Utility functions that may be of general interest but are specifically required by the NeuroAnatomy Toolbox ('nat'). Includes functions to provide a basic make style system to update files based on timestamp information, file locking and touch utility. Convenience functions for working with file paths include abs2rel', split_path and common_path'. Finally there are utility functions for working with zip and gzip files including integrity tests.

Allow users to obtain basketball statistics for the Australian basketball league NBL'<https://nbl.com.au/>. Stats include play-by-play, shooting locations, results and box scores for teams and players.

This package implements the nonparametric causality-in-quantiles test (in mean or variance), returning a test object with an S3 plot() method. The current implementation uses one lag of each series (first-order Granger causality setup). Methodology is based on Balcilar, Gupta, and Pierdzioch (2016a) <doi:10.1016/j.resourpol.2016.04.004> and Balcilar et al. (2016) <doi:10.1007/s11079-016-9388-x>.

This package implements a nonparametric statistical test for rank or score data from partially-balanced incomplete block-design experiments.

Commodity pricing models are (systems of) stochastic differential equations that are utilized for the valuation and hedging of commodity contingent claims (i.e. derivative products on the commodity) and other commodity related investments. Commodity pricing models that capture market dynamics are of great importance to commodity market participants in order to exercise sound investment and risk-management strategies. Parameters of commodity pricing models are estimated through maximum likelihood estimation, using available term structure futures data of a commodity. NFCP (n-factor commodity pricing) provides a framework for the modeling, parameter estimation, probabilistic forecasting, option valuation and simulation of commodity prices through state space and Monte Carlo methods, risk-neutral valuation and Kalman filtering. NFCP allows the commodity pricing model to consist of n correlated factors, with both random walk and mean-reverting elements. The n-factor commodity pricing model framework was first presented in the work of Cortazar and Naranjo (2006) <doi:10.1002/fut.20198>. Examples presented in NFCP replicate the two-factor crude oil commodity pricing model presented in the prolific work of Schwartz and Smith (2000) <doi:10.1287/mnsc.46.7.893.12034> with the approximate term structure futures data applied within this study provided in the NFCP package.

Nested loop cross validation for classification purposes for misclassification error rate estimation. The package supports several methodologies for feature selection: random forest, Student t-test, limma, and provides an interface to the following classification methods in the MLInterfaces package: linear, quadratic discriminant analyses, random forest, bagging, prediction analysis for microarray, generalized linear model, support vector machine (svm and ksvm). Visualizations to assess the quality of the classifier are included: plot of the ranks of the features, scores plot for a specific classification algorithm and number of features, misclassification rate for the different number of features and classification algorithms tested and ROC plot. For further details about the methodology, please check: Markus Ruschhaupt, Wolfgang Huber, Annemarie Poustka, and Ulrich Mansmann (2004) <doi:10.2202/1544-6115.1078>.

This package provides a comprehensive set of functions providing frequentist methods for network meta-analysis (Balduzzi et al., 2023) <doi:10.18637/jss.v106.i02> and supporting Schwarzer et al. (2015) <doi:10.1007/978-3-319-21416-0>, Chapter 8 "Network Meta-Analysis": - frequentist network meta-analysis following Rücker (2012) <doi:10.1002/jrsm.1058>; - additive network meta-analysis for combinations of treatments (Rücker et al., 2020) <doi:10.1002/bimj.201800167>; - network meta-analysis of binary data using the Mantel-Haenszel or non-central hypergeometric distribution method (Efthimiou et al., 2019) <doi:10.1002/sim.8158>, or penalised logistic regression (Evrenoglou et al., 2022) <doi:10.1002/sim.9562>; - rankograms and ranking of treatments by the Surface under the cumulative ranking curve (SUCRA) (Salanti et al., 2013) <doi:10.1016/j.jclinepi.2010.03.016>; - ranking of treatments using P-scores (frequentist analogue of SUCRAs without resampling) according to Rücker & Schwarzer (2015) <doi:10.1186/s12874-015-0060-8>; - split direct and indirect evidence to check consistency (Dias et al., 2010) <doi:10.1002/sim.3767>, (Efthimiou et al., 2019) <doi:10.1002/sim.8158>; - league table with network meta-analysis results; - comparison-adjusted funnel plot (Chaimani & Salanti, 2012) <doi:10.1002/jrsm.57>; - net heat plot and design-based decomposition of Cochran's Q according to Krahn et al. (2013) <doi:10.1186/1471-2288-13-35>; - measures characterizing the flow of evidence between two treatments by König et al. (2013) <doi:10.1002/sim.6001>; - automated drawing of network graphs described in Rücker & Schwarzer (2016) <doi:10.1002/jrsm.1143>; - partial order of treatment rankings ('poset') and Hasse diagram for poset (Carlsen & Bruggemann, 2014) <doi:10.1002/cem.2569>; (Rücker & Schwarzer, 2017) <doi:10.1002/jrsm.1270>; - contribution matrix as described in Papakonstantinou et al. (2018) <doi:10.12688/f1000research.14770.3> and Davies et al. (2022) <doi:10.1002/sim.9346>; - network meta-regression with a single continuous or binary covariate; - subgroup network meta-analysis.

