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Minor typos and corrections
Updated maintainer’s email, adding author. ## 0.1.3
Separation of dependent and independent variables into two separate arguments in PF_lm.R and PF_lm_ss.R
Inclusion of utils.R auxiliary functions with resampling methods following Li, T., Bolic, M., & Djuric, P. M. (2015). Resampling methods for particle filtering: classification, implementation, and strategies. IEEE Signal processing magazine, 32(3), 70-86.
Separate arguments for dependent variable (Y) and independent variables (Data1) in PF_lm and PF_lm_ss
Included parameter lbd for the initial priors when initDisPar is not provided, apply for PF_lm and PF_lm_ss
The returned list of PF_lm and PF_lm_ss includes a summary of the estimated parameters
Argument initDisPar in PF_lm and PF_lm_ss now only includes the parameters that are going to be estimated. See Details section.
Included two algorithms that include evolutionary algorithms-based parameters inside the particle filters version for both linear and non-linear (logistic) models: EPF_L_compl.R and EPF_logist_compl.R
Updated author’s email
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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