This package performs Bayesian wavelet analysis using individual non-local priors as described in Sanyal & Ferreira (2017) <DOI:10.1007/s13571-016-0129-3> and non-local prior mixtures as described in Sanyal (2025) <DOI:10.48550/arXiv.2501.18134>.

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.

Draw nested extreme value random variables, which are the variables that appear in the latent variable formulation of the nested logit model.

Fit univariate non-linear scale mixture of skew-normal(NL-SMSN) regression, details in Garay, Lachos and Abanto-Valle (2011) <doi:10.1016/j.jkss.2010.08.003> and Lachos, Bandyopadhyay and Garay (2011) <doi:10.1016/j.spl.2011.03.019>.

This is the R API for the nfer formalism (<http://nfer.io/>). nfer was developed to specify event stream abstractions for spacecraft telemetry such as the Mars Science Laboratory. Users write rules using a syntax that borrows heavily from Allen's Temporal Logic that, when applied to an event stream, construct a hierarchy of temporal intervals with data. The R API supports loading rules from a file or mining them from historical data. Traces of events or pools of intervals are provided as data frames.

Extends package nat (NeuroAnatomy Toolbox) by providing a collection of NBLAST-related functions for neuronal morphology comparison (Costa et al. (2016) <doi: 10.1016/j.neuron.2016.06.012>).

Automatically runs 18 individual models and 14 ensembles on numeric data, for a total of 32 models. The package automatically returns complete results on all 32 models, 25 charts and six tables. The user simply provides the tidy data, and answers a few questions (for example, how many times would you like to resample the data). From there the package randomly splits the data into train, test and validation sets as the user requests (for example, train = 0.60, test = 0.20, validation = 0.20), fits each of models on the training data, makes predictions on the test and validation sets, measures root mean squared error (RMSE), removes features above a user-set level of Variance Inflation Factor, and has several optional features including scaling all numeric data, four different ways to handle strings in the data. Perhaps the most significant feature is the package's ability to make predictions using the 32 pre trained models on totally new (untrained) data if the user selects that feature. This feature alone represents a very effective solution to the issue of reproducibility of models in data science. The package can also randomly resample the data as many times as the user sets, thus giving more accurate results than a single run. The graphs provide many results that are not typically found. For example, the package automatically calculates the Kolmogorov-Smirnov test for each of the 32 models and plots a bar chart of the results, a bias bar chart of each of the 32 models, as well as several plots for exploratory data analysis (automatic histograms of the numeric data, automatic histograms of the numeric data). The package also automatically creates a summary report that can be both sorted and searched for each of the 32 models, including RMSE, bias, train RMSE, test RMSE, validation RMSE, overfitting and duration. The best results on the holdout data typically beat the best results in data science competitions and published results for the same data set.

Geospatial data for creating maps of New South Wales (NSW), Australia, and some helpers to work with common problems like normalising postcodes. Registers its data with cartographer'.

This package provides helpers for discovering, planning, and downloading open-access datasets from the Neuroscience Multi-Omic Archive ('NeMO'; <https://nemoarchive.org/>). The package builds reproducible file manifests that record search parameters, file metadata, download URLs, checksums, and local file paths. It supports exploratory NeMO metadata queries and provides first-pass bridges from downloaded files into SingleCellExperiment and Seurat workflows.

Neighbour-balanced designs ensure that no treatment is disadvantaged unfairly by its surroundings. The treatment allocation in these designs is such that every treatment appears equally often as a neighbour with every other treatment. Neighbour Balanced Designs are employed when there is a possibility of neighbour effects from treatments used in adjacent experimental units. In the literature, a vast number of such designs have been developed. This package generates some efficient neighbour balanced block designs which are balanced and partially variance balanced for estimating the contrast pertaining to direct and neighbour effects, as well as provides a function for analysing the data obtained from such trials (Azais, J.M., Bailey, R.A. and Monod, H. (1993). "A catalogue of efficient neighbour designs with border plots". Biometrics, 49, 1252-1261 ; Tomar, J. S., Jaggi, Seema and Varghese, Cini (2005)<DOI: 10.1080/0266476042000305177>. "On totally balanced block designs for competition effects"). This package contains functions named nbbd1(),nbbd2(),nbbd3(),pnbbd1() and pnbbd2() which generates neighbour balanced block designs within a specified range of number of treatment (v). It contains another function named anlys()for performing the analysis of data generated from such trials.

Th-U-Pb electron microprobe age dating of monazite, as originally described in <doi:10.1016/0009-2541(96)00024-1>.