NEON data packages can be accessed through the NEON Data Portal <https://www.neonscience.org> or through the NEON Data API (see <https://data.neonscience.org/data-api> for documentation). Data delivered from the Data Portal are provided as monthly zip files packaged within a parent zip file, while individual files can be accessed from the API. This package provides tools that aid in discovering, downloading, and reformatting data prior to use in analyses. This includes downloading data via the API, merging data tables by type, and converting formats. For more information, see the readme file at <https://github.com/NEONScience/NEON-utilities>.

Common ecological distributions for nimble models in the form of nimbleFunction objects. Includes Cormack-Jolly-Seber, occupancy, dynamic occupancy, hidden Markov, dynamic hidden Markov, and N-mixture models. (Jolly (1965) <DOI: 10.2307/2333826>, Seber (1965) <DOI: 10.2307/2333827>, Turek et al. (2016) <doi:10.1007/s10651-016-0353-z>).

This package provides tools for 4D nucleome imaging. Quantitative analysis of the 3D nuclear landscape recorded with super-resolved fluorescence microscopy. See Volker J. Schmid, Marion Cremer, Thomas Cremer (2017) <doi:10.1016/j.ymeth.2017.03.013>.

Box-constrained multiobjective optimization using the elitist non-dominated sorting genetic algorithm - NSGA-II. Fast non-dominated sorting, crowding distance, tournament selection, simulated binary crossover, and polynomial mutation are called in the main program. The methods are described in Deb et al. (2002) <doi:10.1109/4235.996017>.

This is a pure dummy interfaces package which mirrors MsSparkUtils APIs <https://learn.microsoft.com/en-us/azure/synapse-analytics/spark/microsoft-spark-utilities?pivots=programming-language-r> of Azure Synapse Analytics <https://learn.microsoft.com/en-us/azure/synapse-analytics/> for R users, customer of Azure Synapse can download this package from CRAN for local development.

Wraps the nametag library <https://github.com/ufal/nametag>, allowing users to find and extract entities (names, persons, locations, addresses, ...) in raw text and build your own entity recognition models. Based on a maximum entropy Markov model which is described in Strakova J., Straka M. and Hajic J. (2013) <https://ufal.mff.cuni.cz/~straka/papers/2013-tsd_ner.pdf>.

This package provides functions for working with NHS number checksums. The UK's National Health Service issues NHS numbers to all users of its services and this package implements functions for verifying that the numbers are valid according to the checksum scheme the NHS use. Numbers can be validated and checksums created.

Computes and plots the boundary between night and day.

Fast and Accurate Trisomy Prediction in Non-Invasive Prenatal Testing.

This package provides tools for reading and writing NIfTI-1.1 (NII) files, including optimized voxelwise read/write operations and a simplified method to write dataframes to NII. Specification of the NIfTI-1.1 format can be found here <https://nifti.nimh.nih.gov/nifti-1>. Scientific publication first using these tools Koscik TR, Man V, Jahn A, Lee CH, Cunningham WA (2020) <doi:10.1016/j.neuroimage.2020.116764> "Decomposing the neural pathways in a simple, value-based choice." Neuroimage, 214, 116764.

