This package provides estimation methods for markets in equilibrium and disequilibrium. Supports the estimation of an equilibrium and four disequilibrium models with both correlated and independent shocks. Also provides post-estimation analysis tools, such as aggregation, marginal effect, and shortage calculations. See Karapanagiotis (2024) <doi:10.18637/jss.v108.i02> for an overview of the functionality and examples. The estimation methods are based on full information maximum likelihood techniques given in Maddala and Nelson (1974) <doi:10.2307/1914215>. They are implemented using the analytic derivative expressions calculated in Karapanagiotis (2020) <doi:10.2139/ssrn.3525622>. Standard errors can be estimated by adjusting for heteroscedasticity or clustering. The equilibrium estimation constitutes a case of a system of linear, simultaneous equations. Instead, the disequilibrium models replace the market-clearing condition with a non-linear, short-side rule and allow for different specifications of price dynamics.

We develop Multi-source Graph Synthesis (MUGS), an algorithm designed to create embeddings for pediatric Electronic Health Record (EHR) codes by leveraging graphical information from three distinct sources: (1) pediatric EHR data, (2) EHR data from the general patient population, and (3) existing hierarchical medical ontology knowledge shared across different patient populations. See Li et al. (2024) <doi:10.1038/s41746-024-01320-4> for details.

Datasets and functions for the book "Modélisation statistique par la pratique avec R", F. Bertrand, E. Claeys and M. Maumy-Bertrand (2019, ISBN:9782100793525, Dunod, Paris). The first chapter of the book is dedicated to an introduction to the R statistical software. The second chapter deals with correlation analysis: Pearson, Spearman and Kendall simple, multiple and partial correlation coefficients. New wrapper functions for permutation tests or bootstrap of matrices of correlation are provided with the package. The third chapter is dedicated to data exploration with factorial analyses (PCA, CA, MCA, MDA) and clustering. The fourth chapter is dedicated to regression analysis: fitting and model diagnostics are detailed. The exercises focus on covariance analysis, logistic regression, Poisson regression, two-way analysis of variance for fixed or random factors. Various example datasets are shipped with the package: for instance on pokemon, world of warcraft, house tasks or food nutrition analyses.

This package implements the methods described in Bond S, Farewell V, 2006, Exact Likelihood Estimation for a Negative Binomial Regression Model with Missing Outcomes, Biometrics.

This package provides tools to handle, manipulate and explore trajectory data, with an emphasis on data from tracked animals. The package is designed to support large studies with several million location records and keep track of units where possible. Data import directly from movebank <https://www.movebank.org/cms/movebank-main> and files is facilitated.

Builds matched nulls for cluster-count claims: synthetic twins of a dataset that preserve every marginal distribution and the full correlation matrix while containing no cluster structure by construction. A reported number of clusters or "types" can then be tested against what the data's own margins and covariance already produce, using any clustering pipeline. A t-copula option adds tail dependence to the null, so that an apparent excess of clusters can be checked against a heavier-tailed alternative before it is read as evidence of types. Implements the matched-null procedure of Meng (2026) "Types Without Taxa" <doi:10.17605/OSF.IO/2EKCG>.

Uses a kernel smoothing approach to calculate Mutual Information for comparisons between all types of variables including continuous vs continuous, continuous vs discrete and discrete vs discrete. Uses a nonparametric bias correction giving Bias Corrected Mutual Information (BCMI). Implemented efficiently in Fortran 95 with OpenMP and suited to large genomic datasets.

Identifies the optimal number of clusters by calculating the similarity between two clustering methods at the same number of clusters using the corrected indices of Rand and Jaccard as described in Albatineh and Niewiadomska-Bugaj (2011). The number of clusters at which the index attain its maximum more frequently is a candidate for being the optimal number of clusters.

Makes it possible to create an internally consistent repository consisting of selected packages from CRAN-like repositories. The user specifies a set of desired packages, and miniCRAN recursively reads the dependency tree for these packages, then downloads only this subset. The user can then install packages from this repository directly, rather than from CRAN. This is useful in production settings, e.g. server behind a firewall, or remote locations with slow (or zero) Internet access.

This package provides functions to estimate start and duration of moult from moult data, based on models developed in Underhill and Zucchini (1988, 1990).

Flexible time classes for time series analysis and forecasting with mixed temporal granularities. Supports linear and cyclical time representations in discrete and continuous forms, with timezone support, across multiple calendar systems including Gregorian and ISO week date calendars. Time points are stored numerically relative to a chronon; an atomic time granule defined by time units of a calendar. Calendrical arithmetic enables conversion between time granules (e.g. days to months) and calendar systems. Multi-unit arithmetic allows for temporal analysis with other granules of common calendars (e.g. fortnights are 2-week units). Time vectors of different granularities (e.g. monthly and quarterly) can be combined in a single vector, making mixtime ideal for data that changes observation frequency over time or requires temporal reconciliation across scales. The package is extensible, allowing users to define custom calendars that build upon civil and astronomical time systems.

