An input data object. Could be read with NMreadCsv or NMscanInput.

Path to mod/lst file. Only one of file, text, or lines to be given. See ?NMreadSection for understanding when to use, file, text, or lines.

The mod/lst as characters.

The mod/lst as character, line by line.

Invert the filters? This means read what Nonmem would disregard, and disregard what Nonmem would read.

The default is to return data as a data.frame. Pass a function (say tibble::as_tibble) in as.fun to convert to something else. If data.tables are wanted, use as.fun="data.table". The default can be configured using NMdataConf.

Don't report information along the way if no warnings or errors. Default is FALSE.

A Nonmem control stream or list file

See ?NMdataConf

Additional arguments passed to

The data to check. data.frame, data.table, tibble, anything that can be converted to data.table.

Alternatively to checking a data object, you can use file to specify a control stream to check. This can either be a (working or non-working) input control stream or an output control stream. In this case, NMdataCheck checks column names in data against control stream (see NMcheckColnames), reads the data as Nonmem would do, and do the same checks on the data as NMdataCheck would do using the data argument. col.flagn is ignored in this case - instead, ACCEPT/IGNORE statements in control stream are applied. The file argument is useful for debugging a Nonmem model.

columns that contain subject-level covariates. They are expected to be non-missing, numeric and not varying within subjects.

A list specifying columns that contain subject:occasion-level covariates. They are expected to be non-missing, numeric and not varying within combinations of subject and occasion. covs.occ=list(PERIOD=c("FED")) means that FED is the covariate, while PERIOD indicates the occasion.

Columns that are expected to be present, numeric and non-NA. If a character vector is given, the columns are expected to be used in all rows. If a column is only used for a subset of rows, use a list and name the elements by subsetting strings. See examples.

The name of the column that holds the subject identifier. Default is "ID".

The name of the column holding actual time.

The name of the column holding the dependent variable. For now, only one column can be specified, and MDV is assumed to match this column. Default is DV.

The name of the column holding the binary indicator of the dependent variable missing. Default is MDV.

The name(s) of the compartment column(s). These will be checked to be positive integers for all rows. They are also used in checks for row duplicates.

The name of the dose amount column.

Optionally, the name of the column holding numeric exclusion flags. Default value is FLAG and can be configured using NMdataConf. Even though FLAG is the default value, no finding will be returned if the column is missing unless explicitly defined as col.flagn="FLAG". This is because this way of using exclusion flags is only one of many ways you could choose to handle exclusions. Disable completely by using col.flagn=FALSE.

A column with a unique value for each row. Such a column is recommended to use if possible. Default ("ROW") can be modified using NMdataConf.

Optional unique subject identifier. It is recommended to keep a unique subject identifier (typically a character string including an abbreviated study name and the subject id) from the clinical datasets in the analysis set. If you supply the name of the column holding this identifier, NMcheckData will check that it is non-missing, that it is unique within values of col.id (i.e. that the analysis subject ID's are unique across actual subjects), and that col.id is unique within the unique subject ID (a violation of the latter is less likely).

Additional column names to consider in search of duplicate events. col.id, col.cmt, col.evid, and col.time are always considered if found in data, and cols.dup is added to this list if provided.

"est" for estimation data (default), and "sim" for simulation data. Differences are that col.row is not expected for simulation data, and subjects will be checked to have EVID==0 rows for estimation data and EVID==2 rows for simulation data.

Columns to not check. This is particularly useful when checking data sets that do not include i.e. 'CMT', 'EVID', and others. To skip checking specific columns, provide their names like 'cols.disable=c("CMT","EVID")'.

Strings to be accepted when trying to convert characters to numerics. This will typically be a string that represents missing values. Default is ".". Notice, actual NA, i.e. not a string, is allowed independently of na.strings. See ?NMisNumeric.

If TRUE (not default), the table summary that is printed if quiet=FALSE is returned as well. In that case, a list is returned, and the findings are in an element called findings.

Keep quiet? Default is not to.

