Showing 11 of total 11 results (show query)
vandomed
accelerometry:Functions for Processing Accelerometer Data
A collection of functions that perform operations on time-series accelerometer data, such as identify non-wear time, flag minutes that are part of an activity bout, and find the maximum 10-minute average count value. The functions are generally very flexible, allowing for a variety of algorithms to be implemented. Most of the functions are written in C++ for efficiency.
Maintained by Dane R. Van Domelen. Last updated 6 years ago.
accelerometerexercisemoving-averagephysical-activitysedentary-lifewearable-devicescpp
24.0 match 6 stars 6.62 score 31 scripts 5 dependentscran
astrochron:A Computational Tool for Astrochronology
Routines for astrochronologic testing, astronomical time scale construction, and time series analysis <doi:10.1016/j.earscirev.2018.11.015>. Also included are a range of statistical analysis and modeling routines that are relevant to time scale development and paleoclimate analysis.
Maintained by Stephen Meyers. Last updated 6 months ago.
22.3 match 5 stars 3.85 score 141 scriptsrobjhyndman
forecast:Forecasting Functions for Time Series and Linear Models
Methods and tools for displaying and analysing univariate time series forecasts including exponential smoothing via state space models and automatic ARIMA modelling.
Maintained by Rob Hyndman. Last updated 7 months ago.
forecastforecastingopenblascpp
4.0 match 1.1k stars 18.63 score 16k scripts 239 dependentsmikeblazanin
gcplyr:Wrangle and Analyze Growth Curve Data
Easy wrangling and model-free analysis of microbial growth curve data, as commonly output by plate readers. Tools for reshaping common plate reader outputs into 'tidy' formats and merging them with design information, making data easy to work with using 'gcplyr' and other packages. Also streamlines common growth curve processing steps, like smoothing and calculating derivatives, and facilitates model-free characterization and analysis of growth data. See methods at <https://mikeblazanin.github.io/gcplyr/>.
Maintained by Mike Blazanin. Last updated 2 months ago.
7.5 match 30 stars 7.90 score 75 scriptsfate-ewi
bayesdfa:Bayesian Dynamic Factor Analysis (DFA) with 'Stan'
Implements Bayesian dynamic factor analysis with 'Stan'. Dynamic factor analysis is a dimension reduction tool for multivariate time series. 'bayesdfa' extends conventional dynamic factor models in several ways. First, extreme events may be estimated in the latent trend by modeling process error with a student-t distribution. Second, alternative constraints (including proportions are allowed). Third, the estimated dynamic factors can be analyzed with hidden Markov models to evaluate support for latent regimes.
Maintained by Eric J. Ward. Last updated 5 months ago.
2.3 match 28 stars 8.28 score 101 scriptskvasilopoulos
transx:Transform Univariate Time Series
Univariate time series operations that follow an opinionated design. The main principle of 'transx' is to keep the number of observations the same. Operations that reduce this number have to fill the observations gap.
Maintained by Kostas Vasilopoulos. Last updated 4 years ago.
detrendfiltersoutlierstime-seriestransx
4.0 match 3 stars 4.29 score 13 scriptsquantsch
Strategy:Generic Framework to Analyze Trading Strategies
Users can build and test customized quantitative trading strategies. Some quantitative trading strategies are already implemented, e.g. various moving-average filters with trend following approaches. The implemented class called "Strategy" allows users to access several methods to analyze performance figures, plots and backtest the strategies. Furthermore, custom strategies can be added, a generic template is available. The custom strategies require a certain input and output so they can be called from the Strategy-constructor.
Maintained by Julian Busch. Last updated 8 years ago.
1.0 match 4.18 score 30 scriptsazulgarza
lineartestr:Linear Specification Testing
Tests whether the linear hypothesis of a model is correct specified using Dominguez-Lobato test. Also Ramsey's RESET (Regression Equation Specification Error Test) test is implemented and Wald tests can be carried out. Although RESET test is widely used to test the linear hypothesis of a model, Dominguez and Lobato (2019) proposed a novel approach that generalizes well known specification tests such as Ramsey's. This test relies on wild-bootstrap; this package implements this approach to be usable with any function that fits linear models and is compatible with the update() function such as 'stats'::lm(), 'lfe'::felm() and 'forecast'::Arima(), for ARMA (autoregressiveāmoving-average) models. Also the package can handle custom statistics such as Cramer von Mises and Kolmogorov Smirnov, described by the authors, and custom distributions such as Mammen (discrete and continuous) and Rademacher. Manuel A. Dominguez & Ignacio N. Lobato (2019) <doi:10.1080/07474938.2019.1687116>.
Maintained by Federico Garza. Last updated 4 years ago.
dominguez-lobato-testlinear-regressionlinear-specificationlobatoreset-testwild-bootstrap
1.0 match 1 stars 2.70 score 2 scripts