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cran

timeSeries:Financial Time Series Objects (Rmetrics)

'S4' classes and various tools for financial time series: Basic functions such as scaling and sorting, subsetting, mathematical operations and statistical functions.

Maintained by Georgi N. Boshnakov. Last updated 6 months ago.

2 stars 7.89 score 146 dependents

trackerproject

trackeR:Infrastructure for Running, Cycling and Swimming Data from GPS-Enabled Tracking Devices

Provides infrastructure for handling running, cycling and swimming data from GPS-enabled tracking devices within R. The package provides methods to extract, clean and organise workout and competition data into session-based and unit-aware data objects of class 'trackeRdata' (S3 class). The information can then be visualised, summarised, and analysed through flexible and extensible methods. Frick and Kosmidis (2017) <doi: 10.18637/jss.v082.i07>, which is updated and maintained as one of the vignettes, provides detailed descriptions of the package and its methods, and real-data demonstrations of the package functionality.

Maintained by Ioannis Kosmidis. Last updated 1 years ago.

90 stars 6.37 score 58 scripts 1 dependents

r-forge

pems.utils:Portable Emissions (and Other Mobile) Measurement System Utilities

Utility functions for the handling, analysis and visualisation of data from portable emissions measurement systems ('PEMS') and other similar mobile activity monitoring devices. The package includes a dedicated 'pems' data class that manages many of the quality control, unit handling and data archiving issues that can hinder efforts to standardise 'PEMS' research.

Maintained by Karl Ropkins. Last updated 3 months ago.

openjdk

3.06 score 19 scripts

ablommaert

spectralAnalysis:Pre-Process, Visualize and Analyse Spectral Data

Infrared, near-infrared and Raman spectroscopic data measured during chemical reactions, provide structural fingerprints by which molecules can be identified and quantified. The application of these spectroscopic techniques as inline process analytical tools (PAT), provides the pharmaceutical and chemical industry with novel tools, allowing to monitor their chemical processes, resulting in a better process understanding through insight in reaction rates, mechanistics, stability, etc. Data can be read into R via the generic spc-format, which is generally supported by spectrometer vendor software. Versatile pre-processing functions are available to perform baseline correction by linking to the 'baseline' package; noise reduction via the 'signal' package; as well as time alignment, normalization, differentiation, integration and interpolation. Implementation based on the S4 object system allows storing a pre-processing pipeline as part of a spectral data object, and easily transferring it to other datasets. Interactive plotting tools are provided based on the 'plotly' package. Non-negative matrix factorization (NMF) has been implemented to perform multivariate analyses on individual spectral datasets or on multiple datasets at once. NMF provides a parts-based representation of the spectral data in terms of spectral signatures of the chemical compounds and their relative proportions. See 'hNMF'-package for references on available methods. The functionality to read in spc-files was adapted from the 'hyperSpec' package.

Maintained by Adriaan Blommaert. Last updated 1 years ago.

2.26 score 18 scripts

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