- Confirmed that utilities > 1 are supported in semi Markov models
and added an example to the test suite to check it. See similar notes on
`DecisionTree`

. - Removed the warning issued if a utility value of > 1 is sampled
via function
`MarkovState$utility`

. - Confirmed that utilities > 1 are supported in decision trees and
added a fictitious model to the test suite to check it. To achieve this,
the utility should be defined as a model variable, e.g.
`u <- ConstModVar$new("utility", const = 2)`

and passed as the`utility`

argument to`LeafNode`

. Scalar arguments remain subject to range checking in [-Inf,1] for normal usage and to retain the previous behaviour. - Added vectorised function
`as_value`

to return the value of an object if it is a`ModVar`

(via its`get()`

method) or if it is a numeric object. Intended as an internal function to avoid type testing on polymorphic variables. - Added vectorised function
`is_ModVar`

to test whether an object is a model variable. Intended as an internal function to the package. - Added function
`abortifnot`

(a replacement for`stopifnot`

using rlang but with limited capability). It is a non-exported function intended for use in checking function arguments without increasing the cyclomatic complexity of the function itself. - Clarified the documentation for argument
`W`

(list of walks) for`DecisionTree$evaluate_walks`

. Added the alternative argument`Wi`

in which the indices of edges in each walk are provided, to improve efficiency in avoiding repeated conversion between edges and indices during PSA. Added function`edge_properties`

to collect information for computing path products and sums without repeated tree walking. - Added functions
`vertex_along`

,`edge_along`

,`vertex_at`

and`edge_at`

to class`Graph`

to clarify the relationship between nodes and edges and their indices, and to help optimise iterations in graph algorithms.

- Edited codecov badge reference in readme.Rmd with revised preferred URL.
- Changed citation style to one that does not write DOIs (which sometimes cause errors on CRAN checks).
- Changed difftime class checks to use inherits(), not class(), as per CRAN checks.
- Removed empty labels in blocks for DecisionTree$evaluate(), as per new CRAN warnings.
- Improved code efficiency in
`SemiMarkovModel$cycle()`

by generating intermediate results as matrices. - Added Paola to the package author list.
- Added Paola’s Decision Tree tutorial vignette.
- Added extra tests to the test harness for
`ExprModVar`

to check that nested autocorrelation is supported (i.e. when at least one model variable appears twice or more as an operand of an expression, when it is evaluated recursively). - Clarified the meanings of the options to
`set`

for`ModVar`

and`ExprModVar`

in the documentation for those classes. - Each test in test-ExprModVar that involves sampling has an expected failure rate of around 0.1% and is excluded from CRAN.
- Each
`ExprModVar`

now has an empirical distribution, which is sampled on creation, to optimize functions`mu_hat`

,`sigma_hat`

and`q_hat`

, at Paola’s suggestion. - Added class
`EmpiricalDistribution`

and its test harness. - Changed
`CohortMarkovModel`

to`SemiMarkovModel`

in README. - Corrected
`OccCost`

and`EntryCost`

columns in`SemiMarkovModel$cycle`

to make them per person costs. - Default occupancy cost per state set to zero in
`SemiMarkovModel`

.

- Added data/BriggsEx47, as example 4.7 from Briggs et al to /data.
- Added elementary semi-Markov model vignette (Chancellor).
- Added narrative to
`SemiMarkovModel`

as caution for converting between transition rates and per-cycle probabilities. Cited work of Jones et al- and Welton (2005) which motivated the approach taken.

- Added
`set_probabilities`

method to`SemiMarkovModel`

to set transition probabilities from a matrix. - Added multivariate
`DirichletDistribution`

class, mainly to support PSA in semi-Markov models. - Refactored model variable classes into much smaller convenience
classes with an underlying distribution. For example
`BetaModVar`

has a`BetaDistribution`

uncertainty. - Refactored
`ModVar`

with a “has-a” relationship to an underlying uncertainty distribution. Incorporated ability to link several model variables to a common underlying distribution (for use with multinomial Dirichlet etc.). - Added distribution class
`DiracDistribution`

. - Added subclasses of
`Distribution`

for each of the currently supported distributions (Beta, Normal, Log Normal, Gamma). - Added base class
`Distribution`

to represent multivariate distributions. - Added single/combined therapy HIV vignette.
- Added class
`SemitMarkovModel`

and its test script. - Added class
`Transition`

(inherits from`Node`

) and its test script. - Added class
`MarkovState`

(inherits from`Edge`

) and its test script. - Self loops in digraphs have a value of zero in the incidence matrix.

