Changes in version 0.1.3.1 (2023-02-02)                 

  - Minor typos and corrections
  - Updated maintainer's email, adding author.

                 Changes in version 0.1.3 (2020-10-12)                  

  - 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

                 Changes in version 0.1.2 (2018-08-14)                  

  - 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

                 Changes in version 0.1.1 (2017-10-13)                  

  - Changes in the documentation

                 Changes in version 0.1.0 (2017-09-24)