The default is to return data as a data.frame. Pass a function (say tibble::as_tibble) in as.fun to convert to something else. If data.tables are wanted, use as.fun="data.table". The default can be configured using NMdataConf.

a model file (input or output control stream)

row identifier

subject identifier

Prioritized input data file formats to look for and use if found. Default is c("csv") which means rds and fst will _not_ be used. Typically only the csv file is wanted because we want to test the input data as read by Nonmem.

Keep quiet? Default is FALSE.

How to find the input control stream if you are using the output control stream.

The data directory can only be read from the control stream (.mod) and not from the output file (.lst). So if you only have the output control stream, use dir.data to tell in which directory to find the data file. If dir.data is provided, the .mod file is not used at all.

The function to run results through before returning them.

Deprecated. Use formats.read instead.

passed to NMcheckData

the Nonmem code.

NMdata options to modify. These are named arguments, like for base::options. Normally, multiple arguments can be used. The exception is if reset=TRUE is used which means all options are restored to default values. If NULL is passed to an argument, the argument is reset to default. is See examples for how to use.

Allow to store configuration of variables that are not pre-defined in NMdata. This should only be needed in cases where say another package wants to use the NMdata configuration system for variables unknown to NMdata.

If TRUE, an overview of the configuration changes is summarized in a printed table. This is useful for transparency when sourcing a file with configuration. Default is FALSE.

Optionally, a single parameter name (say "as.fun").

Allow access to configuration of variables that are not pre-defined in NMdata. This should only be needed in cases where say another package wants to use the NMdata configuration system for variables unknown to NMdata.

The name of the parameter, say "as.fun"

The value to pass. If missing or NULL, the value returned by NMdataConf/NMdataGetOption will typically be used.

argument to look up. Only one argument can be looked up.

an NMdata object

arguments passed to other methods.

The data set to expand

The name of the column holding the time on which time since previous dose will be based. This is typically actual or nominal time since first dose.

The subject identifier. All new columns will be derived within unique values of this column.

The name of the event ID column. This must exist in data. Default is EVID.

Keep track of what rows were in data originally and which ones are added by NMexpandDoses by including a column called nmexpand? nmexpand will be TRUE if the row is "generated" by NMexpandDoses.

A string that will be evaluated as a custom expression to identify relevant events.

Suppress messages back to user (default is FALSE)

The default is to return data as a data.frame. Pass a function (say tibble::as_tibble) in as.fun to convert to something else. If data.tables are wanted, use as.fun="data.table". The default can be configured using NMdataConf.

The input control stream or the list file.

See NMscanInput. If used, only the file name mentioned in $DATA is used. dir.data will be used as the path, and the existence of the file in that directory is not checked.

The input control stream. Default is to look for \"file\" with extension changed to '.mod' (PSN style). You can also supply the path to the file, or you can provide a function that translates the output file path to the input file path. The default behavior can be configured using NMdataConf. See dir.data too.

Specification of the data file path. When this is used, the control streams are not used at all.

A file path to read from. Normally a .mod or .lst. See lines and text as well.

Text lines to process. This is an alternative to using the file and text arguments.

Use this argument if the text to process is one long character string, and indicate the line separator with the linesep argument. Use only one of file, lines, and text.

The name of section to extract. Examples: "INPUT", "PK", "TABLE", etc. It can also be result sections like "MINIMIZATION".

The section denoted as a string compatible with regular expressions. "$" (remember to escape properly) for sections in .mod files, "0" for results in .lst files.

A regular expression to capture the end of the section. The default is to look for the next occurrence of char.section.

If "text", plain text lines are returned. If "idx", matching line numbers are returned. "text" is default.

Keep empty lines in output? Default is FALSE. Notice, comments are removed before empty lines are handled if 'keep.comments=TRUE'.

Keep the section name in output (say, "$PROBLEM") Default is TRUE. It can only be FALSE, if return="text".

Default is to keep comments. If FALSE, the will be removed.

If multiple hits, concatenate into one. This will most often be relevant with name="TABLE". If FALSE, a list will be returned, each element representing a table. Default is TRUE. So if you want to process the tables separately, you probably want FALSE here.

