Libao is a cross-platform audio library that allows programs to output audio using a simple API on a wide variety of platforms. It currently supports:

1. Null output (handy for testing without a sound device),

2. WAV files,

3. AU files,

4. RAW files,

5. OSS (Open Sound System, used on Linux and FreeBSD),

6. ALSA (Advanced Linux Sound Architecture),

7. aRts (Analog RealTime Synth, used by KDE),

8. PulseAudio (next generation GNOME sound server),

9. esd (EsounD or Enlightened Sound Daemon),

10. Mac OS X,

11. Windows (98 and later),

12. AIX,

13. Sun/NetBSD/OpenBSD,

14. IRIX,

15. NAS (Network Audio Server),

16. RoarAudio (Modern, multi-OS, networked Sound System),

17. OpenBSD's sndio.

An iterative process that optimizes a function by alternately performing restricted optimization over parameter subsets. Instead of joint optimization, it breaks the optimization problem down into simpler sub-problems. This approach can make optimization feasible when joint optimization is too difficult.

Trigger animation effects on scroll on any HTML element of shiny and rmarkdown', such as any text or plot, thanks to the AOS Animate On Scroll jQuery library.

This package provides a set of functions to analyze overdispersed counts or proportions. Most of the methods are already available elsewhere but are scattered in different packages. The proposed functions should be considered as complements to more sophisticated methods such as generalized estimating equations (GEE) or generalized linear mixed effect models (GLMM).

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 functions to analyse overdispersed counts or proportions. These functions should be considered as complements to more sophisticated methods such as generalized estimating equations (GEE) or generalized linear mixed effect models (GLMM). aods3 is an S3 re-implementation of the deprecated S4 package aod.

Fit, interpret, and compute predictions with oblique random forests. Includes support for partial dependence, variable importance, passing customized functions for variable importance and identification of linear combinations of features. Methods for the oblique random survival forest are described in Jaeger et al., (2023) <DOI:10.1080/10618600.2023.2231048>.

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>.

This package provides source-only AOCommon collection of functionality that is reused in several astronomical applications, such as wsclean, aoflagger, DP3 and everybeam.

To address the violation of the assumption of normally distributed variables, researchers frequently employ bootstrapping. Building upon established packages for R (Sigmann et al. (2024) <doi:10.32614/CRAN.package.afex>, Lenth (2024) <doi:10.32614/CRAN.package.emmeans>), we provide bootstrapping functions to approximate a normal distribution of the parameter estimates for between-subject, within-subject, and mixed one-way and two-way ANOVA.

It covers various approaches to analysis of variance, provides an assumption testing section in order to provide a decision diagram that allows selecting the most appropriate technique. It provides the classical analysis of variance, the nonparametric equivalent of Kruskal Wallis, and the Bayesian approach. These results are shown in an interactive shiny panel, which allows modifying the arguments of the tests, contains interactive graphics and presents automatic conclusions depending on the tests in order to contribute to the interpretation of these analyzes. AovBay uses Stan and FactorBayes for Bayesian analysis and Highcharts for interactive charts.

AOFlagger is a tool that can find and remove radio-frequency interference (RFI) in radio astronomical observations. It can make use of Lua scripts to make flagging strategies flexible, and the tools are applicable to a wide set of telescopes.

BLIS is a portable software framework for instantiating high-performance BLAS-like dense linear algebra libraries. The framework was designed to isolate essential kernels of computation that enable optimized implementations of most of its commonly used and computationally intensive operations. The optimizations are done for single and double precision routines. AMD has extensively optimized the implementation of BLIS for AMD processors.

Download data from the Access to Opportunities Project (AOP)'. The aopdata package brings annual estimates of access to employment, health, education and social assistance services by transport mode, as well as data on the spatial distribution of population, jobs, health care, schools and social assistance facilities at a fine spatial resolution for all cities included in the project. More info on the AOP website <https://www.ipea.gov.br/acessooportunidades/en/>.

AOCL-Utils is designed to be integrated into other AOCL libraries. Each project has their own mechanism to identify CPU and provide necessary features such as 'dynamic dispatch'. The main purpose of this library is to provide a centralized mechanism to update/validate and provide information to the users of this library.

