Client for AWS Transcribe <https://aws.amazon.com/documentation/transcribe>, a cloud transcription service that can convert an audio media file in English and other languages into a text transcript.

The normal process of creating clinical study slides is that a statistician manually type in the numbers from outputs and a separate statistician to double check the typed in numbers. This process is time consuming, resource intensive, and error prone. Automatic slide generation is a solution to address these issues. It reduces the amount of work and the required time when creating slides, and reduces the risk of errors from manually typing or copying numbers from the output to slides. It also helps users to avoid unnecessary stress when creating large amounts of slide decks in a short time window.

Calculates concentration and dispersion in ordered rating scales. It implements various measures of concentration and dispersion to describe what researchers variably call agreement, concentration, consensus, dispersion, or polarization among respondents in ordered data. It also implements other related measures to classify distributions. In addition to a generic city-block based concentration measure and a generic dispersion measure, the package implements various measures, including van der Eijk's (2001) <DOI: 10.1023/A:1010374114305> measure of agreement A, measures of concentration by Leik, Tatsle and Wierman, Blair and Lacy, Kvalseth, Berry and Mielke, Reardon, and Garcia-Montalvo and Reynal-Querol. Furthermore, the package provides an implementation of Galtungs AJUS-system to classify distributions, as well as a function to identify the position of multiple modes.

This package provides new_partialised() and new_composed(), which extend partial() and compose() functions of purrr to make it easier to extract and replace arguments and functions. It also has additional adverbial functions.

This package provides a dynamic time warping (DTW) algorithm for stratigraphic alignment, translated into R from the original published MATLAB code by Hay et al. (2019) <doi:10.1130/G46019.1>. The DTW algorithm incorporates two geologically relevant parameters (g and edge) for augmenting the typical DTW cost matrix, allowing for a range of sedimentologic and chronologic conditions to be explored, as well as the generation of an alignment library (as opposed to a single alignment solution). The g parameter relates to the relative sediment accumulation rate between the two time series records, while the edge parameter relates to the amount of total shared time between the records. Note that this algorithm is used for all DTW alignments in the Align Shiny application, detailed in Hagen et al. (in review).

This package provides a simple interface to the instance metadata for a virtual machine running in Microsoft's Azure cloud. This provides information about the VM's configuration, such as its processors, memory, networking, storage, and so on. Part of the AzureR family of packages.

This package performs statistical testing to compare predictive models based on multiple observations of the A statistic (also known as Area Under the Receiver Operating Characteristic Curve, or AUC). Specifically, it implements a testing method based on the equivalence between the A statistic and the Wilcoxon statistic. For more information, see Hanley and McNeil (1982) <doi:10.1148/radiology.143.1.7063747>.

Construct language-aware lists. Make "and"-separated and "or"-separated lists that automatically conform to the user's language settings.

This package provides a few functions and several data set for the Springer book Applied Predictive Modeling'.

Fits Modern Analogue Technique and Weighted Averaging transfer function models for prediction of environmental data from species data, and related methods used in palaeoecology.

This package implements the Age Band Decomposition (ABD) method for standardizing tree ring width data while preserving both low and high frequency variability. Unlike traditional detrending approaches that can distort long term growth trends, ABD decomposes ring width series into multiple age classes, detrends each class separately, and then recombines them to create standardized chronologies. This approach improves the detection of growth signals linked to past climatic and environmental factors, making it particularly valuable for dendroecological and dendroclimatological studies. The package provides functions to perform ABD-based standardization, compare results with other common methods (e.g., BAI, C method, RCS), and facilitate the interpretation of growth patterns under current and future climate variability.

Browse through a continuously updated list of existing RStudio addins and install/uninstall their corresponding packages.

Manage dependencies during package development. This can retrieve all dependencies that are used in ".R" files in the "R/" directory, in ".Rmd" files in "vignettes/" directory and in roxygen2 documentation of functions. There is a function to update the "DESCRIPTION" file of your package with CRAN packages or any other remote package. All functions to retrieve dependencies of ".R" scripts and ".Rmd" or ".qmd" files can be used independently of a package development.

Add-on to the airGR package which provides the tools to assimilate observed discharges in daily GR hydrological models. The package consists in two functions allowing to perform the assimilation of observed discharges via the Ensemble Kalman filter or the Particle filter as described in Piazzi et al. (2021) <doi:10.1029/2020WR028390>.

This package provides a free software for a fast and easy analysis of 1:1 molecular interaction studies. This package is suitable for a high-throughput data analysis. Both the online app and the package are completely open source. You provide a table of sensogram, tell anabel which method to use, and it takes care of all fitting details. The first two releases of anabel were created and implemented as in (<doi:10.1177/1177932218821383>, <doi:10.1093/database/baz101>).

Describes a series first. After that does time series analysis using one hybrid model and two specially structured Machine Learning (ML) (Artificial Neural Network or ANN and Support Vector Regression or SVR) models. More information can be obtained from Paul and Garai (2022) <doi:10.1007/s41096-022-00128-3>.

This package provides automated visual inference of residual plots using computer vision models, facilitating diagnostic checks for classical normal linear regression models.

Predicts antimicrobial peptides using random forests trained on the n-gram encoded peptides. The implemented algorithm can be accessed from both the command line and shiny-based GUI. The AmpGram model is too large for CRAN and it has to be downloaded separately from the repository: <https://github.com/michbur/AmpGramModel>.

Adversarial random forests (ARFs) recursively partition data into fully factorized leaves, where features are jointly independent. The procedure is iterative, with alternating rounds of generation and discrimination. Data becomes increasingly realistic at each round, until original and synthetic samples can no longer be reliably distinguished. This is useful for several unsupervised learning tasks, such as density estimation and data synthesis. Methods for both are implemented in this package. ARFs naturally handle unstructured data with mixed continuous and categorical covariates. They inherit many of the benefits of random forests, including speed, flexibility, and solid performance with default parameters. For details, see Watson et al. (2023) <https://proceedings.mlr.press/v206/watson23a.html>.

This package provides the conditional Nelson-Aalen and Aalen-Johansen estimators. The methods are based on Bladt & Furrer (2023), in preparation.

This package provides a novel parametrization of log transformation and a shift parameter to automate the transformation process are proposed in R package AutoTransQF based on Feng et al. (2016). Please read Feng et al. (2016) <doi:10.1002/sta4.104> for more details of the method.

This package provides tools for downloading hourly averages, daily maximums and minimums from each of the pollution, wind, and temperature measuring stations or geographic zones in the Mexico City metro area. The package also includes the locations of each of the stations and zones. See <http://aire.cdmx.gob.mx/> for more information.

Convenience functions for aggregating a data frame or data table. Currently mean, sum and variance are supported. For Date variables, the recency and duration are supported. There is also support for dummy variables in predictive contexts. Code has been completely re-written in data.table for computational speed.

Anytime-valid inference for linear models, namely, sequential t-tests, sequential F-tests, and confidence sequences with time-uniform Type-I error and coverage guarantees. This allows hypotheses to be continuously tested without sacrificing false positive guarantees. It is based on the methods documented in Lindon et al. (2022) <doi:10.48550/arXiv.2210.08589>.