Data on Asylum and Resettlement for the UK, provided by the Home Office <https://www.gov.uk/government/statistical-data-sets/immigration-system-statistics-data-tables>.

Build and train a variational autoencoder (VAE) for mixed-type tabular data (continuous, binary, categorical). Models are implemented using TensorFlow and Keras via the reticulate interface, enabling reproducible VAE training for heterogeneous tabular datasets.

This package provides easy access to the AviList Global Avian Checklist, the first unified global bird taxonomy that harmonizes previous differences between International Ornithological Committee ('IOC'), Clements', and BirdLife checklists. This package contains the complete AviList dataset as R data objects ready for ornithological research and analysis. For more details see AviList Core Team (2025) <doi:10.2173/avilist.v2025>.

This package provides tools for simulating data generated by direct observation recording. Behavior streams are simulated based on an alternating renewal process, given specified distributions of event durations and interim times. Different procedures for recording data can then be applied to the simulated behavior streams. Functions are provided for the following recording methods: continuous duration recording, event counting, momentary time sampling, partial interval recording, whole interval recording, and augmented interval recording.

Implementation of the Artificial Hydrocarbon Networks for data modeling.

This package provides functions to perform statistical inference in the balanced one-way ANOVA model with a random factor: confidence intervals, prediction interval, and Weerahandi generalized pivotal quantities. References: Burdick & Graybill (1992, ISBN-13: 978-0824786441); Weerahandi (1995) <doi:10.1007/978-1-4612-0825-9>; Lin & Liao (2008) <doi:10.1016/j.jspi.2008.01.001>.

Multivariate tools to analyze comparative data, i.e. a phylogeny and some traits measured for each taxa. The package contains functions to represent comparative data, compute phylogenetic proximities, perform multivariate analysis with phylogenetic constraints and test for the presence of phylogenetic autocorrelation. The package is described in Jombart et al (2010) <doi:10.1093/bioinformatics/btq292>.

Accessible wrappers for popular shiny UI components, enforcing ARIA attributes and structural requirements in line with BITV 2.0 (Barrierefreie-Informationstechnik-Verordnung) and WCAG 2.1 AA. Covers action buttons, text and select inputs, fluid page layouts with HTML landmarks and skip links, DT data tables, and bar and line graphs from ggplot2'. Components validate label presence, expose keyboard-accessible ARIA states, and provide a high-contrast toggle. This package was developed by d-fine GmbH on behalf of the German Federal Ministry of Research, Technology and Space (BMFTR).

This package implements the differential equations associated to different versions of Allometric Trophic Models (ATN) to estimate the temporal dynamics of species biomasses in food webs. It offers several features to generate synthetic food webs and to parametrise models as well as a wrapper to the ODE solver deSolve.

Routines for astrochronologic testing, astronomical time scale construction, and time series analysis <doi:10.1016/j.earscirev.2018.11.015>. Also included are a range of statistical analysis and modeling routines that are relevant to time scale development and paleoclimate analysis.

PCA done by eigenvalue decomposition of a data correlation matrix, here it automatically determines the number of factors by eigenvalue greater than 1 and it gives the uncorrelated variables based on the rotated component scores, Such that in each principal component variable which has the high variance are selected. It will be useful for non-statisticians in selection of variables. For more information, see the <http://www.ijcem.org/papers032013/ijcem_032013_06.pdf> web page.

Sets the alpha level for coefficients in a regression model as a decreasing function of the sample size through the use of Jeffreys Approximate Bayes factor. You tell alphaN() your sample size, and it tells you to which value you must lower alpha to avoid Lindley's Paradox. For details, see Wulff and Taylor (2024) <doi:10.1177/14761270231214429>.

This package implements the Adaptive Multiple Importance Sampling (AMIS) algorithm, as described by Retkute et al. (2021, <doi:10.1214/21-AOAS1486>), to estimate key epidemiological parameters by combining outputs from a geostatistical model of infectious diseases (such as prevalence, incidence, or relative risk) with a disease transmission model. Utilising the resulting posterior distributions, the package enables forward projections at the local level.

Analyses of frequencies can be performed using an alternative test based on the G statistic. The test has similar type-I error rates and power as the chi-square test. However, it is based on a total statistic that can be decomposed in an additive fashion into interaction effects, main effects, simple effects, contrast effects, etc., mimicking precisely the logic of ANOVA. We call this set of tools ANOFA (Analysis of Frequency data) to highlight its similarities with ANOVA. This framework also renders plots of frequencies along with confidence intervals. Finally, effect sizes and planning statistical power are easily done under this framework. The ANOFA is a tool that assesses the significance of effects instead of the significance of parameters; as such, it is more intuitive to most researchers than alternative approaches based on generalized linear models. See Laurencelle and Cousineau (2023) <doi:10.20982/tqmp.19.2.p173>.

