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ramikrispin

TSstudio:Functions for Time Series Analysis and Forecasting

Provides a set of tools for descriptive and predictive analysis of time series data. That includes functions for interactive visualization of time series objects and as well utility functions for automation time series forecasting.

Maintained by Rami Krispin. Last updated 2 years ago.

forecastingtime-seriestimeseriestsstudiovisualization

424 stars 9.00 score 656 scripts

tylerjpike

sovereign:State-Dependent Empirical Analysis

A set of tools for state-dependent empirical analysis through both VAR- and local projection-based state-dependent forecasts, impulse response functions, historical decompositions, and forecast error variance decompositions.

Maintained by Tyler J. Pike. Last updated 2 years ago.

econometricsforecastingimpulse-responselocal-projectionmacroeconomicsstate-dependenttime-seriesvector-autoregression

12 stars 4.78 score 8 scripts

epiforecasts

EpiNow:Estimate Realtime Case Counts and Time-varying Epidemiological Parameters

To identify changes in the reproduction number, rate of spread, and doubling time during the course of outbreaks whilst accounting for potential biases due to delays in case reporting.

Maintained by Sam Abbott. Last updated 5 years ago.

cpp

33 stars 4.74 score 111 scripts

craig-pt

tsgc:Time Series Methods Based on Growth Curves

The 'tsgc' package provides comprehensive tools for the analysis and forecasting of epidemic trajectories. It is designed to model the progression of an epidemic over time while accounting for the various uncertainties inherent in real-time data. Underpinned by a dynamic Gompertz model, the package adopts a state space approach, using the Kalman filter for flexible and robust estimation of the non-linear growth pattern commonly observed in epidemic data. The reinitialization feature enhances the model’s ability to adapt to the emergence of new waves. The forecasts generated by the package are of value to public health officials and researchers who need to understand and predict the course of an epidemic to inform decision-making. Beyond its application in public health, the package is also a useful resource for researchers and practitioners in fields where the trajectories of interest resemble those of epidemics, such as innovation diffusion. The package includes functionalities for data preprocessing, model fitting, and forecast visualization, as well as tools for evaluating forecast accuracy. The core methodologies implemented in 'tsgc' are based on well-established statistical techniques as described in Harvey and Kattuman (2020) <doi:10.1162/99608f92.828f40de>, Harvey and Kattuman (2021) <doi:10.1098/rsif.2021.0179>, and Ashby, Harvey, Kattuman, and Thamotheram (2024) <https://www.jbs.cam.ac.uk/wp-content/uploads/2024/03/cchle-tsgc-paper-2024.pdf>.

Maintained by Craig Thamotheram. Last updated 8 months ago.

1 stars 4.68 score 24 scripts

jenswahl

stochvolTMB:Likelihood Estimation of Stochastic Volatility Models

Parameter estimation for stochastic volatility models using maximum likelihood. The latent log-volatility is integrated out of the likelihood using the Laplace approximation. The models are fitted via 'TMB' (Template Model Builder) (Kristensen, Nielsen, Berg, Skaug, and Bell (2016) <doi:10.18637/jss.v070.i05>).

Maintained by Jens Wahl. Last updated 2 months ago.

cpp

8 stars 4.60 score

epiforecasts

EpiSoon:Forecast Cases Using Reproduction Numbers

To forecast the time-varying reproduction number and use this to forecast reported case counts. Includes tools to evaluate a range of models across samples and time series using proper scoring rules.

Maintained by Sam Abbott. Last updated 2 years ago.

case-forecastsforecasts

7 stars 4.26 score 25 scripts 1 dependents

cran

STCCGEV:Conditional Copula Model for Crop Yield Forecasting

Provides functions to model and forecast crop yields using a spatial temporal conditional copula approach. The package incorporates extreme weather covariates and Bayesian Structural Time Series models to analyze crop yield dependencies across multiple regions. Includes tools for fitting, simulating, and visualizing results. This method build upon established R packages, including 'Hofert' 'et' 'al'. (2025) <doi:10.32614/CRAN.package.copula>, 'Scott' (2024) <doi:10.32614/CRAN.package.bsts>, and 'Stephenson' 'et' 'al'. (2024) <doi:10.32614/CRAN.package.evd>.

Maintained by Yongkun Li. Last updated 6 days ago.

2.00 score

felipeelorrieta

iAR:Irregularly Observed Autoregressive Models

Data sets, functions and scripts with examples to implement autoregressive models for irregularly observed time series. The models available in this package are the irregular autoregressive model (Eyheramendy et al.(2018) <doi:10.1093/mnras/sty2487>), the complex irregular autoregressive model (Elorrieta et al.(2019) <doi:10.1051/0004-6361/201935560>) and the bivariate irregular autoregressive model (Elorrieta et al.(2021) <doi:10.1093/mnras/stab1216>).

Maintained by Elorrieta Felipe. Last updated 23 days ago.

openblascppopenmp

1.00 score

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