If TRUE, leading and trailing are removed, and multiplied succeeding white spaces are reduced to single white spaces.

If asOne=FALSE, do you want the result to be simplified if only one table is found? Default is TRUE which is desirable for interactive analysis. For programming, you probably want FALSE.

Default is to search for exact matches of 'section'. If FALSE, only the first three characters are matched. E.G., this allows "ESTIMATION" to match "ESTIMATION" or "EST".

Either mod, res or NULL. mod is for information that is given in .mod (.lst file can be used but results section is disregarded). If NULL, NA or empty string, everything is considered.

If using the text argument, use linesep to indicate how lines should be separated.

Deprecated. See keep.empty.

Deprecated. See keep.name.

Deprecated. See keep.comments.

Deprecated. See as.one.

The data that NONMEM will read. Either as a 'data.frame', of if a path to an rds or a delimited text file, the data will automatically be read first.

Only used for generation of proposed text for INPUT section. Columns to drop in Nonmem $INPUT. This has two implications. One is that the proposed $INPUT indicates =DROP after the given column names. The other that in case it is a non-numeric column, succeeding columns will still be included in $INPUT and can be read by NONMEM.

Name of a numeric column with zero value for rows to include in Nonmem run, non-zero for rows to skip. The argument is only used for generating the proposed $DATA text to paste into the Nonmem control stream. Default is defined by 'NMdataConf()'. To skip this feature, use 'col.flagn=FALSE'.

For the $INPUT text proposal only. If you want to rename columns in NONMEM $DATA, NMwriteData can adjust the suggested $DATA text. If you plan to use BBW instead of BWBASE in Nonmem, consider rename=c(BBW="BWBASE"). The result will include BBW and not BWBASE.

For the $INPUT text proposal only. If you plan to use additional names for columns in Nonmem $INPUT, NMwriteData can adjust the suggested $INPUT text. Say you plan to use CONC as DV in Nonmem, use copy=c(DV="CONC"), i.e. copy=c(newname="existing"). INPUT suggestion will in this case contain DV=CONC.

The file name NONMEM will read the data from (for the $DATA section). It can be a full path.

For the $DATA text proposal only. The path to the input datafile to be used in the Nonmem $DATA section. Often, a relative path to the actual Nonmem run is wanted here. If this is used, only the file name and not the path from the file argument is used.

For the $INPUT text proposal only. If TRUE, all column names in $INPUT text will be converted to capital letters.

Use this to truncate the columns in $INPUT. until can either be a character (column name) or a numeric (column number). If a character is given, it is matched against the resulting column name representation in $INPUT, i.e. this could be "DV=CONC" if you are using in this case the copy argument. In case until is of length>1, the maximum will be used (probably only interesting if character values are supplied).

For the $INPUT text proposal only. Assume Nonmem can read TIME and DATE even if it can't be translated to numeric. This is necessary if using the 00:00 format. Default is TRUE.

If positive, will be passed to strwrap for the $INPUT text. If missing or NULL, strwrap will be called with default value. If negative or zero, strwrap will not be called.

Hold messages back? Default is defined by NMdataConf.

An object of class NMdata (a result of 'NMscanData()')

If not passed, all the metadata is returned. You can use "details", "tables", or "columns" to get only these subsets. If info is "tables" or "columns"

The default is to return data as a 'data.frame'. Pass a function (say 'tibble::as_tibble') in as.fun to convert to something else. If 'data.table's are wanted, use 'as.fun="data.table"'. The default can be configured using 'NMdataConf()'.

The vector to check Don't export

Tolerated strings that do not translate to numerics. Default is to accept "." because it's common to write missing values that way to Nonmem (even if Nonmem will handle them as zeros rather than missing). Notice actual NA's are accepted so you may want to use na.strings=NULL if you don't code missings as "." and just do this when writing the data set to a delimited file (like NMwriteData will do for you).

Use each=TRUE to evaluate each element in a vector individually. The default (each=FALSE) is to return a single-length logical for a vector x summarizing whether all the elements are numeric-compatible.

The dataset which columns to reorder.

