Showing 87 of total 87 results (show query)

langenbergstefan

sound:A Sound Interface for R

Basic functions for dealing with wav files and sound samples.

Maintained by Stefan Langenberg. Last updated 1 years ago.

67.2 match 3.79 score 124 scripts

bczernecki

thunder:Computation and Visualisation of Atmospheric Convective Parameters

Allow to compute and visualise convective parameters commonly used in the operational prediction of severe convective storms. Core algorithm is based on a highly optimized 'C++' code linked into 'R' via 'Rcpp'. Highly efficient engine allows to derive thermodynamic and kinematic parameters from large numerical datasets such as reanalyses or operational Numerical Weather Prediction models in a reasonable amount of time. Package has been developed since 2017 by research meteorologists specializing in severe thunderstorms. The most relevant methods used in the package based on the following publications Stipanuk (1973) <https://apps.dtic.mil/sti/pdfs/AD0769739.pdf>, McCann et al. (1994) <doi:10.1175/1520-0434(1994)009%3C0532:WNIFFM%3E2.0.CO;2>, Bunkers et al. (2000) <doi:10.1175/1520-0434(2000)015%3C0061:PSMUAN%3E2.0.CO;2>, Corfidi et al. (2003) <doi:10.1175/1520-0434(2003)018%3C0997:CPAMPF%3E2.0.CO;2>, Showalter (1953) <doi:10.1175/1520-0477-34.6.250>, Coffer et al. (2019) <doi:10.1175/WAF-D-19-0115.1>, Gropp and Davenport (2019) <doi:10.1175/WAF-D-17-0150.1>, Czernecki et al. (2019) <doi:10.1016/j.atmosres.2019.05.010>, Taszarek et al. (2020) <doi:10.1175/JCLI-D-20-0346.1>, Sherburn and Parker (2014) <doi:10.1175/WAF-D-13-00041.1>, Romanic et al. (2022) <doi:10.1016/j.wace.2022.100474>.

Maintained by Bartosz Czernecki. Last updated 12 months ago.

capecinconvective-parametersdownload-soundinghodographrawinsondesevere-weatherthundertornadocpp

16.2 match 44 stars 6.30 score 7 scripts

marce10

NatureSounds:Animal Sounds for Bioacustic Analysis

Collection of example animal sounds for bioacoustic analysis.

Maintained by Marcelo Araya-Salas. Last updated 2 years ago.

bioacoustic-analysissound

16.5 match 2 stars 5.52 score 22 scripts 5 dependents

ljvillanueva

soundecology:Soundscape Ecology

Functions to calculate indices for soundscape ecology and other ecology research that uses audio recordings.

Maintained by Luis J. Villanueva-Rivera. Last updated 6 years ago.

7.9 match 24 stars 5.53 score 95 scripts

marce10

dynaSpec:Dynamic Spectrogram Visualizations

A set of tools to generate dynamic spectrogram visualizations in video format.

Maintained by Marcelo Araya-Salas. Last updated 16 days ago.

animal-soundsbioacousticsspectrogram

7.5 match 23 stars 5.50 score 34 scripts

rsund

survo.audio:Sound support for Survo R

Provides sound-API for Survo R.

Maintained by Reijo Sund. Last updated 10 months ago.

7.7 match 3 stars 2.48 score

ljvillanueva

pumilioR:Pumilio in R

R package to query and get data out of a Pumilio sound archive system (http://ljvillanueva.github.io/pumilio/).

Maintained by Luis J. Villanueva-Rivera. Last updated 11 years ago.

4.4 match 2 stars 4.00 score 7 scripts

framverse

framrsquared:FRAM Database Interface

A convenient tool for interfacing with FRAM access databases in R environments.

Maintained by Ty Garber. Last updated 2 months ago.

1.6 match 6 stars 5.06 score 9 scripts

coolbutuseless

carelesswhisper:Automatic Speech Recognition using Whisper.cpp

Wrapper for whisper.cpp to perform automatic speech recognition.

Maintained by mikefc. Last updated 2 years ago.

cpp

1.7 match 57 stars 3.45 score 4 scripts

jrosell

jrrosell:Personal R package for Jordi Rosell

Useful functions for personal usage.

Maintained by Jordi Rosell. Last updated 3 months ago.

1.8 match 2 stars 3.08 score 7 scripts

matthewgerber

SensusR:Sensus Analytics

Provides access and analytic functions for Sensus data.

