This package provides tools for performing nowcasting using the Chain-Ladder method <https://en.wikipedia.org/wiki/Chain-ladder_method>. It supports both non-cumulative delay-based estimation and model-based completeness fitting (e.g., using logistic or Gompertz curves) to predict final counts from partially reported data.

Subsampling methods for big data under different models and assumptions. Starting with linear regression and leading to Generalised Linear Models, softmax regression, and quantile regression. Specifically, the model-robust subsampling method proposed in Mahendran, A., Thompson, H., and McGree, J. M. (2023) <doi:10.1007/s00362-023-01446-9>, where multiple models can describe the big data, and the subsampling framework for potentially misspecified Generalised Linear Models in Mahendran, A., Thompson, H., and McGree, J. M. (2025) <doi:10.48550/arXiv.2510.05902>.

Statistical methods for whole-trial and time-domain analysis of single cell neural response to multiple stimuli presented simultaneously. The package is based on the paper by C Glynn, ST Tokdar, A Zaman, VC Caruso, JT Mohl, SM Willett, and JM Groh (2021) "Analyzing second order stochasticity of neural spiking under stimuli-bundle exposure", is in press for publication by the Annals of Applied Statistics. A preprint may be found at <arXiv:1911.04387>.

Additional nonlinear regression functions using self-start (SS) algorithms. One of the functions is the Beta growth function proposed by Yin et al. (2003) <doi:10.1093/aob/mcg029>. There are several other functions with breakpoints (e.g. linear-plateau, plateau-linear, exponential-plateau, plateau-exponential, quadratic-plateau, plateau-quadratic and bilinear), a non-rectangular hyperbola and a bell-shaped curve. Twenty eight (28) new self-start (SS) functions in total. This package also supports the publication Nonlinear regression Models and applications in agricultural research by Archontoulis and Miguez (2015) <doi:10.2134/agronj2012.0506>, a book chapter with similar material <doi:10.2134/appliedstatistics.2016.0003.c15> and a publication by Oddi et. al. (2019) in Ecology and Evolution <doi:10.1002/ece3.5543>. The function nlsLMList uses nlsLM for fitting, but it is otherwise almost identical to nlme::nlsList'.In addition, this release of the package provides functions for conducting simulations for nlme and gnls objects as well as bootstrapping. These functions are intended to work with the modeling framework of the nlme package. It also provides four vignettes with extended examples.

In this implementation of the Naive Bayes classifier following class conditional distributions are available: Bernoulli', Categorical', Gaussian', Poisson', Multinomial and non-parametric representation of the class conditional density estimated via Kernel Density Estimation. Implemented classifiers handle missing data and can take advantage of sparse data.

Automatic time series modelling with neural networks. Allows fully automatic, semi-manual or fully manual specification of networks. For details of the specification methodology see: (i) Crone and Kourentzes (2010) <doi:10.1016/j.neucom.2010.01.017>; and (ii) Kourentzes et al. (2014) <doi:10.1016/j.eswa.2013.12.011>.

This package provides functions for probability and non-probability sampling design, sample selection, and population estimation tailored to natural resource management. Probability methods include simple random sampling, stratified sampling, systematic sampling, cluster sampling, and probability-proportional-to-size sampling. Non-probability methods include convenience, judgement-based, and quota sampling. Estimation functions cover means, totals, ratio estimators, regression estimators, and the unequal-probability estimator of Horvitz and Thompson (1952, <doi:10.2307/2280784>) for unequal-probability designs. Utilities support biomass, soil-loss, and carbon-stock estimation from field plots. Spatial extensions provide random, systematic, stratified, and raster-weighted sampling within geographic polygons using the sf and terra packages, with extraction of remote-sensing covariates at sample locations. Applications include forest inventory, soil erosion monitoring, watershed studies, and ecological field surveys.

This package provides a tool set for food information and dietary assessment. It uses food composition data from several reference databases, including: USDA (United States), CIQUAL (France), BEDCA (Spain), CNF (Canada) and STFCJ (Japan). NutrienTrackeR calculates the intake levels for both macronutrient and micronutrients, and compares them with the recommended dietary allowances (RDA). It includes a number of visualization tools, such as time series plots of nutrient intake, and pie-charts showing the main foods contributing to the intake level of a given nutrient. A shiny app exposing the main functionalities of the package is also provided.

