This package provides functions to estimate the parameters of the generalized Poisson distribution with or without covariates using maximum likelihood. The references include Nikoloulopoulos A.K. & Karlis D. (2008). "On modeling count data: a comparison of some well-known discrete distributions". Journal of Statistical Computation and Simulation, 78(3): 437--457, <doi:10.1080/10629360601010760> and Consul P.C. & Famoye F. (1992). "Generalized Poisson regression model". Communications in Statistics - Theory and Methods, 21(1): 89--109, <doi:10.1080/03610929208830766>.

This package provides a "tabular-data-resource" (<https://specs.frictionlessdata.io/tabular-data-resource/>) is a simple format to describe a singular tabular data resource such as a CSV file. It includes support both for metadata such as author and title and a schema to describe the data, for example the types of the fields/columns in the data. Create a tabular-data-resource by providing a data.frame and specifying metadata. Write and read tabular-data-resources to and from disk.

Finds causal connections in precision data, finds lags and embeddings in time series, guides training of neural networks and other smooth models, evaluates their performance, gives a mathematically grounded answer to the over-training problem. Smooth regression is based on the Gamma test, which measures smoothness in a multivariate relationship. Causal relations are smooth, noise is not. sr includes the Gamma test and search techniques that use it. References: Evans & Jones (2002) <doi:10.1098/rspa.2002.1010>, AJ Jones (2004) <doi:10.1007/s10287-003-0006-1>.

Functions, methods, and datasets for fitting dimension reduction regression, using slicing (methods SAVE and SIR), Principal Hessian Directions (phd, using residuals and the response), and an iterative IRE. Partial methods, that condition on categorical predictors are also available. A variety of tests, and stepwise deletion of predictors, is also included. Also included is code for computing permutation tests of dimension. Adding additional methods of estimating dimension is straightforward. For documentation, see the vignette in the package. With version 3.0.4, the arguments for dr.step have been modified.

In Australia, a financial year (or fiscal year) is the period from 1 July to 30 June of the following calendar year. As such, many databases need to represent and validate financial years efficiently. While the use of integer years with a convention that they represent the year ending is common, it may lead to ambiguity with calendar years. On the other hand, string representations may be too inefficient and do not easily admit arithmetic operations. This package tries to make validation of financial years quicker while retaining clarity.

Analyze bioequivalence study data with industrial strength. The statistical core is delegated to the sasLM package, which reproduces SAS PROC GLM output; this covers the analysis of variance, the least square means, and the confidence interval of a 2x2 crossover study. Sample size could be determined for various crossover designs, such as 2x2 design, 2x4 design, 4x4 design, Balaam design, Two-sequence dual design, and William design. Reference: Chow SC, Liu JP. Design and Analysis of Bioavailability and Bioequivalence Studies. 3rd ed. (2009, ISBN:978-1-58488-668-6).

The quantity-intensity (Q/I) relationships, first introduced by Beckett (1964), can be employed to assess the K supplying capacity of different soils based on solid-solution exchange equilibria. Such relationships describe the changes in K+ concentration in the soil solution (or the intensity factor) in relation to the corresponding changes in K+ at exchange sites of the soil (or the capacity or quantity factor). Activity ratio of K to Ca or Ca+Mg is generally used as the variable denoting the intensity, whereas, change in exchangeable K is used to denote the quantity factor.

Discriminant Analysis (DA) for evolutionary inference (Qin, X. et al, 2020, <doi:10.22541/au.159256808.83862168>), especially for population genetic structure and community structure inference. This package incorporates the commonly used linear and non-linear, local and global supervised learning approaches (discriminant analysis), including Linear Discriminant Analysis of Kernel Principal Components (LDAKPC), Local (Fisher) Linear Discriminant Analysis (LFDA), Local (Fisher) Discriminant Analysis of Kernel Principal Components (LFDAKPC) and Kernel Local (Fisher) Discriminant Analysis (KLFDA). These discriminant analyses can be used to do ecological and evolutionary inference, including demography inference, species identification, and population/community structure inference.

Empirical likelihood (EL) inference for two-sample problems. The following statistics are included: the difference of two-sample means, smooth Huber estimators, quantile (qdiff) and cumulative distribution functions (fdiff), probability-probability (P-P) and quantile-quantile (Q-Q) plots as well as receiver operating characteristic (ROC) curves. Also includes two-sample block-wise empirical likelihood (BEL) and a frequency-domain empirical likelihood test for autocorrelation differences (FDEL). Methods for EL, P-P, Q-Q, ROC, qdiff and fdiff are based on Valeinis and Cers (2011) <http://home.lu.lv/~valeinis/lv/petnieciba/EL_TwoSample_2011.pdf>.