Maintained by Matthew S. Gerber. Last updated 6 years ago.

2.0 match 1.48 score 30 scripts

bioc

MatrixQCvis:Shiny-based interactive data-quality exploration for omics data

Data quality assessment is an integral part of preparatory data analysis to ensure sound biological information retrieval. We present here the MatrixQCvis package, which provides shiny-based interactive visualization of data quality metrics at the per-sample and per-feature level. It is broadly applicable to quantitative omics data types that come in matrix-like format (features x samples). It enables the detection of low-quality samples, drifts, outliers and batch effects in data sets. Visualizations include amongst others bar- and violin plots of the (count/intensity) values, mean vs standard deviation plots, MA plots, empirical cumulative distribution function (ECDF) plots, visualizations of the distances between samples, and multiple types of dimension reduction plots. Furthermore, MatrixQCvis allows for differential expression analysis based on the limma (moderated t-tests) and proDA (Wald tests) packages. MatrixQCvis builds upon the popular Bioconductor SummarizedExperiment S4 class and enables thus the facile integration into existing workflows. The package is especially tailored towards metabolomics and proteomics mass spectrometry data, but also allows to assess the data quality of other data types that can be represented in a SummarizedExperiment object.

Maintained by Thomas Naake. Last updated 5 months ago.

visualizationshinyappsguiqualitycontroldimensionreductionmetabolomicsproteomicstranscriptomics

0.5 match 4.74 score 4 scripts

tomaspinall

NFCP:N-Factor Commodity Pricing Through Term Structure Estimation

Commodity pricing models are (systems of) stochastic differential equations that are utilized for the valuation and hedging of commodity contingent claims (i.e. derivative products on the commodity) and other commodity related investments. Commodity pricing models that capture market dynamics are of great importance to commodity market participants in order to exercise sound investment and risk-management strategies. Parameters of commodity pricing models are estimated through maximum likelihood estimation, using available term structure futures data of a commodity. 'NFCP' (n-factor commodity pricing) provides a framework for the modeling, parameter estimation, probabilistic forecasting, option valuation and simulation of commodity prices through state space and Monte Carlo methods, risk-neutral valuation and Kalman filtering. 'NFCP' allows the commodity pricing model to consist of n correlated factors, with both random walk and mean-reverting elements. The n-factor commodity pricing model framework was first presented in the work of Cortazar and Naranjo (2006) <doi:10.1002/fut.20198>. Examples presented in 'NFCP' replicate the two-factor crude oil commodity pricing model presented in the prolific work of Schwartz and Smith (2000) <doi:10.1287/mnsc.46.7.893.12034> with the approximate term structure futures data applied within this study provided in the 'NFCP' package.

Maintained by Thomas Aspinall. Last updated 3 years ago.

0.5 match 5 stars 4.40 score 4 scripts

cran

ReDirection:Predict Dominant Direction of Reactions of a Biochemical Network

Biologically relevant, yet mathematically sound constraints are used to compute the propensity and thence infer the dominant direction of reactions of a generic biochemical network. The reactions must be unique and their number must exceed that of the reactants,i.e., reactions >= reactants + 2. 'ReDirection', computes the null space of a user-defined stoichiometry matrix. The spanning non-zero and unique reaction vectors (RVs) are combinatorially summed to generate one or more subspaces recursively. Every reaction is represented as a sequence of identical components across all RVs of a particular subspace. The terms are evaluated with (biologically relevant bounds, linear maps, tests of convergence, descriptive statistics, vector norms) and the terms are classified into forward-, reverse- and equivalent-subsets. Since, these are mutually exclusive the probability of occurrence is binary (all, 1; none, 0). The combined propensity of a reaction is the p1-norm of the sub-propensities, i.e., sum of the products of the probability and maximum numeric value of a subset (least upper bound, greatest lower bound). This, if strictly positive is the probable rate constant, is used to infer dominant direction and annotate a reaction as "Forward (f)", "Reverse (b)" or "Equivalent (e)". The inherent computational complexity (NP-hard) per iteration suggests that a suitable value for the number of reactions is around 20. Three functions comprise ReDirection. These are check_matrix() and reaction_vector() which are internal, and calculate_reaction_vector() which is external.

Maintained by Siddhartha Kundu. Last updated 3 years ago.

0.5 match 1.00 score 1 scripts