Columns that should come almost first. See details.

Columns to move to back of dataset. If you work with a large dataset, and some columns are irrelevant for the Nonmem runs, you can use this argument.

Should columns which names contain lowercase characters be moved towards the back? Some people use a standard of lowercase variables (say "race") being character representations ("Asian", "Caucasian", etc.) variables and the uppercase (1,2,...) being the numeric representation for Nonmem.

Should columns which cannot be converted to numeric be put towards the end? A column can be a character or a factor in R, but still be valid in Nonmem (often the case for ID which can only contain numeric digits but really is a character or factor). So rather than only looking at the column class, the columns are attempted converted to numeric. Notice, it will attempted to be converted to numeric to test whether Nonmem will be able to make sense of it, but the values in the resulting dataset will be untouched. No values will be edited. If TRUE, logicals will always be put last. NA's must be NA or ".".

Sort columns alphabetically. Notice, this is the last order priority applied.

Name of the (numeric) unique subject ID. Can be controlled with 'NMdataConf()'.

The name of the column containing nominal time. If given, it will put the column quite far left, just after row counter and 'col.id'. Default value is NOMTIME and can be configured with 'NMdataConf()'.

The name of the column containing actual time. If given, it will put the column quite far left, just after row counter, subject ID, and nominal time. Default value is 'TIME'. Can be controlled with 'NMdataConf()'.

A row counter column. This will be the first column in the dataset. Technically, you can use it for whatever column you want first. Default value is 'ROW' and can be configured with 'NMdataConf()'.

The name of the column containing numerical flag values for data row omission. Default value is FLAG and can be configured with 'NMdataConf()'.

a vector of column names to put early to represent dependent variable(s). Default is DV.

For the $INPUT text proposal only. Assume Nonmem can read TIME and DATE even if it can't be translated to numeric. This is necessary if using the 00:00 format. Default is TRUE.

The default is to return a data.table if data is a data.table and return a data.frame in all other cases. Pass a function in as.fun to convert to something else. The default can be configured using 'NMdataConf()'. However, if data is a data.table, settings via 'NMdataConf()' are ignored.

If true, no warning will be given about missing standard Nonmem columns.

The ".cov" covariance Nonmem matrix file to read

If 'TRUE' (default) the extension will automatically be modified using 'NMdataConf()$file.cov'. This means 'file' can be the path to an input or output control stream, and 'NMreadCov()' will still read the '.cov' file.

The table number to read. The ".cov" file can contain multiple tables and will often do so if using SAEM/IMP methods. Default is "max" which means the last table is used. Alternative values are "min" and "all" or numeric values. If "all" or multiple numeric values are used, a list is returned. However, see 'simplify' too.

If 'TRUE' (default) and only one table is returned (say using tableno="max") only that matrix is returned as a matrix object. If 'FALSE' or multiple tables are returned, the result is a list.

The file to read. Must be pure text.

List of arguments passed to fread. Notice that except for "file", you need to supply all arguments to fread if you use this argument. Default values can be configured using NMdataConf.

The default is to return data as a data.frame. Pass a function (say tibble::as_tibble) in as.fun to convert to something else. If data.tables are wanted, use as.fun="data.table". The default can be configured using NMdataConf.

Format of file to read. Can be of length>1 in which case the first format found will be used (i.e. format is a prioritized vector). If not one of "rds" or "fst", it is assumed to be a delimited text file. Default is to determine this from the file name extension. Notice, if a delimited format is used, the extension can very well be different from "csv" (say file name is "input.tab")". This will work for any delimited format supported by fread.

Optional arguments to pass to read_fst if format="fst" is used.

Path to the ext file

The .ext file contains both final parameter estimates and iterations of the estimates. If return="pars" (default) the final estimates are returned in addition to what other parameter-level information is found, like FIX, sd etc. as columns. If return="iterations", the iterations are returned (including objective function value). If return="obj" objective function value at final estimate is returned. If return="all", all er returned, though in separate data.frames compiled in a list.

