This package provides the functions for planning and conducting a clinical trial with adaptive sample size determination. Maximal statistical efficiency will be exploited even when dramatic or multiple adaptations are made. Such a trial consists of adaptive determination of sample size at an interim analysis and implementation of frequentist statistical test at the interim and final analysis with a prefixed significance level. The required assumptions for the stage-wise test statistics are independent and stationary increments and normality. Predetermination of adaptation rule is not required.

Loss reserving generally focuses on identifying a single model that can generate superior predictive performance. However, different loss reserving models specialise in capturing different aspects of loss data. This is recognised in practice in the sense that results from different models are often considered, and sometimes combined. For instance, actuaries may take a weighted average of the prediction outcomes from various loss reserving models, often based on subjective assessments. This package allows for the use of a systematic framework to objectively combine (i.e. ensemble) multiple stochastic loss reserving models such that the strengths offered by different models can be utilised effectively. Our framework is developed in Avanzi et al. (2023). Firstly, our criteria model combination considers the full distributional properties of the ensemble and not just the central estimate - which is of particular importance in the reserving context. Secondly, our framework is that it is tailored for the features inherent to reserving data. These include, for instance, accident, development, calendar, and claim maturity effects. Crucially, the relative importance and scarcity of data across accident periods renders the problem distinct from the traditional ensemble techniques in statistical learning. Our framework is illustrated with a complex synthetic dataset. In the results, the optimised ensemble outperforms both (i) traditional model selection strategies, and (ii) an equally weighted ensemble. In particular, the improvement occurs not only with central estimates but also relevant quantiles, such as the 75th percentile of reserves (typically of interest to both insurers and regulators). Reference: Avanzi B, Li Y, Wong B, Xian A (2023) "Ensemble distributional forecasting for insurance loss reserving" <doi:10.48550/arXiv.2206.08541>.

Manage and analyze animal movement data. The functionality of amt includes methods to calculate home ranges, track statistics (e.g. step lengths, speed, or turning angles), prepare data for fitting habitat selection analyses, and simulation of space-use from fitted step-selection functions.

This package provides access to biographical and political data about Australian federal politicians who served between 1901 and 2021. This enhances how reproducible research is that uses this data.

An interface for performing all stages of ADMIXTOOLS analyses (<https://reich.hms.harvard.edu/software>) entirely from R. Wrapper functions (D, f4, f3, etc.) completely automate the generation of intermediate configuration files, run ADMIXTOOLS programs on the command-line, and parse output files to extract values of interest. This allows users to focus on the analysis itself instead of worrying about low-level technical details. A set of complementary functions for processing and filtering of data in the EIGENSTRAT format is also provided.

Efficient algorithms <https://jmlr.org/papers/v24/21-0751.html> for computing Area Under Minimum, directional derivatives, and line search optimization of a linear model, with objective defined as either max Area Under the Curve or min Area Under Minimum.

Continuous and discrete (count or categorical) estimation of density, probability mass function (p.m.f.) and regression functions are performed using associated kernels. The cross-validation technique and the local Bayesian procedure are also implemented for bandwidth selection.

This package implements the Arellano-Bond estimation method combined with LASSO for dynamic linear panel models. See Chernozhukov et al. (2024) "Arellano-Bond LASSO Estimator for Dynamic Linear Panel Models". arXiv preprint <doi:10.48550/arXiv.2402.00584>.

Designed to help health economic modellers when building and reviewing models. The visualisation functions allow users to more easily review the network of functions in a project, and get lay summaries of them. The asserts included are intended to check for common errors, thereby freeing up time for modellers to focus on tests specific to the individual model in development or review. For more details see Smith and colleagues (2024)<doi:10.12688/wellcomeopenres.23180.1>.

In total it has 7 functions, three for calculating machine calibration, which determine application rate (L/ha), nozzle flow (L/min) and amount of product (L or kg) to be added. to the tank with each sprayer filling. Two functions for graphs of the flow distribution of the nozzles (L/min) in the application bar and, of the temporal variability of the meteorological conditions (air temperature, relative humidity of the air and wind speed). Two functions to determine the spray deposit (uL/cm2), through the methodology called spectrophotometry, with the aid of bright blue (Palladini, L.A., Raetano, C.G., Velini, E.D. (2005), <doi:10.1590/S0103-90162005000500005>) or metallic markers (Chaim, A., Castro, V.L.S.S., Correles, F.M., Galvão, J.A.H., Cabral, O.M.R., Nicolella, G. (1999), <doi:10.1590/S0100-204X1999000500003>). The package supports the analysis and representation of information, using a single free software that meets the most diverse areas of activity in application technology.

