Understanding morphological variation is an important task in many applications. Recent studies in computational biology have focused on developing computational tools for the task of sub-image selection which aims at identifying structural features that best describe the variation between classes of shapes. A major part in assessing the utility of these approaches is to demonstrate their performance on both simulated and real datasets. However, when creating a model for shape statistics, real data can be difficult to access and the sample sizes for these data are often small due to them being expensive to collect. Meanwhile, the landscape of current shape simulation methods has been mostly limited to approaches that use black-box inference---making it difficult to systematically assess the power and calibration of sub-image models. In this R package, we introduce the alpha-shape sampler: a probabilistic framework for simulating realistic 2D and 3D shapes based on probability distributions which can be learned from real data or explicitly stated by the user. The ashapesampler package supports two mechanisms for sampling shapes in two and three dimensions. The first, empirically sampling based on an existing data set, was highlighted in the original main text of the paper. The second, probabilistic sampling from a known distribution, is the computational implementation of the theory derived in that paper. Work based on Winn-Nunez et al. (2024) <doi:10.1101/2024.01.09.574919>.

This package contains functions carrying out adaptive procedures using mixed scaling approach to establish bioequivalence for in-vitro permeation test (IVPT) data. Currently, the package provides procedures based on parallel replicate design and balanced data, according to the U.S. Food and Drug Administration's "Draft Guidance on Acyclovir" <https://www.accessdata.fda.gov/drugsatfda_docs/psg/Acyclovir_topical%20cream_RLD%2021478_RV12-16.pdf>. Potvin et al. (2008) <doi:10.1002/pst.294> provides the basis for our adaptive design (see Method B). For a comprehensive overview of the method, refer to Lim et al. (2023) <doi:10.1002/pst.2333>. This package reflects the views of the authors and should not be construed to represent the views or policies of the U.S. Food and Drug Administration.

Package to query the Twitter Academic Research Product Track, providing access to full-archive search and other v2 API endpoints. Functions are written with academic research in mind. They provide flexibility in how the user wishes to store collected data, and encourage regular storage of data to mitigate loss when collecting large volumes of tweets. They also provide workarounds to manage and reshape the format in which data is provided on the client side.

This package provides a set of tests for compositional pathologies. Tests for coherence of correlations with aIc.coherent() as suggested by (Erb et al. (2020) <doi:10.1016/j.acags.2020.100026>), compositional dominance of distance with aIc.dominant(), compositional perturbation invariance with aIc.perturb() as suggested by (Aitchison (1992) <doi:10.1007/BF00891269>) and singularity of the covariation matrix with aIc.singular(). Currently tests five data transformations: prop, clr, TMM, TMMwsp, and RLE from the R packages ALDEx2', edgeR and DESeq2 (Fernandes et al (2014) <doi:10.1186/2049-2618-2-15>, Anders et al. (2013)<doi:10.1038/nprot.2013.099>).

An unsupervised fully-automated pipeline for transcriptome analysis or a supervised option to identify characteristic genes from predefined subclasses. We rely on the pamr <http://www.bioconductor.org/packages//2.7/bioc/html/pamr.html> clustering algorithm to cluster the Data and then draw a heatmap of the clusters with the most significant genes and the least significant genes according to the pamr algorithm. This way we get easy to grasp heatmaps that show us for each cluster which are the clusters most defining genes.

Make the compiled Java modules of the Amazon Web Services ('AWS') SDK available to be used in downstream R packages interacting with AWS'. See <https://aws.amazon.com/sdk-for-java> for more information on the AWS SDK for Java.

Another implementation of object-orientation in R. It provides syntactic sugar for the S4 class system and two alternative new implementations. One is an experimental version built around S4 and the other one makes it more convenient to work with lists as objects.

This package provides an htmlwidgets interface to apexcharts.js'. Apexcharts is a modern JavaScript charting library to build interactive charts and visualizations with simple API. Apexcharts examples and documentation are available here: <https://apexcharts.com/>.

Presents a series of molecular and genetic routines in the R environment with the aim of assisting in analytical pipelines before and after the use of asreml or another library to perform analyses such as Genomic Selection or Genome-Wide Association Analyses. Methods and examples are described in Gezan, Oliveira, Galli, and Murray (2022) <https://asreml.kb.vsni.co.uk/wp-content/uploads/sites/3/ASRgenomics_Manual.pdf>.

