Calculate various functions needed for design and monitoring clinical trials with negative binomial endpoint with variable follow-up. This version has a few changes compared to the previous version 1.0.0, including (1) correct a typo in Type 1 censoring, mtbnull=bnull and (2) restructure the code to account for shape parameter equal to zero, i.e. Poisson scenario.

Scrapes and cleans data from the NHL and ESPN APIs into data.frames and lists. Wraps 125+ endpoints documented in <https://github.com/RentoSaijo/nhlscraper/wiki> from high-level multi-season summaries and award winners to low-level decisecond replays and bookmakers odds, making them more accessible. Features cleaning and visualization tools, primarily for play-by-plays.

This package implements the Normal-Block model, a Gaussian graphical model with a latent clustering structure for the multivariate analysis of continuous data. The model clusters variables and, building on the graphical lasso, infers a network of statistical dependencies between clusters rather than between individual variables, for known or unknown clusterings, with an optional zero-inflation extension for data with an excess of exact zeros. A complementary family clusters variables by their regression response to covariates rather than by their covariance, sharing one profile per cluster. See Tous & Chiquet (2026) <doi:10.1016/j.csda.2026.108347> for the model itself and its variational expectation-maximization estimation procedure.

Analysis of multivariate data with two-way completely randomized factorial design. The analysis is based on fully nonparametric, rank-based methods and uses test statistics based on the Dempster's ANOVA, Wilk's Lambda, Lawley-Hotelling and Bartlett-Nanda-Pillai criteria. The multivariate response is allowed to be ordinal, quantitative, binary or a mixture of the different variable types. The package offers two functions performing the analysis, one for small and the other for large sample sizes. The underlying methodology is largely described in Bathke and Harrar (2016) <doi:10.1007/978-3-319-39065-9_7> and in Munzel and Brunner (2000) <doi:10.1016/S0378-3758(99)00212-8> and in Kiefel and Bathke (2022) <doi:10.1515/stat-2022-0112>.

Probabilistic time series forecasting via Natural Gradient Boosting for Probabilistic Prediction.

Build, compose, stack, sum, and realize feed-forward neural networks as algebraic objects. Implements the calculus and constructive approximations described by Rafi, Padgett, and Nakarmi (2024) <doi:10.48550/arXiv.2402.01058>, Grohs, Hornung, Jentzen, et al. (2023) <doi:10.1007/s10444-022-09970-2>, and Jentzen, Kuckuck, and von Wurstemberger (2023) <doi:10.48550/arXiv.2310.20360>. Includes neural network polynomials, transcendental-function approximations, multidimensional maximum convolution, and vectorized batch realization.

Represent network or igraph objects whose vertices can be represented by features in an sf object as a network graph surmising a sf plot. Fits into ggplot2 grammar.

Simplify the exploratory data analysis process for multiple network data sets with the help of hierarchical clustering, consensus clustering and heatmaps. Multiple network data consists of multiple disjoint networks that have common variables (e.g. ego networks). This package contains the necessary tools for exploring such data, from the data pre-processing stage to the creation of dynamic visualizations.

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.

Providing a common set of simplified web scraping tools for working with the NHS Data Dictionary <https://datadictionary.nhs.uk/data_elements_overview.html>. The intended usage is to access the data elements section of the NHS Data Dictionary to access key lookups. The benefits of having it in this package are that the lookups are the live lookups on the website and will not need to be maintained. This package was commissioned by the NHS-R community <https://nhsrcommunity.com/> to provide this consistency of lookups. The OpenSafely lookups have now been added <https://www.opencodelists.org/docs/>.

Given a network (e.g. a food web), estimates several network indices. These include: Ascendency network indices, Direct and indirect dependencies, Effective measures, Environ network indices, General network indices, Pathway analysis, Network uncertainty indices and constraint efficiencies and the trophic level and omnivory indices of food webs.

Allele frequency databases for 50 forensic short tandem repeat (STR) markers, covering Norway and several broader regional populations: Europe, Africa, South America, West Asia, Middle Asia, and East Asia. Developed and maintained for use at the Department of Forensic Sciences, Oslo, Norway.

Posterior distribution of case-control fine-mapping. Specifically, Bayesian variable selection for single-nucleotide polymorphism (SNP) data using the normal-gamma prior. Alenazi A.A., Cox A., Juarez M,. Lin W-Y. and Walters, K. (2019) Bayesian variable selection using partially observed categorical prior information in fine-mapping association studies, Genetic Epidemiology. <doi:10.1002/gepi.22213>.

