fit_dsmm()create_sequence()get_kernel()parametric_dsmm()nonparametric_dsmm()
(@Bisaloo, 2)CODE_OF_CONDUCT.md file can be found in the .github folder.get_kernel() has small changes in the description, regarding the
reduction of dimensions when selecting a specific argument of u, v, l or t.The Github workflow R-hub v2 is added in .github/workflows. This ensures
that after every github update of the online dsmmR repository, the CRAN
checks are being made for multiple platforms (linux, macos, windows). This can
be run manually through the command rhub::rhub_check(branch = 'master').
Another Github workflow codecov was added in .github/workflows. This
ensures that the dsmmR package is mostly covered by the automated tests in
place.
valid_p_dist(),
valid_fdist_nonparametric(), valid_fdist_parametric() and added a cases for
the f non-drifting case in get_fdist_parametric(). This was causing some
errors to appear when trying to print an estimated fitted model when f was not
drifting (Model 2).paper.bib, DOIs were added in the correct
formatting. This was a small fix done for the JOSS publication.fit_dsmm() gains a new attribute multi_estimation, which enables the
estimation of a drifting semi-Markov model using multiple sequences. There
are two possible options: avg_model and count_sum.
avg_model averages the q_i received from multiple sequencescount_sum adds the counts of the states for each sequence
(of equal size) and then computes the q_i.simulate.dsmm() gains a new attribute, max_seq_length, which is renamed
from old attribute seq_length for clarity.
Depends section. Now we impose the requirement for R >= 3.5.0,
in order to make proper use of the isTRUE() and isFALSE() functions in
the is_logical() function defined in utils.R. These functions will remain
for their clarity.fit_dsmm() and simulate.dsmm() functions now better explain the difference
between a sequence of states and the embedded Markov chain.
Notably, it was specified that sequences are input in fit_dsmm(), specified
with the argument sequence.
In simulate.dsmm(), such a sequence is the resulting output.
The embedded Markov chain can be seen as part of the output from fit_dsmm(),
named emc. Furthermore, the function base::rle() is mentioned for clarity.README and DESCRIPTION files with an acknowledgement section.Added a NEWS.md file to track changes to the package.
Now the fit_dsmm() function has a default value for the states attribute,
being the sorted unique values of the sequence character vector attribute.
simulate.dsmm() sometimes did not function as expected
when nsim = 1.
Now it is possible to specify nsim = 0, so that the simulated
sequence will only include the initial state and its
corresponding sojourn time, e.g. "a", "a", "a".
By giving nsim = 1 , a single simulation will be made from the
drifting semi-Markov kernel, returning for example "a", "a",
"a", "c".
Updated the documentation for simulate.dsmm(), with accordance to
the changes made.
Updated the README file.
Added high-level documentation of the package.
Added installation instructions with access to the development version of the package through github.
Updated the documentation for dsmmR-package.
Added a "Community Guidelines" section, so that users can report errors or mistakes and contribute directly to the software through the newly-established open-source github page at https://github.com/Mavrogiannis-Ioannis/dsmmR.
Added a "Notes" section, specifying that automated tests are in place in order to aid the user with any false input made and, furthermore, to ensure that the functions used return the expected output.