panelPomp - Inference for Panel Partially Observed Markov Processes

Data analysis based on panel partially-observed Markov process (PanelPOMP) models. To implement such models, simulate them and fit them to panel data, 'panelPomp' extends some of the facilities provided for time series data by the 'pomp' package. Implemented methods include filtering (panel particle filtering) and maximum likelihood estimation (Panel Iterated Filtering) as proposed in Breto, Ionides and King (2020) "Panel Data Analysis via Mechanistic Models" <doi:10.1080/01621459.2019.1604367>.

Last updated 2 months ago

5.63 score 47 scripts 138 downloads

arima2 - Likelihood Based Inference for ARIMA Modeling

Estimating and analyzing auto regressive integrated moving average (ARIMA) models. The primary function in this package is arima(), which fits an ARIMA model to univariate time series data using a random restart algorithm. This approach frequently leads to models that have model likelihood greater than or equal to that of the likelihood obtained by fitting the same model using the arima() function from the 'stats' package. This package enables proper optimization of model likelihoods, which is a necessary condition for performing likelihood ratio tests. This package relies heavily on the source code of the arima() function of the 'stats' package. For more information, please see Jesse Wheeler and Edward L. Ionides (2023) <arXiv:2310.01198>.

Last updated 5 months ago

arimaarmamaximum-likelihood-estimationtime-seriestime-series-analysis

4.08 score 2 stars 10 scripts 484 downloads