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.

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>.

Density, distribution function, quantile function and random generation for the Nakagami distribution of Nakagami (1960) <doi:10.1016/B978-0-08-009306-2.50005-4>.

This package provides different interval estimates of a location parameter when the sample size is one or more. These include classical methods when n=1, new Bayesian analogues of these classical methods, and extensions of these methods to larger sample sizes in the normal case. Other functions calculate Bayes factors based on t-statistics, and implement the (generalized) inverse normal distribution. These methods are described in Gerard (2026) <doi:10.48550/arXiv.2607.25007>.

This package provides functions for nominal data mining based on bipartite graphs, which build a pipeline for analysis and missing values imputation. Methods are mainly from the paper: Jafari, Mohieddin, et al. (2021) <doi:10.1101/2021.03.18.436040>, some new ones are also included.

We solve non linear least squares problems with optional equality and/or inequality constraints. Non linear iterations are globalized with back-tracking method. Linear problems are solved by dense QR decomposition from LAPACK which can limit the size of treated problems. On the other side, we avoid condition number degradation which happens in classical quadratic programming approach. Inequality constraints treatment on each non linear iteration is based on NNLS method (by Lawson and Hanson). We provide an original function lsi_ln for solving linear least squares problem with inequality constraints in least norm sens. Thus if Jacobian of the problem is rank deficient a solution still can be provided. However, truncation errors are probable in this case. Equality constraints are treated by using a basis of Null-space. User defined function calculating residuals must return a list having residual vector (not their squared sum) and Jacobian. If Jacobian is not in the returned list, package numDeriv is used to calculated finite difference version of Jacobian. The NLSIC method was fist published in Sokol et al. (2012) <doi:10.1093/bioinformatics/btr716>.

This package provides pseudo-weighted estimates of means and prevalences for finite population inference from nonprobability samples using auxiliary information from one or multiple probability reference surveys. The package supports estimation with multiple reference surveys, allowing auxiliary information to be combined when no single survey contains all variables relevant to participation. Optional cumulative precalibration can be applied to align weighted totals of shared variables across surveys. Methods are based on the generalized estimating equations framework of Landsman et al. (2026) <doi:10.1002/sim.70403> for correcting participation bias. For a single reference survey, the package implements the raking ratio calibration method and includes the adjusted logistic propensity (ALP) method of Wang, Valliant, and Li (2021) <doi:10.1002/sim.9122>, as well as the Chen-Li-Wu (CLW) method of Chen, Li, and Wu (2020) <doi:10.1080/01621459.2019.1677241>. Analytic variance estimation uses Taylor linearization and accounts for complex sampling designs in the reference surveys via integration with the survey package.

This package provides utility functions and custom probability distribution for Bayesian analyses of radiocarbon dates within the nimble modelling framework. It includes various population growth models, nimbleFunction objects, as well as a suite of functions for prior and posterior predictive checks for demographic inference (Crema and Shoda (2021) <doi:10.1371/journal.pone.0251695>) and other analyses.

Includes five particle filtering algorithms for use with state space models in the nimble system: Auxiliary', Bootstrap', Ensemble Kalman filter', Iterated Filtering 2', and Liu-West', as described in Michaud et al. (2021), <doi:10.18637/jss.v100.i03>. A full User Manual is available at <https://r-nimble.org>.

Perform a stratified weighted log-rank test in a randomized controlled trial. Tests can be visualized as a difference in average score on the two treatment arms. These methods are described in Magirr and Burman (2018) <doi:10.48550/arXiv.1807.11097>, Magirr (2020) <doi:10.48550/arXiv.2007.04767>, and Magirr and Jimenez (2022) <doi:10.48550/arXiv.2201.10445>.

This package provides measures to describe and manipulate one-mode, two-mode, multiplex, and multilevel networks using matrix algebra. Implements functions for network centrality, cohesive subgroups, structural holes, similarity measures, path distances, signed networks, and random network generation. Supports ego-centric and whole-network analyses, including dyadic and triadic census, structural balance, and bipartite projections. Key references: Bonacich (1972) <doi:10.1080/0022250X.1972.9989806>, Breiger (1974) <doi:10.2307/2576011>, Kivelä et al. (2014) <doi:10.1093/comnet/cnu016>, Espinosa-Rada et al. (2024) <doi:10.1016/j.socnet.2023.11.008>.