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