This package provides a collection of functions to download and process weather data from the Oklahoma Mesonet <https://mesonet.org>. Functions are available for downloading station metadata, downloading Mesonet time series (MTS) files, importing MTS files into R, and converting soil temperature change measurements into soil matric potential and volumetric soil moisture.

This package performs the execution of the main procedures of multiple comparisons in the literature, Scott-Knott (1974) <http://www.jstor.org/stable/2529204>, Batista (2016) <http://repositorio.ufla.br/jspui/handle/1/11466>, including graphic representations and export to different extensions of its results. An additional part of the package is the presence of the performance evaluation of the tests (Type I error per experiment and the power). This will assist the user in making the decision for the chosen test.

This package provides a collection of function to solve multiple criteria optimization problems using genetic algorithms (NSGA-II). Also included is a collection of test functions.

Generate a monochrome palette from a starting colour for a specified number of colours. The package can also be used to display colour palettes in the plot window, with or without hex codes and colour labels.

This package provides tools for Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA) for intersectional inequality research. Methods are described in Merlo (2018) <doi:10.1016/j.socscimed.2017.12.026> and Evans et al. (2018) <doi:10.1016/j.socscimed.2017.11.011>. The package creates intersectional strata, fits multilevel MAIHDA models, estimates variance partition coefficients, proportional change in variance, stratum effects, and discriminatory-accuracy summaries, and provides diagnostic and presentation plots.

This package provides a fast and general method for restructuring classical longitudinal observational data into augmented transition data suitable for multi-state modeling with the msm package. Works with any longitudinal data where subjects accumulate repeated observations with start and end times and an optional terminal outcome. Methods are described in Grossetti, Ieva and Paganoni (2018) <doi:10.1007/s10729-017-9400-z>.

This package provides functions for obtaining estimates of the parameter of the niche preemption model (also known as the geometric series), in particular a maximum likelihood estimator (Graffelman, 2021) <doi:10.1101/2021.01.27.428381>. The niche preemption model is a widely used model in ecology and biodiversity studies.

It offers random-forest-based functions to impute clustered incomplete data. The package is tailored for but not limited to imputing multitissue expression data, in which a gene's expression is measured on the collected tissues of an individual but missing on the uncollected tissues.

Fit growth curves to various known microbial growth models automatically to estimate growth parameters. Growth curves can be plotted with their uncertainty band. Growth models are: modified Gompertz model (Zwietering et al. (1990) <doi:10.1128/aem.56.6.1875-1881.1990>), Baranyi model (Baranyi and Roberts (1994) <doi:10.1016/0168-1605%2894%2990157-0>), Rosso model (Rosso et al. (1993) <doi:10.1006/jtbi.1993.1099>) and linear model (Dantigny (2005) <doi:10.1016/j.ijfoodmicro.2004.10.013>).

This package provides a graphical user interface (GUI) for performing Multidimensional Scaling applications and interactively analysing the results all within the GUI environment. The MDS-GUI provides means of performing Classical Scaling, Least Squares Scaling, Metric SMACOF, Non-Metric SMACOF, Kruskal's Analysis and Sammon Mapping with animated optimisation.

Supports matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry workflows from raw Bruker spectra to cohort-level peak matrices. Provides spectrum loading, Savitzky-Golay smoothing, baseline correction (SNIP and TopHat), Gaussian kernel-regression-based peak detection including shoulder peaks, peak-quality assessment, filtering, and cohort feature analysis. Computationally intensive routines are implemented in C++ using Rcpp'. The implemented signal-processing methods include those described by Savitzky and Golay (1964) <doi:10.1021/ac60214a047>, Ryan et al. (1988) <doi:10.1016/0168-583X(88)90063-8>, Stanford, Bagley and Solomon (2016) <doi:10.1186/s12953-016-0107-8>, and Nadaraya-Watson kernel regression (Nadaraya (1964) <doi:10.1137/1109020>; Watson (1964) <https://www.jstor.org/stable/25049340>).

Complements the book "Using R for Modelling and Quantitative Methods in Fisheries" ISBN 9780367469894, published in 2021 by Chapman & Hall in their "Using R series". There are numerous functions and data-sets that are used in the book's many practical examples.

The utility of this package is in simulating mixtures of Gaussian distributions with different levels of overlap between mixture components. Pairwise overlap, defined as a sum of two misclassification probabilities, measures the degree of interaction between components and can be readily employed to control the clustering complexity of datasets simulated from mixtures. These datasets can then be used for systematic performance investigation of clustering and finite mixture modeling algorithms. Among other capabilities of MixSim', there are computing the exact overlap for Gaussian mixtures, simulating Gaussian and non-Gaussian data, simulating outliers and noise variables, calculating various measures of agreement between two partitionings, and constructing parallel distribution plots for the graphical display of finite mixture models.