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News

0.1.3.1

• 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

0.1.2

• Function PF_lm.R has changed the output, now it returns a list of three elements
• Changes in the examples section of PF_lm.R documentation
• Better performance achieved with the new function PF_lm_ss.R which uses the method of simple sampling as resampling method

0.1.1

• Changes in the documentation

0.1.0 Initial Release

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.