An adaptation of Non-dominated Sorting Genetic Algorithm III for multi objective feature selection tasks. Non-dominated Sorting Genetic Algorithm III is a genetic algorithm that solves multiple optimization problems simultaneously by applying a non-dominated sorting technique. It uses a reference points based selection operator to explore solution space and preserve diversity. See the original paper by K. Deb and H. Jain (2014) <DOI:10.1109/TEVC.2013.2281534> for a detailed description.

Density, distribution function, quantile function and random generation for the 3D Navarro, Frenk & White (NFW) profile. For details see Robotham & Howlett (2018) <arXiv:1805.09550>.

Routines for fitting and simulating data under autoregressive fractionally integrated moving average (ARFIMA) models, without the constraint of covariance stationarity. Two fitting methods are implemented, a pseudo-maximum likelihood method and a minimum distance estimator. Mayoral, L. (2007) <doi:10.1111/j.1368-423X.2007.00202.x>. Beran, J. (1995) <doi:10.1111/j.2517-6161.1995.tb02054.x>.

Implement and enhance the performance of spatial fuzzy clustering using Fuzzy Geographically Weighted Clustering with various optimization algorithms, mainly from Xin She Yang (2014) <ISBN:9780124167438> with book entitled Nature-Inspired Optimization Algorithms. The optimization algorithm is useful to tackle the disadvantages of clustering inconsistency when using the traditional approach. The distance measurements option is also provided in order to increase the quality of clustering results. The Fuzzy Geographically Weighted Clustering with nature inspired optimisation algorithm was firstly developed by Arie Wahyu Wijayanto and Ayu Purwarianti (2014) <doi:10.1109/CITSM.2014.7042178> using Artificial Bee Colony algorithm.

Estimating the first and second derivatives of a regression function by the method of Wang and Lin (2015) <http://www.jmlr.org/papers/v16/wang15b.html>.

Noninferiority tests for difference in failure rates at a prespecified control rate or prespecified time. For details, see Fay and Follmann, 2016 <DOI:10.1177/1740774516654861>.

This package provides functions and datasets to support the book by Galecki and Burzykowski (2013), Linear Mixed-Effects Models: A Step-by-Step Approach', Springer. Includes functions for power calculations, log-likelihood contributions, and data simulation for linear mixed-effects models.

Inference and dependence measure for the non-central squared Gaussian, Student, Clayton, Gumbel, and Frank copula models.The description of the methodology is taken from Section 3 of Nasri, Remillard and Bouezmarni (2019) <doi:10.1016/j.jmva.2019.03.007>.

Facilitates nonresponse bias analysis (NRBA) for survey data. Such data may arise from a complex sampling design with features such as stratification, clustering, or unequal probabilities of selection. Multiple types of analyses may be conducted: comparisons of response rates across subgroups; comparisons of estimates before and after weighting adjustments; comparisons of sample-based estimates to external population totals; tests of systematic differences in covariate means between respondents and full samples; tests of independence between response status and covariates; and modeling of outcomes and response status as a function of covariates. Extensive documentation and references are provided for each type of analysis. Krenzke, Van de Kerckhove, and Mohadjer (2005) <http://www.asasrms.org/Proceedings/y2005/files/JSM2005-000572.pdf> and Lohr and Riddles (2016) <https://www150.statcan.gc.ca/n1/en/pub/12-001-x/2016002/article/14677-eng.pdf?st=q7PyNsGR> provide an overview of the methods implemented in this package.

Non-negative Matrix Factorization.

Two implementations of canonical correlation analysis (CCA) that are based on iterated regression. By choosing the appropriate regression algorithm for each data domain, it is possible to enforce sparsity, non-negativity or other kinds of constraints on the projection vectors. Multiple canonical variables are computed sequentially using a generalized deflation scheme, where the additional correlation not explained by previous variables is maximized. nscancor() is used to analyze paired data from two domains, and has the same interface as cancor() from the stats package (plus some extra parameters). mcancor() is appropriate for analyzing data from three or more domains. See <https://sigg-iten.ch/learningbits/2014/01/20/canonical-correlation-analysis-under-constraints/> and Sigg et al. (2007) <doi:10.1109/MLSP.2007.4414315> for more details.

This package implements methods for centrality related analyses of networks. While the package includes the possibility to build more than 20 indices, its main focus lies on index-free assessment of centrality via partial rankings obtained by neighborhood-inclusion or positional dominance. These partial rankings can be analyzed with different methods, including probabilistic methods like computing expected node ranks and relative rank probabilities (how likely is it that a node is more central than another?). The methodology is described in depth in the vignettes and in Schoch (2018) <doi:10.1016/j.socnet.2017.12.003>.

Sparse VAR estimation based on LASSO.

Visualization and analysis tools to aid in the interpretation of neural network models. Functions are available for plotting, quantifying variable importance, conducting a sensitivity analysis, and obtaining a simple list of model weights.

This package provides a set of functions providing several outlier (i.e., studies with extreme findings) and influential detection measures and methodologies in network meta-analysis : - simple outlier and influential detection measures - outlier and influential detection measures by considering study deletion (shift the mean) - plots for outlier and influential detection measures - Q-Q plot for network meta-analysis - Forward Search algorithm in network meta-analysis. - forward plots to monitor statistics in each step of the forward search algorithm - forward plots for summary estimates and their confidence intervals in each step of forward search algorithm.

This package contains a sample of the 2005 Grade 8 Mathematics data from the National Assessment of Educational Progress (NAEP). This data set is called the NAEP Primer.

This package provides transfusion-related differential tests on Near-infrared spectroscopy (NIRS) time series with detection limit, which contains two testing statistics: Mean Area Under the Curve (MAUC) and slope statistic. This package applied a penalized spline method within imputation setting. Testing is conducted by a nested permutation approach within imputation. Refer to Guo et al (2018) <doi:10.1177/0962280218786302> for further details.

This package provides functions for interacting directly with the Nasdaq Data Link API to offer data in a number of formats usable in R, downloading a zip with all data from a Nasdaq Data Link database, and the ability to search. This R package uses the Nasdaq Data Link API. For more information go to <https://docs.data.nasdaq.com/>. For more help on the package itself go to <https://data.nasdaq.com/tools/r>.