We provide a toolbox to conduct a Bayesian meta-analysis for estimating the current expansion rate of the Universe, called the Hubble constant H0, via time delay cosmography. The input data are Fermat potential difference and time delay estimates. For a robust inference, we assume a Student's t error for these inputs. Given these inputs, the meta-analysis produces posterior samples of the model parameters including the Hubble constant via Metropolis-Hastings within Gibbs. The package provides an option to implement repelling-attracting Metropolis-Hastings within Gibbs in a case where the parameter space has multiple modes.

Flexible general-purpose toolbox implementing genetic algorithms (GAs) for stochastic optimisation. Binary, real-valued, and permutation representations are available to optimize a fitness function, i.e., a function provided by users depending on their objective function. Several genetic operators are available and can be combined to explore the best settings for the current task. Furthermore, users can define new genetic operators and easily evaluate their performances. Local search using general-purpose optimisation algorithms can be applied stochastically to exploit interesting regions. GAs can be run sequentially or in parallel, using an explicit master-slave parallelisation or a coarse-grain islands approach.

Generate the James Blinding Index, as described in James et al (1996) <https://pubmed.ncbi.nlm.nih.gov/8841652/> and the Bang Blinding Index, as described in Bang et al (2004) <https://pubmed.ncbi.nlm.nih.gov/15020033/>. These are measures to assess whether or not satisfactory blinding has been maintained in a randomized, controlled, clinical trial. These can be generated for trial subjects, research coordinators and principal investigators, based upon standardized questionnaires that have been administered, to assess whether or not they can correctly guess to which treatment arm (e.g. placebo or treatment) subjects were assigned at randomization.

User-friendly functions for extracting a data table (row for each match, column for each group) from non-tabular text data using regular expressions, and for melting columns that match a regular expression. Patterns are defined using a readable syntax that makes it easy to build complex patterns in terms of simpler, re-usable sub-patterns. Named R arguments are translated to column names in the output; capture groups without names are used internally in order to provide a standard interface to three regular expression C libraries ('PCRE', RE2', ICU'). Output can also include numeric columns via user-specified type conversion functions.

Facilitates easy analysis of factorial experiments, including purely within-Ss designs (a.k.a. "repeated measures"), purely between-Ss designs, and mixed within-and-between-Ss designs. The functions in this package aim to provide simple, intuitive and consistent specification of data analysis and visualization. Visualization functions also include design visualization for pre-analysis data auditing, and correlation matrix visualization. Finally, this package includes functions for non-parametric analysis, including permutation tests and bootstrap resampling. The bootstrap function obtains predictions either by cell means or by more advanced/powerful mixed effects models, yielding predictions and confidence intervals that may be easily visualized at any level of the experiment's design.

Cross-validation methods of regression models that exploit features of various modeling functions to improve speed. Some of the methods implemented in the package are novel, as described in Fox and Monette (2026) <doi:10.18637/jss.v116.i08>, and the package vignettes. For general introductions to cross-validation, see, for example, Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani (2021, ISBN 978-1-0716-1417-4, Secs. 5.1, 5.3), "An Introduction to Statistical Learning with Applications in R, Second Edition", and Trevor Hastie, Robert Tibshirani, and Jerome Friedman (2009, ISBN 978-0-387-84857-0, Sec. 7.10), "The Elements of Statistical Learning, Second Edition".

This package provides a set of utilities for calculating the Deficit (frailty) Index (DI) in gerontological studies. The deficit index was first proposed by Arnold Mitnitski and Kenneth Rockwood and represents a proxy measure of aging and also can be served as a sensitive predictor of survival. For more information, see (i)"Accumulation of Deficits as a Proxy Measure of Aging" by Arnold B. Mitnitski et al. (2001), The Scientific World Journal 1, <DOI:10.1100/tsw.2001.58>; (ii) "Frailty, fitness and late-life mortality in relation to chronological and biological age" by Arnold B Mitnitski et al. (2001), BMC Geriatrics2002 2(1), <DOI:10.1186/1471-2318-2-1>.

This package implements functions to derive uncertainty intervals for (i) regression (linear and probit) parameters under missing not at random (non-ignorable missingness) as introduced in Genbäck, M., Stanghellini, E., and de Luna, X. (2015) <doi:10.1007/s00362-014-0610-x> and Genbäck, M., Ng, N., Stanghellini, E., and de Luna, X. (2018) <doi:10.1007/s10433-017-0448-x>. Also includes methods for doubly robust and outcome regression estimators of average causal effects under unobserved confounding as in Genbäck, M. and de Luna, X. (2018) <doi:10.1111/biom.13001>, and for partial correlation analysis following Gorbach, T. and de Luna, X. (2018) <doi:10.1016/j.spl.2018.05.027>.