This package provides tools for the quantitative analysis of axon integrity in microscopy images. It implements image pre-processing, adaptive thresholding, feature extraction, and support vector machine-based classification to compute indices such as the Axon Integrity Index (AII) and Degeneration Index (DI). The package is designed for reproducible and automated analysis in neuroscience research.

It performs All-Resolutions Inference (ARI) on functional Magnetic Resonance Image (fMRI) data. As a main feature, it estimates lower bounds for the proportion of active voxels in a set of clusters as, for example, given by a cluster-wise analysis. The method is described in Rosenblatt, Finos, Weeda, Solari, Goeman (2018) <doi:10.1016/j.neuroimage.2018.07.060>.

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.

Estimate diagnosis performance (Sensitivity, Specificity, Positive predictive value, Negative predicted value) of a diagnostic test where can not measure the golden standard but can estimate it using the attributable fraction.

Annuity Random Interest Rates proposes different techniques for the approximation of the present and final value of a unitary annuity-due or annuity-immediate considering interest rate as a random variable. Cruz Rambaud et al. (2017) <doi:10.1007/978-3-319-54819-7_16>. Cruz Rambaud et al. (2015) <doi:10.23755/rm.v28i1.25>.

The archdata package provides several types of data that are typically used in archaeological research. It provides all of the data sets used in "Quantitative Methods in Archaeology Using R" by David L Carlson, one of the Cambridge Manuals in Archaeology.

This package implements Collective And Point Anomaly (CAPA) Fisch, Eckley, and Fearnhead (2022) <doi:10.1002/sam.11586>, Multi-Variate Collective And Point Anomaly (MVCAPA) Fisch, Eckley, and Fearnhead (2021) <doi:10.1080/10618600.2021.1987257>, Proportion Adaptive Segment Selection (PASS) Jeng, Cai, and Li (2012) <doi:10.1093/biomet/ass059>, and Bayesian Abnormal Region Detector (BARD) Bardwell and Fearnhead (2015) <doi:10.1214/16-BA998>. These methods are for the detection of anomalies in time series data. Further information regarding the use of this package along with detailed examples can be found in Fisch, Grose, Eckley, Fearnhead, and Bardwell (2024) <doi:10.18637/jss.v110.i01>.

Graphical functionalities for the representation of multivariate data. It is a complete re-implementation of the functions available in the ade4 package.

Extremely efficient procedures for fitting the entire group lasso and group elastic net regularization path for GLMs, multinomial, the Cox model and multi-task Gaussian models. Similar to the R package glmnet in scope of models, and in computational speed. This package provides R bindings to the C++ code underlying the corresponding Python package adelie'. These bindings offer a general purpose group elastic net solver, a wide range of matrix classes that can exploit special structure to allow large-scale inputs, and an assortment of generalized linear model classes for fitting various types of data. The package is an implementation of Yang, J. and Hastie, T. (2024) <doi:10.48550/arXiv.2405.08631>.

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.

Automated Characterization of Health Information at Large-Scale Longitudinal Evidence Systems. Creates a descriptive statistics summary for an Observational Medical Outcomes Partnership Common Data Model standardized data source. This package includes functions for executing summary queries on the specified data source and exporting reporting content for use across a variety of Observational Health Data Sciences and Informatics community applications.

Uncertainty quantification and inverse estimation by probabilistic generative models from the beginning of the data analysis. An example is a Fourier basis method for inverse estimation in scattering analysis of microscopy videos. It does not require specifying a certain range of Fourier bases and it substantially reduces computational cost via the generalized Schur algorithm. See the reference: Mengyang Gu, Yue He, Xubo Liu and Yimin Luo (2023), <doi:10.48550/arXiv.2309.02468>.

This package provides a collection of tools for antitrust practitioners, including the ability to calibrate different consumer demand systems and simulate the effects of mergers under different competitive regimes.

DEPRECATED. Do not start building new projects based on this package. (The (in-house) APD file format was initially developed to store Affymetrix probe-level data, e.g. normalized CEL intensities. Chip types can be added to APD file and similar to methods in the affxparser package, this package provides methods to read APDs organized by units (probesets). In addition, the probe elements can be arranged optimally such that the elements are guaranteed to be read in order when, for instance, data is read unit by unit. This speeds up the read substantially. This package is supporting the Aroma framework and should not be used elsewhere.).

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.