Package index • ARTEMIS

All functions

anyDrugMissingInRegimens(): Any Drug Missing In Regimens?
anyUncoveredDrugs(): Any uncovered drugs?
buildBlacklistRegex(): Build a single regex from blacklist terms
calculateRegimenTimes(): Calculates Regimen start and end time.
cleanByBlacklist(): Run blacklist cleaning pipeline on in-scope regimens_env Uses `regimens_env$regimens` from the current scope, filters rows where any cell contains a blacklist term (case-insensitive), updates `regimens_env$regimens` in place.
cleanText(): Normalize text for blacklist matching Strips punctuation, trims whitespace, and lowercases text.
combineOverlaps(): Combine Overlaps
conceptToList(): Map concept IDs to concept names Reads a line-delimited list of concept IDs and returns the matching concept names from `regimens_env$concepts` already in scope.
createDrugDF(): Creates cumulative times for each drug in the drug record
decode(): Decodes a tuple of tuples into a TxDrugA.TyDrugB text format
defaultSmatrix(): Create a default substitution matrix from all unique elements of s1 and s2
default_concept_list: Map concept IDs to concept names Default concept IDs when no file is provided.
df_json: A default cohort indicating metastatic lung cancer patients
.data: .data Object
encode(): Encodes a sequence from txDRUG.tyDRUG format into a tuple of tuples
escapeRegex(): Escape regex metacharacters
filter_stringDF(): Filter a stringDF dataframe to contain only valid patients
generateCohortStats(): Generate several stats related to the input cohort
generateRawAlignments(): Generate Alignments
generateRegimenStats(): Generate Regimen Stats
generateSummaryReport(): Generate summary report
getConDF(): Generate a con_df dataframe without using CDMConnector
lineOfTreatment(): Adds line of treatment to a processed regimen alignment ouput
loadCohort(): Load the default regimen group dataframe
loadDrugs(): Load the default valid drugs dataframe
loadGroups(): Load the default regimen group dataframe
loadRegimens(): Load regimens for a given condition
no_unique_aligned_drugs(): Calculates the number of unique aligned drugs in a regimen
nonRegimenDrugExposure(): Non-Regimen Drug Exposure?
plotAlignment(): Plots a full alignment output
plotErasFrequency(): Plots a plot displaying the ERA frequency of the top N most frequent eras
plotFrequency(): Plot Regimen Frequency Plot frequency of the top N most frequent regimens
plotRegimenLengthDistribution(): Plot Regimen Length Distribution
plotSankey(): Plots a sankey diagram displaying the flow between first, second and third regimen eras
plotScoreDistribution(): Adjusted Score distribution plot
postprocessSinglePatientDF(): Postprocess alignment output
processAlignments(): Post-process alignment output
progress(): Progress report#'
py_lib_install(): Python Utility Installs Ensures relevant python libraries are installed
regName: Global Variable Warnings These warnings serve no purpose and since tidyr 1.2.0 the .data pronoun is not used in select() and unnest(), thus leading to these warnings being erroneously generated
regimenEndsBeforeLastDrug(): Regimens ends before last drug?
regimenStartAfterFirstDrug(): Regimens start after the first drug?
regimenStartBeforeFirstDrug(): Regimens start before the first drug?
regimengroups: A default set of regimen groups, such as chemotherapy, ICI-combinations etc.
regimens: Default Regimen data for lung cancer
removeOverlaps(): Removes overlapping regimens from alignment output
rowHasBlacklist(): Detect blacklist matches across any column (case-insensitive) Expands multi-word terms by adding whitespace-stripped variants before regex matching, while keeping original terms intact.
sameConsecutiveRegimens(): Consecutive regimens are the same?
stringDF_from_cdm(): Generate a set of patient drug record strings from a valid CDM connection and a valid cohort JSON.
validConditions(): Display a list of valid conditions
validdrugs: A default set of valid drugs used for alignment
writeOutputs(): Filter a stringDF dataframe to contain only valid patients