The default is to return data as a data.frame. Pass a function (say 'tibble::as_tibble') in as.fun to convert to something else. If 'data.table's are wanted, use 'as.fun="data.table"'. The default can be configured using 'NMdataConf()'.

See '?NMscanData'

See '?NMscanData'

If 'TRUE' (default) the extension will automatically be modified using 'NMdataConf()$file.ext'. This means 'file' can be the path to an input or output control stream, and 'NMreadExt' will still read the '.ext' file.

In case the ext file contains multiple tables, this argument controls which one to choose. The options are

• "max" (default) Pick the table with the highest table number. This typically means the results from the last '$ESTIMATION' step are used.

• "min" Pick results from the first table available.

• "all" Keep all results. The tables can be distinguished by the 'tableno' column.

• an integer greater than 0, in which case the table with this table number will be picked.

Deprecated. Please use file instead.

Use a slow but more robust method to read tables? If missing or 'NULL', the fast method will be tried first, and if any issues are seen, the method will switch to 'slow=TRUE'. If 'FALSE',it will also swich in case of issues, but a warning is issued. In other words, it should be safe to not use this argument.

Control stream path

Control stream lines if already read from file

Return the filters only or also return the remaining text in a separate object? If 'FALSE', a list with the two objects is returned.

Function to run on the tables with filters.

Path to a control stream. See 'lines' too.

A control stream as text lines. Use this or 'file'.

The section to read. Typically, "theta", "omega", or "sigma". Default is those three.

By default (when return="pars", a parameter table with initial values, FIX, lower and upper bounds etc. In most cases, that is what is needed to derive information about parameter definitions. If return="all", two additional tables are returned which can be used if the aim is to modify and write the resulting parameters to a control stream.

See ?NMscanData

Path to the control stream to read.

As an alternative to 'file', the control stream or selected lines of the control stream can be provided as a vector of lines.

Defines naming and splitting of contents of lines in parameter sections. Default is "%init;%idx;%symbol;%label;%unit". Be careful to remember percentage symbols in front of any variable names.

Like 'format', applied to '$OMEGA' section. Default is to reuse 'format'.

Like 'format', applied to '$SIGMA' section. Default is to reuse 'format.omega'.

Is a blank in 'fields' to be treated as a field seperator? Default is not to (i.e. neglect spaces in 'fields').

If TRUE, each line in the control stream is assigned to one parameter, at most. This means, if two parameters are listed in one line, the comments will only be used for one of the parameters, and only that parameter will be kept in output. Where this will typically happen is in '$OMEGA' and '$SIGMA' sections where off-diagonal may be put on the same line as diagonal elements. Since the off-diagonal elements are covariances of variables that have already been identified by the diagonals, the off-diagonal elements can be automatically described. For example, if 'OMEGA(1,1)' is between-subject variability (BSV) on CL and 'OMEGA(2,2) is BSV on V, then we know that 'OMEGA(2,1)' is covariance of (BSV on) CL and V.

If an index field is manually provided in the control stream comments, define the name of that field in 'format' and tell 'NMreadParsTab()' to use this idx to organize especially OMEGA and SIGMA elements by pointing to it with 'field.idx'. The default is to look for a variable called 'idx'. If the index has values like 1-2 on an OMEGA or SIGMA row, the row is interpreted as the covariance between OMEGA/SIGMA 1 and 2.

The default method is to automatically identify element numbering ('i' for THETAs, 'i' and 'j' for OMEGAs and SIGMAs). The automated method is based on identification of 'BLOCK()' structures and numbers of initial values. Should this fail, or should you want to control this manually, you can include a parameter counter in the comments and have 'NMreadParsText()' use that to assign the numbering. 'use.idx=FALSE' is default and means all blocks are handled automatically, 'use.idx=TRUE' assumes you have a counter in all sections, and a character vector like 'use.idx="omega"' can be used to denote which sections use such a counter from the control stream. When using a counter on OMEGA and SIGMA, off-diagonal elements MUST be denoted by 'i-j', like '2-1' for OMEGA(2,1). See 'field.idx' too.

