rdecision 1.1.2

• 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.

rdecision 1.1.1

• 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.

rdecision 1.1.0

• 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
  2017. 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.

rdecision 1.0.4

• 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.

rdecision 1.0.3

• 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.

rdecision 1.0.2

• 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.

rdecision 1.0.1

• 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).

rdecision 1.0.0

• 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.

rdecision 0.1.7

• 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.

rdecision 0.1.6

• 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 ExpressionModelVariables 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.

rdecision 0.1.3

• 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).

rdecision 0.1.2

• 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.

rdecision 0.1.1

• 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.