Researchers often want to evaluate whether there is a negligible relationship among variables. The negligible package provides functions that are useful for conducting negligible effect testing (also called equivalence testing). For example, there are functions for evaluating the equivalence of means or the presence of a negligible association (correlation or regression). Beribisky, N., Mara, C., & Cribbie, R. A. (2020) <doi:10.20982/tqmp.16.4.p424>. Beribisky, N., Davidson, H., Cribbie, R. A. (2019) <doi:10.7717/peerj.6853>. Shiskina, T., Farmus, L., & Cribbie, R. A. (2018) <doi:10.20982/tqmp.14.3.p167>. Mara, C. & Cribbie, R. A. (2017) <doi:10.1080/00220973.2017.1301356>. Counsell, A. & Cribbie, R. A. (2015) <doi:10.1111/bmsp.12045>. van Wieringen, K. & Cribbie, R. A. (2014) <doi:10.1111/bmsp.12015>. Goertzen, J. R. & Cribbie, R. A. (2010) <doi:10.1348/000711009x475853>. Cribbie, R. A., Gruman, J. & Arpin-Cribbie, C. (2004) <doi:10.1002/jclp.10217>.

Calculation of molecular number and brightness from fluorescence microscopy image series. The software was published in a 2016 paper <doi:10.1093/bioinformatics/btx434>. The seminal paper for the technique is Digman et al. 2008 <doi:10.1529/biophysj.107.114645>. A review of the technique was published in 2017 <doi:10.1016/j.ymeth.2017.12.001>.

Create interactive analytic networks. It joins the data analysis power of R to obtain coincidences, co-occurrences and correlations, and the visualization libraries of JavaScript in one package.

This package provides tools for modelling, ML estimation, validation analysis and simulation of non homogeneous Poisson processes in time.

Statistical tools for analyzing cognitive diagnosis (CD) data collected from small settings using the nonparametric classification (NPCD) framework. The core methods of the NPCD framework includes the nonparametric classification (NPC) method developed by Chiu and Douglas (2013) <DOI:10.1007/s00357-013-9132-9> and the general NPC (GNPC) method developed by Chiu, Sun, and Bian (2018) <DOI:10.1007/s11336-017-9595-4> and Chiu and Köhn (2019) <DOI:10.1007/s11336-019-09660-x>. An extension of the NPCD framework included in the package is the nonparametric method for multiple-choice items (MC-NPC) developed by Wang, Chiu, and Koehn (2023) <DOI:10.3102/10769986221133088>. Functions associated with various extensions concerning the evaluation, validation, and feasibility of the CD analysis are also provided. These topics include the completeness of Q-matrix, Q-matrix refinement method, as well as Q-matrix estimation.

This package performs analysis of one-way multivariate data, for small samples using Nonparametric techniques. Using approximations for ANOVA Type, Wilks Lambda, Lawley Hotelling, and Bartlett Nanda Pillai Test statics, the package compares the multivariate distributions for a single explanatory variable. The comparison is also performed using a permutation test for each of the four test statistics. The package also performs an all-subsets algorithm regarding variables and regarding factor levels.

Estimates and plots (as a single plot and as a heat map) the rolling window correlation coefficients between two time series and computes their statistical significance, which is carried out through a non-parametric computing-intensive method. This method addresses the effects due to the multiple testing (inflation of the Type I error) when the statistical significance is estimated for the rolling window correlation coefficients. The method is based on Monte Carlo simulations by permuting one of the variables (e.g., the dependent) under analysis and keeping fixed the other variable (e.g., the independent). We improve the computational efficiency of this method to reduce the computation time through parallel computing. The NonParRolCor package also provides examples with synthetic and real-life environmental time series to exemplify its use. Methods derived from R. Telford (2013) <https://quantpalaeo.wordpress.com/2013/01/04/> and J.M. Polanco-Martinez and J.L. Lopez-Martinez (2021) <doi:10.1016/j.ecoinf.2021.101379>.