This package provides the infrastructure for association rule-based classification including the algorithms CBA, CMAR, CPAR, C4.5, FOIL, PART, PRM, RCAR, and RIPPER to build associative classifiers. Hahsler et al (2019) <doi:10.32614/RJ-2019-048>.

Automated methods to assemble population PK (pharmacokinetic) and PKPD (pharmacodynamic) datasets for analysis in NONMEM (non-linear mixed effects modeling) by Bauer (2019) <doi:10.1002/psp4.12404>. The package includes functions to build datasets from SDTM (study data tabulation module) <https://www.cdisc.org/standards/foundational/sdtm>, ADaM (analysis dataset module) <https://www.cdisc.org/standards/foundational/adam>, or other dataset formats. The package will combine population datasets, add covariates, and create documentation to support regulatory submission and internal communication.

Manage storage in Microsoft's Azure cloud: <https://azure.microsoft.com/en-us/products/category/storage/>. On the admin side, AzureStor includes features to create, modify and delete storage accounts. On the client side, it includes an interface to blob storage, file storage, and Azure Data Lake Storage Gen2': upload and download files and blobs; list containers and files/blobs; create containers; and so on. Authenticated access to storage is supported, via either a shared access key or a shared access signature (SAS). Part of the AzureR family of packages.

Routines for re-scaling isotope maps using known-origin tissue isotope data, assigning origin of unknown samples, and summarizing and assessing assignment results. Methods are adapted from Wunder (2010, in ISBN:9789048133536) and Vander Zanden, H. B. et al. (2014) <doi:10.1111/2041-210X.12229> as described in Ma, C. et al. (2020) <doi:10.1111/2041-210X.13426>.

It implemented Age-Period-Interaction Model (APC-I Model) proposed in the paper of Liying Luo and James S. Hodges in 2019. A new age-period-cohort model for describing and investigating inter-cohort differences and life course dynamics.

This package provides a collection of lightweight functions that can be used to determine the computing environment in which your code is running. This includes operating systems, continuous integration (CI) environments, containers, and more.

Interface package for sala', the spatial network analysis library from the depthmapX software application. The R parts of the code are based on the rdepthmap package. Allows for the analysis of urban and building-scale networks and provides metrics and methods usually found within the Space Syntax domain. Methods in this package are described by K. Al-Sayed, A. Turner, B. Hillier, S. Iida and A. Penn (2014) "Space Syntax methodology", and also by A. Turner (2004) <https://discovery.ucl.ac.uk/id/eprint/2651> "Depthmap 4: a researcher's handbook".

Extraction of subsequences into FASTA files from GenBank annotations where gene names may vary among accessions. Borstein & O'Meara (2018) <doi:10.7717/peerj.5179>.

This package provides functions for analysis of data generated from experiments in augmented randomised complete block design according to Federer, W.T. (1961) <doi:10.2307/2527837>. Computes analysis of variance, adjusted means, descriptive statistics, genetic variability statistics etc. Further includes data visualization and report generation functions.

This package contains functions from: Aho, K. (2014) Foundational and Applied Statistics for Biologists using R. CRC/Taylor and Francis, Boca Raton, FL, ISBN: 978-1-4398-7338-0.

Designed for optimal use in performing fast, accurate walking strides segmentation from high-density data collected from a wearable accelerometer worn during continuous walking activity.

An application for analysis of Adverse Events, as described in Chen, et al., (2023) <doi:10.3390/cancers15092521>. The required data for the application includes demographics, follow up, adverse event, drug administration and optional tumor measurement data. The app can produce swimmers plots of adverse events, Kaplan-Meier plots and Cox Proportional Hazards model results for the association of adverse event biomarkers and overall survival and progression free survival. The adverse event biomarkers include occurrence of grade 3, low grade (1-2), and treatment related adverse events. Plots and tables of results are downloadable.

This package provides functions for Accurate and Speedy linkage map construction, manipulation and diagnosis of Doubled Haploid, Backcross and Recombinant Inbred R/qtl objects. This includes extremely fast linkage map clustering and optimal marker ordering using MSTmap (see Wu et al.,2008).

Estimating and analyzing auto regressive integrated moving average (ARIMA) models. The primary function in this package is arima(), which fits an ARIMA model to univariate time series data using a random restart algorithm. This approach frequently leads to models that have model likelihood greater than or equal to that of the likelihood obtained by fitting the same model using the arima() function from the stats package. This package enables proper optimization of model likelihoods, which is a necessary condition for performing likelihood ratio tests. This package relies heavily on the source code of the arima() function of the stats package. For more information, please see Jesse Wheeler and Edward L. Ionides (2025) <doi:10.1371/journal.pcbi.1012032>.