See ?NMscanData

See ?NMscanData

See ?NMscanData

If an index field in comments should be used to number thetas. The index field is used to organize '$OMEGA's and '$SIGMA's because they are matrices but I do not see where this is advantageous to do for '$THETA's. Default 'use.theta.idx=FALSE' which means '$THETA's are simply counted.

Deprecated. Use 'format'.

Deprecated. Use 'format.omega'.

Deprecated. Use 'format.sigma'.

Path to the phi file. See 'auto.ext' too.

The default is to return data as a data.frame. Pass a function (say tibble::as_tibble) in as.fun to convert to something else. If data.tables are wanted, use as.fun="data.table". The default can be configured using NMdataConf.

See ?NMscanData

See ?NMscanData

If 'auto.ext=TRUE', the file name extension will automatically be changed using the setting in 'NMdataConf()$file.phi' - this by default means that the '.phi' extension will be used no matter what extension the provided file name has.

Deprecated. Use 'file'.

A file path to read from. Normally a .mod or .lst. See lines also.

Text lines to process. This is an alternative to using the file argument.

Deprecated, use 'lines'. Use this argument if the text to process is one long character string, and indicate the line separator with the linesep argument (handled by NMextractText). Use only one of file, lines, and text.

The name of section to extract without "$". Examples: "INPUT", "PK", "TABLE", etc. Not case sensitive.

If "text", plain text lines are returned. If "idx", matching line numbers are returned. "text" is default.

Keep empty lines in output? Default is FALSE. Notice, comments are removed before empty lines are handled if 'keep.comments=TRUE'.

Keep the section name in output (say, "$PROBLEM") Default is FALSE. It can only be FALSE, if return="text".

Default is to keep comments. If FALSE, the will be removed. See keep.empty too. Notice, there is no way for NMreadSection to keep comments and also drop lines that only contain comments.

If multiple hits, concatenate into one. This will most often be relevant with name="TABLE". If FALSE, a list will be returned, each element representing a table. Default is TRUE. So if you want to process the tables separately, you probably want FALSE here.

If TRUE, leading and trailing are removed, and multiplied succeeding white spaces are reduced to single white spaces.

If asOne=FALSE, do you want the result to be simplified if only one section is found? Default is TRUE which is desirable for interactive analysis. For programming, you probably want FALSE.

Deprecated. See keep.empty.

Deprecated. See keep.name.

Deprecated. See keep.comments.

Deprecated. See as.one.

Additional arguments passed to NMextractText

A model file. Extension will be replaced by ".shk".

If 'TRUE' (default) the extension will automatically be modified using 'NMdataConf()$file.shk'. This means 'file' can be the path to an input or output control stream, and 'NMreadShk' will still read the '.shk' file.

See ?NMdataConf

Control stream path.

Character vector with control stream file.

path to Nonmem table file

In case of simulations where tables are being repeated, a counter of the repetition number can be useful to include in the output. For now, this will only work if the NOHEADER option is not used. This is because NMreadTab searches for the "TABLE NO..." strings in Nonmem output tables. If col.tableno is TRUE (default), a counter of tables is included as a column called NMREP. Notice, the table numbers in NMREP are cumulatively counting the number of tables reported in the file. NMREP is not the actual table number as given by Nonmem.

col.nmrep If tables are repeated, include a counter? It does not relate to the order of the $TABLE statements but to cases where a $TABLE statement is run repeatedly. E.g., in combination with the SUBPROBLEMS feature in Nonmem, it is useful to keep track of the table (repetition) number. If col.nmrep is TRUE, this will be carried forward and added as a column called NMREP. This is default behavior when more than one $TABLE repetition is found in data. Set it to a different string to request the column with a different name. The argument is passed to NMscanTables.

The name of a column containing the name or description of the table (generated by Nonmem). The default is "table.name". Use FALSE not to include this column.

Use header=FALSE if table was created with NOHEADER option in $TABLE.

The number of rows to skip. The default is skip=1 if header==TRUE and skip=0 if header==FALSE.

logical stating whether or not information is printed about what is being done. Default can be configured using NMdataConf.