This package provides tools for the stages of a single-case (N-of-1) experimental study that come before analysis: generating randomization schedules that can be preregistered and reproduced exactly, under run-length constraints, exporting them for mobile data collection, validating incoming ecological momentary assessment (EMA) records, and monitoring compliance. Designed around the workflow of a 70-day randomized N-of-1 study collected with stock iOS tools at 92.9% compliance; the package ships with that study's complete dataset.

Statistical inference with non-probability samples when auxiliary information from external sources such as probability samples or population totals or means is available. The package implements various methods such as inverse probability (propensity score) weighting, mass imputation and doubly robust approach. Details can be found in: Chen et al. (2020) <doi:10.1080/01621459.2019.1677241>, Yang et al. (2020) <doi:10.1111/rssb.12354>, Kim et al. (2021) <doi:10.1111/rssa.12696>, Yang et al. (2021) <https://www150.statcan.gc.ca/n1/pub/12-001-x/2021001/article/00004-eng.htm> and Wu (2022) <https://www150.statcan.gc.ca/n1/pub/12-001-x/2022002/article/00002-eng.htm>. For details on the package and its functionalities see <doi:10.48550/arXiv.2504.04255>.

Implement methods to determine the dynamic fit index cutoffs for network models. The package allows users to evaluate model fit based on their own model statement, model type, and sample size. Methods are described in Du and Epskamp (2026) <doi:10.31234/osf.io/5wj2y_v2>.

This package provides tools for working with nonlinear least squares problems. For the estimation of models reliable and robust tools than nls(), where the the Gauss-Newton method frequently stops with singular gradient messages. This is accomplished by using, where possible, analytic derivatives to compute the matrix of derivatives and a stabilization of the solution of the estimation equations. Tools for approximate or externally supplied derivative matrices are included. Bounds and masks on parameters are handled properly.

This package provides functions for revealing what happens when effect size estimates from previous studies are taken into account when evaluating each new dataset in a study sequence. The analyses can be conducted for cumulative meta-analyses and for Bayesian data analyses. The package contains sample data for a wide selection of research topics. Jointly considering previous findings along with new data is more likely to result in correct conclusions than does the traditional practice of not incorporating previous findings, which often results in a back and forth ping-pong of conclusions when evaluating a sequence of studies. O'Connor & Ermacora (2021, <doi:10.1037/cbs0000259>).

The raw dataset and model used in Lai et al. (2021) Decoupled responses of native and exotic tree diversities to distance from old-growth forest and soil phosphorous in novel secondary forests. Applied Vegetation Science, 24, e12548.

This package implements the non-asymptotically valid and asymptotically exact confidence intervals in two cases: estimation of the mean, and estimation of (a linear combination of) the coefficients in a linear regression model, following (Derumigny, Girard and Guyonvarch, 2025) <doi:10.48550/arXiv.2507.16776>.

Non-linear least squares regression with the Levenberg-Marquardt algorithm using multiple starting values for increasing the chance that the minimum found is the global minimum.

This package provides a system for writing hierarchical statistical models largely compatible with BUGS and JAGS', writing nimbleFunctions to operate models and do basic R-style math, and compiling both models and nimbleFunctions via custom-generated C++. NIMBLE includes default methods for MCMC, Laplace Approximation, deterministic nested approximations, Monte Carlo Expectation Maximization, and some other tools. The nimbleFunction system makes it easy to do things like implement new MCMC samplers from R, customize the assignment of samplers to different parts of a model from R, and compile the new samplers automatically via C++ alongside the samplers NIMBLE provides. NIMBLE extends the BUGS'/'JAGS language by making it extensible: New distributions and functions can be added, including as calls to external compiled code. Although most people think of MCMC as the main goal of the BUGS'/'JAGS language for writing models, one can use NIMBLE for writing arbitrary other kinds of model-generic algorithms as well. A full User Manual is available at <https://r-nimble.org>.

Collection of example animal sounds for bioacoustic analysis.

This package implements nonparametric multiple change-point detection for univariate sequences using Wild Binary Segmentation, as described in Ross (2026) "Nonparametric Detection of Multiple Location-Scale Change Points via Wild Binary Segmentation" <doi:10.48550/arXiv.2107.01742>. The package provides Mann--Whitney, Mood, Lepage, Cramér--von Mises, modified Baumgartner, and standardised Zhang Z_C rank-based statistics, together with method-specific thresholds for controlling the probability of incorrectly detecting a change point in a homogeneous sequence.

Indices, heuristics, simulations and strategies to help determine the number of factors/components to retain in exploratory factor analysis and principal component analysis.

Converts numeric vectors to character vectors of English number names. Provides conversion to cardinals, ordinals, numerators, and denominators. Supports negative and non-integer numbers.