Instant access to harmonized National Health and Nutrition Examination Survey (NHANES) data spanning 1999-2023. Retrieve pre-processed datasets from reliable cloud storage with automatic type reconciliation and integrated search tools for variables and datasets. Simplifies NHANES data workflows by handling cycle management and maintaining data consistency across survey waves. Data is sourced from <https://www.cdc.gov/nchs/nhanes/>.

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.

Automated population pharmacokinetic modeling framework for data-driven initialisation, model evaluation, and metaheuristic optimization. Supports genetic algorithms, ant colony optimization, tabu search, and stepwise procedures for automated model selection and parameter estimation within the nlmixr2 ecosystem.

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>.

This package provides a comprehensive toolkit for calculating and visualizing Nitrogen Use Efficiency (NUE) indicators in agricultural research. The package implements 23 parameters categorized into fertilizer-based, plant-based, soil-based, isotope-based, ecology-based, and system-based indicators based on Congreves et al. (2021) <doi:10.3389/fpls.2021.637108>. Key features include vectorized calculations for paired-plot experimental designs, batch processing capabilities for handling large datasets, and built-in visualization tools using ggplot2'. Designed to streamline the workflow from raw agronomic data to publication-ready metrics and plots.

Statistical tool for learning the structure of direct associations among variables for continuous data, discrete data and mixed discrete-continuous data. The package is based on the copula graphical model in Behrouzi and Wit (2017) <doi:10.1111/rssc.12287>.

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.

Network meta-analysis tools based on contrast-based approach using the multivariate meta-analysis and meta-regression models (Noma et al. (2025) <doi:10.1101/2025.09.15.25335823>). Comprehensive analysis tools for network meta-analysis and meta-regression (e.g., synthesis analysis, ranking analysis, and creating league table) are available through simple commands. For inconsistency assessment, the local and global inconsistency tests based on the Higgins design-by-treatment interaction model are available. In addition, the side-splitting methods and Jackson's random inconsistency model can be applied. Standard graphical tools for network meta-analysis, including network plots, ranked forest plots, and transitivity analyses, are also provided. For the synthesis analyses, the Noma-Hamura's improved REML (restricted maximum likelihood)-based methods (Noma et al. (2023) <doi:10.1002/jrsm.1652> <doi:10.1002/jrsm.1651>) are adopted as the default methods.

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

There are three distinct approaches for phase error correction, they are: a single linear model with a choice of optimization functions, multiple linear models with optimization function choices and a shrinkage-based method. The methodology is based on our new algorithms and various references (Binczyk et al. (2015) <doi:10.1186/1475-925X-14-S2-S5>,Chen et al. (2002) <doi:10.1016/S1090-7807(02)00069-1>, de Brouwer (2009) <doi:10.1016/j.jmr.2009.09.017>, Džakula (2000) <doi:10.1006/jmre.2000.2123>, Ernst (1969) <doi:10.1016/0022-2364(69)90003-1>, Liland et al. (2010) <doi:10.1366/000370210792434350>).

This package contains a module to define neural networks from custom components and versions of Autoencoder, BP, LVQ, MAM NN.

Extends the classical Newman studentized range statistic in various ways that can be applied to genome-scale transcriptomic or other expression data.

The aim is to develop an R package, which is the new.dist package, for the probability (density) function, the distribution function, the quantile function and the associated random number generation function for discrete and continuous distributions, which have recently been proposed in the literature. This package implements the following distributions: The Power Muth Distribution, a Bimodal Weibull Distribution, the Discrete Lindley Distribution, The Gamma-Lomax Distribution, Weighted Geometric Distribution, a Power Log-Dagum Distribution, Kumaraswamy Distribution, Lindley Distribution, the Unit-Inverse Gaussian Distribution, EP Distribution, Akash Distribution, Ishita Distribution, Maxwell Distribution, the Standard Omega Distribution, Slashed Generalized Rayleigh Distribution, Two-Parameter Rayleigh Distribution, Muth Distribution, Uniform-Geometric Distribution, Discrete Weibull Distribution.

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 R native ONNX model inference without requiring Python', reticulate bindings, or TensorFlow'. This package directly binds the ONNX Runtime C API via Rcpp', enabling real-time inference for .onnx engines, all within R. Standard CPU execution is supported as well as the CoreML Execution Provider (CEP) for Apple Silicon, all without external bindings. This package handles OS detection, linking ONNX libraries, and inference. For more information about ONNX Runtime see <https://onnxruntime.ai/>.