The default is to return data as a data.frame. Pass a function (say tibble::as_tibble) in as.fun to convert to something else. If data.tables are wanted, use as.fun="data.table". The default can be configured using NMdataConf.

Arguments passed to data.table::fread().

A Nonmem table file. Can be output tables, or one of the several different results files from Nonmem.

Name of the column (to be created) containing the "table name" which is derived from the Nonmem description of the table sometimes pasted above the table data.

Path to a control stream to process. See 'lines' too.

If the control stream has been read already, the text can be provided here instead of using the 'file' argument. Character vector of text lines.

Either a model name (like "Base") or a function that derives the model name from the control stream file path. The default is dropping the file name extension on the control stream file name.

Parameter type(s) to include. Default is all three possible which is c("THETA","OMEGA","SIGMA").

Name of the column containing the model name.

Sections of the control stream to consider. Default is all of c("PRED","PK","ERROR").

The default is to return data as a data.frame. Pass a function (say tibble::as_tibble) in as.fun to convert to something else. If data.tables are wanted, use as.fun="data.table". The default can be configured using NMdataConf.

Paths to input control streams to modify. See file.pattern and dir too.

A pattern to look for if 'dir' is supplied too (and not 'file.mod'). This is used to modify multiple input control streams at once.

Directory in which to look for 'file.pattern'. Notice, use either just 'file.mod' or both 'dir' and 'file.pattern'.

Path to input control stream to use in newfile

A path to a new control stream to write to (and don't edit contents of 'file.mod'). Default is to overwrite 'file.mod'.

Additional arguments to pass to NMwriteSection.

Path to a Nonmem control stream or output file from Nonmem (.mod or .lst)

A column with a unique value for each row. Such a column is recommended to use if possible. See merge.by.row and details as well. Default ("ROW") can be modified using NMdataConf.

Should the input data be added to the output data. Only column names that are not found in output data will be retrieved from the input data. Default is TRUE which can be modified using NMdataConf. See merge.by.row too.

If use.input=TRUE, this argument determines the method by which the input data is added to output data. The default method (merge.by.row=FALSE) is to interpret the Nonmem code to imitate the data filtering (IGNORE and ACCEPT statements), but the recommended method is merge.by.row=TRUE which means that data will be merged by a unique row identifier. The row identifier must be present in input and at least one full length output data table. See argument col.row too.

Include rows from input data files that do not exist in output tables? This will be added to the $row dataset only, and $run, $id, and $occ datasets are created before this is taken into account. A column called nmout will be TRUE when the row was found in output tables, and FALSE when not. Default is FALSE and can be configured using NMdataConf.

The input control stream file path. Default is to look for \"file\" with extension changed to .mod (PSN style). You can also supply the path to the file, or you can provide a function that translates the output file path to the input file path. The default behavior can be configured using NMdataConf. See dir.data too.

The data directory can only be read from the control stream (.mod) and not from the output file (.lst). So if you only have the output control stream, use dir.data to tell in which directory to find the data file. If dir.data is provided, the .mod file is not used at all.

Specification of the data file path. When this is used, the control streams are not used at all.

Default is TRUE, meaning that input data column names are translated according to $INPUT section in Nonmem listing file.

The default is to give some information along the way on what data is found. But consider setting this to TRUE for non-interactive use. Default can be configured using NMdataConf.

Prioritized input data file formats to look for and use if found. Default is c("rds","csv") which means rds will be used if found, and csv if not. fst is possible too. Default can be modified using NMdataConf().

List of arguments passed to when reading _input_ data. Notice that except for "input" and "file", you need to supply all arguments to fread if you use this argument. Default values can be configured using NMdataConf.

The default is to return data as a data.frame. Pass a function (say tibble::as_tibble) in as.fun to convert to something else. If data.tables are wanted, use as.fun="data.table". The default can be configured using NMdataConf.

The name of the subject ID variable, default is "ID".

The model name to be stored if col.model is not NULL. If not supplied, the name will be taken from the control stream file name by omitting the directory/path and deleting the .lst extension (path/run001.lst becomes run001). This can be a character string or a function which is called on the value of file (file is another argument to NMscanData). The function must take one character argument and return another character string. As example, see NMdataConf()$modelname. The default can be configured using NMdataConf.