This package provides a lightweight grammar of tables. Build a table by declaring a JSON spec (titles, spanners, row groups, footnotes, formatting functions, etc.); a tiny vanilla JavaScript runtime builds the HTML table from the spec on page load. Cells can hold inline plots (error bars, dot plots, mini line and bar charts) drawn as SVG in the browser from the numbers alone. Tables can optionally be made interactive (sorting, searching, filtering, pagination, column hiding, expandable row detail, and CSV download) via a separate extension loaded only when requested. No sass', no V8', no htmlwidgets â just base R and xfun ('htmltools is used only for the optional Shiny binding and for embedding tables in other HTML output).

Geographical detectors for measuring spatial stratified heterogeneity, as described in Jinfeng Wang (2010) <doi:10.1080/13658810802443457> and Jinfeng Wang (2016) <doi:10.1016/j.ecolind.2016.02.052>. Includes the optimal discretization of continuous data, four primary functions of geographical detectors, comparison of size effects of spatial unit and the visualizations of results. To use the package and to refer the descriptions of the package, methods and case datasets, please cite Yongze Song (2020) <doi:10.1080/15481603.2020.1760434>. The model has been applied in factor exploration of road performance and multi-scale spatial segmentation for network data, as described in Yongze Song (2018) <doi:10.3390/rs10111696> and Yongze Song (2020) <doi:10.1109/TITS.2020.3001193>, respectively.

Some tools for developing general equilibrium models and some general equilibrium models. These models can be used for teaching economic theory and are built by the methods of new structural economics (see LI Wu, 2019, ISBN: 9787521804225, General Equilibrium and Structural Dynamics: Perspectives of New Structural Economics. Beijing: Economic Science Press). The model form and mathematical methods can be traced back to J. von Neumann (1945, A Model of General Economic Equilibrium. The Review of Economic Studies, 13. pp. 1-9), J. G. Kemeny, O. Morgenstern and G. L. Thompson (1956, A Generalization of the von Neumann Model of an Expanding Economy, Econometrica, 24, pp. 115-135) et al. By the way, J. G. Kemeny is a co-inventor of the computer language BASIC.

An implementation of the MaxLFQ algorithm by Cox et al. (2014) <doi:10.1074/mcp.M113.031591> using least-squares analysis in a comprehensive pipeline for processing proteomics data in data-independent acquisition mode (Pham et al. 2020 <doi:10.1093/bioinformatics/btz961>, and a new algorithm using the conjugate gradient method Pham et al. 2026 <doi:10.1021/acs.jproteome.5c01038>). It offers additional options for protein quantification using the N most intense fragment ions, using all fragment ions, the median polish algorithm by Tukey (1977, ISBN:0201076160), a robust linear model, and fragment weighting. A new iq format enables the processing of large-scale datasets. In general, the tool can be used to integrate multiple proportional observations into a single quantitative value.

Estimation of person parameters (abilities) for the 1, 2, 3 and 4 parameter logistic model, the (generalized) partial credit model and tests that mix dichotomous and polytomous items, under the assumption that the item parameters are known and fixed: maximum likelihood, weighted likelihood (Warm, 1989 <doi:10.1007/BF02294627>), maximum and expected a posteriori and robust estimation (Schuster and Yuan, 2011 <doi:10.3102/1076998610396890>). Further features are jackknife estimates (Wainer and Wright, 1980 <doi:10.1007/BF02293910>), plausible values, the person fit statistics lz, lz* (Snijders, 2001 <doi:10.1007/BF02294437>), infit and outfit, and the simulation of response data. Models fitted with the eRm package can be passed on directly. The package is useful, e.g., when items of an item bank are given to a new sample of persons.

The aim of od is to provide tools and example datasets for working with origin-destination ('OD') datasets of the type used to describe aggregate urban mobility patterns (Carey et al. 1981) <doi:10.1287/trsc.15.1.32>. The package builds on functions for working with OD data in the package stplanr', (Lovelace and Ellison 2018) <doi:10.32614/RJ-2018-053> with a focus on computational efficiency and support for the sf class system (Pebesma 2018) <doi:10.32614/RJ-2018-009>. With few dependencies and a simple class system based on data frames, the package is intended to facilitate efficient analysis of OD datasets and to provide a place for developing new functions. The package enables the creation and analysis of geographic entities representing large scale mobility patterns, from daily travel between zones in cities to migration between countries.

This package provides support software for Statistical Analysis and Data Display (Second Edition, Springer, ISBN 978-1-4939-2121-8, 2015) and (First Edition, Springer, ISBN 0-387-40270-5, 2004) by Richard M. Heiberger and Burt Holland. This contemporary presentation of statistical methods features extensive use of graphical displays for exploring data and for displaying the analysis. The second edition includes redesigned graphics and additional chapters. The authors emphasize how to construct and interpret graphs, discuss principles of graphical design, and show how accompanying traditional tabular results are used to confirm the visual impressions derived directly from the graphs. Many of the graphical formats are novel and appear here for the first time in print. All chapters have exercises. All functions introduced in the book are in the package. R code for all examples, both graphs and tables, in the book is included in the scripts directory of the package.