This package provides a collection of tools that allow users to perform critical steps in the process of assessing ecological niche evolution over phylogenies, with uncertainty incorporated explicitly in reconstructions. The method proposed here for ancestral reconstruction of ecological niches characterizes species niches using a bin-based approach that incorporates uncertainty in estimations. Compared to other existing methods, the approaches presented here reduce risk of overestimation of amounts and rates of ecological niche evolution. The main analyses include: initial exploration of environmental data in occurrence records and accessible areas, preparation of data for phylogenetic analyses, executing comparative phylogenetic analyses of ecological niches, and plotting for interpretations. Details on the theoretical background and methods used can be found in: Owens et al. (2020) <doi:10.1002/ece3.6359>, Peterson et al. (1999) <doi:10.1126/science.285.5431.1265>, Soberón and Peterson (2005) <doi:10.17161/bi.v2i0.4>, Peterson (2011) <doi:10.1111/j.1365-2699.2010.02456.x>, Barve et al. (2011) <doi:10.1111/ecog.02671>, Machado-Stredel et al. (2021) <doi:10.21425/F5FBG48814>, Owens et al. (2013) <doi:10.1016/j.ecolmodel.2013.04.011>, Saupe et al. (2018) <doi:10.1093/sysbio/syx084>, and Cobos et al. (2021) <doi:10.1111/jav.02868>.

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

This package implements methods in Mathur and VanderWeele (in preparation) to characterize global evidence strength across W correlated ordinary least squares (OLS) hypothesis tests. Specifically, uses resampling to estimate a null interval for the total number of rejections in, for example, 95% of samples generated with no associations (the global null), the excess hits (the difference between the observed number of rejections and the upper limit of the null interval), and a test of the global null based on the number of rejections.

This package provides computational tools for nonlinear longitudinal models, in particular the intrinsically nonlinear models, in four scenarios: (1) univariate longitudinal processes with growth factors, with or without covariates including time-invariant covariates (TICs) and time-varying covariates (TVCs); (2) multivariate longitudinal processes that facilitate the assessment of correlation or causation between multiple longitudinal variables; (3) multiple-group models for scenarios (1) and (2) to evaluate differences among manifested groups, and (4) longitudinal mixture models for scenarios (1) and (2), with an assumption that trajectories are from multiple latent classes. The methods implemented are introduced in Liu (2025) <doi:10.3758/s13428-025-02596-4>.

Measure the dependence structure between two random variables with a new correlation coefficient and extend it to hypothesis test, feature screening and false discovery rate control.

Various visual and numerical diagnosis methods for the nonlinear mixed effect model, including visual predictive checks, numerical predictive checks, and coverage plots (Karlsson and Holford, 2008, <https://www.page-meeting.org/?abstract=1434>).

Implementation of forward selection based on cross-validated linear and logistic regression.

Calculates a cumulative summation nonparametric extended median test based on the work of Brown & Schaffer (2020) <DOI:10.1080/03610926.2020.1738492>. It then generates a control chart to assess processes and determine if any streams are out of control.

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.

Nonparametric smoothing methods for density and regression estimation and inference with circular data. The package provides kernel density estimation along with inferential tools such as circular SiZer for feature significance, mode estimation, and modal clustering. It includes multiple methods for selecting the smoothing parameter, allowing users to optimize the trade-off between bias and variance. Various plotting functions help visualize estimated densities, modes, clusters, and significance features. For regression, the package implements nonparametric estimation of the mean regression function as well as other conditional characteristics, including modal regression and generalized regression. Bandwidth selection is also supported in the regression context, and testing procedures are available to assess structural features or effects in circular regression models.

This package provides null model algorithms for categorical and quantitative community ecology data. Extends classic binary null models (e.g., curveball', swap') to work with categorical data. Provides a stratified randomization framework for continuous data.

Nonparametric maximum likelihood estimation or Gaussian quadrature for overdispersed generalized linear models and variance component models.

Replacement for nls() tools for working with nonlinear least squares problems. The calling structure is similar to, but much simpler than, that of the nls() function. Moreover, where nls() specifically does NOT deal with small or zero residual problems, nlmrt is quite happy to solve them. It also attempts to be more robust in finding solutions, thereby avoiding singular gradient messages that arise in the Gauss-Newton method within nls(). The Marquardt-Nash approach in nlmrt generally works more reliably to get a solution, though this may be one of a set of possibilities, and may also be statistically unsatisfactory. Added print and summary as of August 28, 2012.