- Added option
`value`

to method`set`

in class`ModVar`

. This allows variables to be set to an explicit value; used in threshold finding. - Added
`threshold`

function to`DecisionTree`

to calculate the value of a model variable at which the cost difference becomes zero or ICER crosses a threshold. - Added option
`run`

to`by`

argument of`DecisionTree$evaluate()`

. Avoids application having to`reshape`

output before reporting PSA results. - Fixed bug in method
`DecisionTree$tornado`

which caused bars to be clipped under some circumstances. - Minor revisions to the Tegaderm vignette.

- Package tests that involve sampling randomly from a distribution and comparing the results with parameters of an expected distribution have been excluded when running CRAN tests. Otherwise the central limit theorem or empirical distributions are used to find 99.9% confidence limits on sample mean and SD.
- Added common test helpers and bespoke expectations to
`testthat/setup.R`

. - Changed vignette titles to reflect what kind of problem they illustrate, rather than the problems themselves, to make it clearer on the CRAN page.
- Added method
`as_DOT`

to`Graph`

and`Digraph`

for export to graphviz DOT file format to aid visualization of graphs.

- Added tests to give 100% coverage and replaced
`tolerance`

in`expect_equal`

with`abs`

in expect_true for approximate equality tests. - Further description for documentation.
- Converted vignettes to HTML.
- Added
`WORDLIST`

file and sundry administrative changes for clean package build. - Added
`README`

file, with an example and acknowledgements.

- Added
`draw()`

method to`DecisionTree`

. - Added
`tornado()`

method to`DecisionTree`

for univariate sensitivity analysis. - Optimized probabilistic sensitivity analysis loop in
`DecisionTree`

(1000 evaluations of a typical HTA tree takes < 5s on a typical PC).

- First full release of the package.
- Added graph theory classes. Decision trees and Markov models are forms of graph.
- Renamed
`ModelVariable`

as`ModVar`

for compactness, and renamed its derived classes similarly. - Added test harnesses for more classes.
- Collected vignette citations to file references.bib and changed to
*BMJ*csl style. - Added extra graph theory and decision tree vignettes.

- Removed the label argument from
`ModelVariable`

. - Improved auto-detection of variable label in
`ModelVariable`

. - Added NEWS.md and
`CITATION`

file to inst folder in CRAN preparation. - Added
`tests/testthat`

folder with tests for`ModelVariable`

. - Added scripts to call devtools::check/build on Windows/Mac.
- Fixed notes issued by R CMD check.

- Introduced the
`ModelVariable`

class as the new base class from which to construct the variables in an economic model. The class includes methods to support parametrization of uncertainty in the model variable. - Introduced sub-classes of
`ModelVariable`

to model particular forms of uncertainty. These are`ConstModelVariable`

,`NormalModelVariable`

,`GammaModelVariable`

,`BetaModelVariable`

,`LogNormalModelVariable`

. They do as expected from their names. Some support alternative forms of parametrization. - Introduced
`ExpressionModelVariable`

. A sub-class of`ModelVariable`

, objects of this class are defined with an expression involving other model variables. The concept permits variables to be combined in any mathematical expression that R itself will support. Because`ExpressionModelVariable`

s are themselves`ModelVariables`

, they can can appear in an expression that is used to define another model variable. - Introduced tabulation functions to list the properties of a model variable and its operands.
- Revised
`Node`

and its sub classes to accept`ModelVariables`

as arguments to costs, utilities and probabilities, thus embedding probabilistic sensitivity analysis into decision tree models. - Added the Tegaderm vignette. This is a published example of a
decision tree model with PSA and is partial validation of the
`ModelVariable`

approach to PSA. - Updated the Sumatriptan vignette, after subsuming some of its
pathway traversal code into
`Node`

classes. - Removed
`node.apply`

and`path.apply`

functions, and subsumed them into`Node`

. - Removed functions intended for use with
`node.apply`

and`path.apply`

, and subsumed them into`Node`

. - Provided
`Node`

objects with a Document Object Model (DOM) interface, as far as practicable.

- Moved citations in vignettes from external file
`references.bib`

to directly embed them in the YAML headers. To do: explore whether references can be saved in preferred bib format. - Replaced call to
`nullfile()`

, for suppressed output, in function`des`

with detection of OS to support older R versions (`nullfile`

was introduced to base R at 3.6.0).

- For the Markov solver:
- Function is now called
`des`

- It returns a list of summary matrices (the same ones written to csv files) instead of a single number.
- Output can be suppressed by setting stub=NA.
- Some minor bugs fixed.

- Function is now called

- First local release of rdecision as a package.
- Added classes for solving decision trees (
`Node`

,`LeafNode`

,`ChanceNode`

,`DecisionNode`

) and pathway detection and traversal functions. - Incorporated our discrete event solver, originally written in Matlab for the WatchBP model, then translated as a stand-alone R script, into the package.
- Added vignettes for Sumatriptan model from Briggs (Box 2.3) and from Sonnenberg and Beck’s original 3-state example.