This package provides a collection of methods for both the rank-based estimates and least-square estimates to the Accelerated Failure Time (AFT) model. For rank-based estimation, it provides approaches that include the computationally efficient Gehan's weight and the general's weight such as the logrank weight. Details of the rank-based estimation can be found in Chiou et al. (2014) <doi:10.1007/s11222-013-9388-2> and Chiou et al. (2015) <doi:10.1002/sim.6415>. For the least-square estimation, the estimating equation is solved with generalized estimating equations (GEE). Moreover, in multivariate cases, the dependence working correlation structure can be specified in GEE's setting. Details on the least-squares estimation can be found in Chiou et al. (2014) <doi:10.1007/s10985-014-9292-x>.

This package provides tools for geometric morphometric analysis. The package includes tools of virtual anthropology to align two not articulated parts belonging to the same specimen, to build virtual cavities as endocast (Profico et al, 2021 <doi:10.1002/ajpa.24340>).

This package contains functions to implement automated covariate selection using methods described in the high-dimensional propensity score (HDPS) algorithm by Schneeweiss et.al. Covariate adjustment in real-world-observational-data (RWD) is important for for estimating adjusted outcomes and this can be done by using methods such as, but not limited to, propensity score matching, propensity score weighting and regression analysis. While these methods strive to statistically adjust for confounding, the major challenge is in selecting the potential covariates that can bias the outcomes comparison estimates in observational RWD (Real-World-Data). This is where the utility of automated covariate selection comes in. The functions in this package help to implement the three major steps of automated covariate selection as described by Schneeweiss et. al elsewhere. These three functions, in order of the steps required to execute automated covariate selection are, get_candidate_covariates(), get_recurrence_covariates() and get_prioritised_covariates(). In addition to these functions, a sample real-world-data from publicly available de-identified medical claims data is also available for running examples and also for further exploration. The original article where the algorithm is described by Schneeweiss et.al. (2009) <doi:10.1097/EDE.0b013e3181a663cc> .

The goal of amp.sim is to transform NONMEM models into R syntax so they can be used for simulations using the deSolve', nlmixr2 or mrgsolve package. Additionally, functionality is included to aid simulations performed directly in NONMEM and to automatically create shiny apps for simulation models.

Data sets and examples from National Health and Nutritional Examination Survey (NHANES).

This package performs the analysis of completely randomized experimental designs (CRD), randomized blocks (RBD) and Latin square (LSD), experiments in double and triple factorial scheme (in CRD and RBD), experiments in subdivided plot scheme (in CRD and RBD), subdivided and joint analysis of experiments in CRD and RBD, linear regression analysis, test for two samples. The package performs analysis of variance, ANOVA assumptions and multiple comparison test of means or regression, according to Pimentel-Gomes (2009, ISBN: 978-85-7133-055-9), nonparametric test (Conover, 1999, ISBN: 0471160687), test for two samples, joint analysis of experiments according to Ferreira (2018, ISBN: 978-85-7269-566-4) and generalized linear model (glm) for binomial and Poisson family in CRD and RBD (Carvalho, FJ (2019), <doi:10.14393/ufu.te.2019.1244>). It can also be used to obtain descriptive measures and graphics, in addition to correlations and creative graphics used in agricultural sciences (Agronomy, Zootechnics, Food Science and related areas). Shimizu, G. D., Marubayashi, R. Y. P., Goncalves, L. S. A. (2025) <doi:10.4025/actasciagron.v47i1.73889>.

Fits random effects models for multi-level/high-cardinality factors using credibility theory (Buhlmann-Straub for single-level, Jewell for hierarchical structures), GLM extensions following Ohlsson (2008) <doi:10.1080/03461230701878612>, or Tweedie generalized linear mixed models. Provides functions for model fitting, visualization, and prediction. See Campo, B.D.C. and Antonio, K. (2023) <doi:10.1080/03461238.2022.2161413>.

This package provides a routine to partial out factors with many levels during the optimization of the log-likelihood function of the corresponding generalized linear model (glm). The package is based on the algorithm described in Stammann (2018) <doi:10.48550/arXiv.1707.01815> and is restricted to glm's that are based on maximum likelihood estimation and nonlinear. It also offers an efficient algorithm to recover estimates of the fixed effects in a post-estimation routine and includes robust and multi-way clustered standard errors. Further the package provides analytical bias corrections for binary choice models derived by Fernandez-Val and Weidner (2016) <doi:10.1016/j.jeconom.2015.12.014> and Hinz, Stammann, and Wanner (2020) <doi:10.48550/arXiv.2004.12655>.

This package provides a function to calculate the concentration of un-ionized ammonia in the total ammonia in aqueous solution using the pH and temperature values.

Adjusts output of cranlogs package to account for CRAN'-wide daily automated downloads and re-downloads caused by package updates.