This package provides a Modern and Flexible Neo4J Driver, allowing you to query data on a Neo4J server and handle the results in R. It's modern in the sense it provides a driver that can be easily integrated in a data analysis workflow, especially by providing an API working smoothly with other data analysis and graph packages. It's flexible in the way it returns the results, by trying to stay as close as possible to the way Neo4J returns data. That way, you have the control over the way you will compute the results. At the same time, the result is not too complex, so that the "heavy lifting" of data wrangling is not left to the user.

Fit multinomial logistic regression with a penalty on the nuclear norm of the estimated regression coefficient matrix, using proximal gradient descent.

Implementation of the NetCutter algorithm described in Müller and Mancuso (2008) <doi:10.1371/journal.pone.0003178>. The package identifies co-occurring terms in a list of containers. For example, it may be used to detect genes that co-occur across genomes.

Utilities and kinship information for behavior genetics and developmental research using the National Longitudinal Survey of Youth (NLSY; <https://www.nlsinfo.org/>).

This package provides tools for fitting the neutral community model (NCM) to assess the role of stochastic processes in community assembly. The package implements the framework of Sloan et al. (2006) <doi:10.1111/j.1462-2920.2005.00956.x>, enabling users to evaluate neutral dynamics in ecological and microbial communities.

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.

Counts syllables in character vectors for English words. Imputes syllables as the number of vowel sequences for words not found.

Calculate Overall Survival or Recurrence-Free Survival for breast cancer patients, using NHS Predict'. The time interval for the estimation can be set up to 15 years, with default at 10. Incremental therapy benefits are estimated for hormone therapy, chemotherapy, trastuzumab, and bisphosphonates. An additional function, suited for SCAN audits, features a more user-friendly version of the code, with fewer inputs, but necessitates the correct standardised inputs. This work is not affiliated with the development of NHS Predict and its underlying statistical model. Details on NHS Predict can be found at: <doi:10.1186/bcr2464>. The web version of NHS Predict': <https://breast.predict.nhs.uk/>. A small dataset of 50 fictional patient observations is provided for the purpose of running examples with the main two functions, and an additional dataset is provided for running example with the dedicated SCAN function.

Computes various geospatial indices of socioeconomic deprivation and disparity in the United States. Some indices are considered "spatial" because they consider the values of neighboring (i.e., adjacent) census geographies in their computation, while other indices are "aspatial" because they only consider the value within each census geography. Two types of aspatial neighborhood deprivation indices (NDI) are available, including: (1) based on Messer et al. (2006) <doi:10.1007/s11524-006-9094-x> and (2) based on Andrews et al. (2020) <doi:10.1080/17445647.2020.1750066> and Slotman et al. (2022) <doi:10.1016/j.dib.2022.108002> who use variables chosen by Roux and Mair (2010) <doi:10.1111/j.1749-6632.2009.05333.x>. Both are a decomposition of multiple demographic characteristics from the U.S. Census Bureau American Community Survey 5-year estimates (ACS-5; 2006-2010 onward). Using data from the ACS-5 (2005-2009 onward), the package can also compute indices of racial or ethnic residential segregation, including but limited to those discussed in Massey & Denton (1988) <doi:10.1093/sf/67.2.281>, and additional indices of socioeconomic disparity.

Functions, examples and data from the first and the second edition of "Numerical Methods and Optimization in Finance" by M. Gilli, D. Maringer and E. Schumann (2019, ISBN:978-0128150658). The package provides implementations of optimisation heuristics (Differential Evolution, Genetic Algorithms, Particle Swarm Optimisation, Simulated Annealing and Threshold Accepting), and other optimisation tools, such as grid search and greedy search. There are also functions for the valuation of financial instruments such as bonds and options, for portfolio selection and functions that help with stochastic simulations.

This package provides a SQLite-backed cell-level cache that can be used as a drop-in backend by the nordstat family of packages ('rKolada', rTrafa', and pixieweb'). Designed for multi-user web applications where minimal fetch latency and asynchronous writes are required. Individual statistical values ("cells") are stored in a gatekeeper schema with a sidecar table for arbitrary metadata dimensions, enabling deduplication across overlapping queries.