It can sometimes be difficult to ascertain when some events (such as property crime) occur because the victim is not present when the crime happens. As a result, police databases often record a start (or from') date and time, and an end (or to') date and time. The time span between these date/times can be minutes, hours, or sometimes days, hence the term Aoristic'. Aoristic is one of the past tenses in Greek and represents an uncertain occurrence in time. For events with a location describes with either a latitude/longitude, or X,Y coordinate pair, and a start and end date/time, this package generates an aoristic data frame with aoristic weighted probability values for each hour of the week, for each observation. The coordinates are not necessary for the program to calculate aoristic weights; however, they are part of this package because a spatial component has been integral to aoristic analysis from the start. Dummy coordinates can be introduced if the user only has temporal data. Outputs include an aoristic data frame, as well as summary graphs and displays. For more information see: Ratcliffe, JH (2002) Aoristic signatures and the temporal analysis of high volume crime patterns, Journal of Quantitative Criminology. 18 (1): 23-43. Note: This package replaces an original aoristic package (version 0.6) by George Kikuchi that has been discontinued with his permission.

AOCL-libFLAME is a portable library for dense matrix computations, providing the complete functionality present in Linear Algebra Package (LAPACK). The library provides scientific and numerical computing communities with a modern, high-performance dense linear algebra library that is extensible, easy to use, and available under an open source license. It is a C-only implementation. Applications relying on stadard Netlib LAPACK interfaces can start taking advantage of AOCL-libFLAME with virtually no changes to their source code.

This package provides a collection of functions to construct A-optimal block designs for comparing test treatments with one or more control(s). Mainly A-optimal balanced treatment incomplete block designs, weighted A-optimal balanced treatment incomplete block designs, A-optimal group divisible treatment designs and A-optimal balanced bipartite block designs can be constructed using the package. The designs are constructed using algorithms based on linear integer programming. To the best of our knowledge, these facilities to construct A-optimal block designs for comparing test treatments with one or more controls are not available in the existing R packages. For more details on designs for tests versus control(s) comparisons, please see Hedayat, A. S. and Majumdar, D. (1984) <doi:10.1080/00401706.1984.10487989> A-Optimal Incomplete Block Designs for Control-Test Treatment Comparisons, Technometrics, 26, 363-370 and Mandal, B. N. , Gupta, V. K., Parsad, Rajender. (2017) <doi:10.1080/03610926.2015.1071394> Balanced treatment incomplete block designs through integer programming. Communications in Statistics - Theory and Methods 46(8), 3728-3737.

This package provides FFI bindings to aom.

BLIS is a portable software framework for instantiating high-performance BLAS-like dense linear algebra libraries. The framework was designed to isolate essential kernels of computation that enable optimized implementations of most of its commonly used and computationally intensive operations. The optimizations are done for single and double precision routines. AMD has extensively optimized the implementation of BLIS for AMD processors.

The package provides a class for typesetting articles for the Annals of Mathematics.

AOCL-ScaLAPACK is a library of high-performance linear algebra routines for parallel distributed memory machines. It depends on external libraries including BLAS and LAPACK for Linear Algebra computations. AMD’s optimized version of ScaLAPACK (AOCL-ScaLAPACK) enables using the BLIS and libFLAME libraries with optimized dense-matrix functions and solvers for AMD processors

BLIS is a portable software framework for instantiating high-performance BLAS-like dense linear algebra libraries. The framework was designed to isolate essential kernels of computation that enable optimized implementations of most of its commonly used and computationally intensive operations. The optimizations are done for single and double precision routines. AMD has extensively optimized the implementation of BLIS for AMD processors.

This package provides functions for processing and analyzing survey data from the All of Us Social Determinants of Health (AOUSDOH) program, including tools for calculating health and well-being scores, recoding variables, and simplifying survey data analysis. For more details see - Koleck TA, Dreisbach C, Zhang C, Grayson S, Lor M, Deng Z, Conway A, Higgins PDR, Bakken S (2024) <doi:10.1093/jamia/ocae214>.