A column of this name containing the model name will be included in the returned data. The default is to store this in a column called "model". See argument "modelname" as well. Set to NULL if not wanted. Default can be configured using NMdataConf.

A column of this name will be a logical representing whether row was in output table or not. Default can be modified using NMdataConf.

If tables are repeated, include a counter? It does not relate to the order of the $TABLE statements but to cases where a $TABLE statement is run repeatedly. E.g., in combination with the SUBPROBLEMS feature in Nonmem, it is useful to keep track of the table (repetition) number. If col.nmrep is TRUE, this will be carried forward and added as a column called NMREP. This is default behavior when more than one $TABLE repetition is found in data. Set it to a different string to request the column with a different name. The argument is passed to NMscanTables.

If TRUE (default), NMorderColumns is used to reorder the columns before returning the data. NMorderColumns will be called with alpha=FALSE, so columns are not sorted alphabetically. But standard Nonmem columns like ID, TIME, and other will be first. If col.row is used, this will be passed to NMorderColumns too.

If TRUE (default) and if input data is used, input control stream and input data are checked to be newer than output control stream and output tables. These are important assumptions for the way information is merged by NMscanData. However, if data has been transferred from another system where Nonmem was run, these checks may not make sense, and you may not want to see these warnings. The default can be configured using NMdataConf. For the output control stream, the time stamp recorded by Nonmem is used if possible, and if the input data is created with NMwriteData, the recorded creation time is used if possible. If not, and for all other files, the file modification times are used.

If supplied, the timezone to be used when reading the time stamp in the output control stream. Please supply something listed in OlsonNames(). Can be configured using NMdataConf() too.

Skip missing output table files with a warning? Default is FALSE in which case an error is thrown.

Deprecated. Use col.tableno.

Deprecated - use formats.read instead. If provided (though not recommended), this will overwrite formats.read, and only formats rds and csv can be used.

a .lst (output) or a .mod (input) control stream file. The filename does not need to end in .lst. It is recommended to use the output control stream because it reflects the model as it was run rather than how it is planned for next run. However, see file.mod and dir.data.

Prioritized input data file formats to look for and use if found. Default is c("rds","csv") which means rds will be used if found, and csv if not. fst is possible too. Default can be modified using NMdataConf().

The input control stream file path. Default is to look for \"file\" with extension changed to .mod (PSN style). You can also supply the path to the file, or you can provide a function that translates the output file path to the input file path. If dir.data is missing, the input control stream is needed. This is because the .lst does not contain the path to the data file. The .mod file is only used for finding the data file. How to interpret the datafile is read from the .lst file. The default can be configured using NMdataConf. See dir.data too.

The data directory can only be read from the control stream (.mod) and not from the output file (.lst). So if you only have the output file, use dir.data to tell in which directory to find the data file. If dir.data is provided, the .mod file is not used at all.

Specification of the data file path. When this is used, the control streams are not used at all.

If TRUE (default), IGNORE and ACCEPT statements in the Nonmem control streams are applied before returning the data. This affects what rows are returned, not columns.

If TRUE (default), data columns are named as interpreted by Nonmem (in '$INPUT').

recover columns that were not used in the Nonmem control stream? This means adding column from the input data file that are not used in '$INPUT'. If data file contains more columns than mentioned in '$INPUT', these will be named as in data file (if data file contains named variables). This affects what columns are returned, not rows.

If TRUE, metadata is added to output. In this case, you get a list. Typically, this is mostly useful if programming up functions which behavior must depend on properties of the output. See details.

The name of the subject ID column. Optional and only used to calculate number of subjects in data. Default is modified by NMdataConf.

The name of the row counter column. Optional and only used to check whether the row counter is in the data.

Default is to inform a little, but TRUE is useful for non-interactive stuff.

List of arguments passed to fread. Notice that except for "input" and "file", you need to supply all arguments to fread if you use this argument. Default values can be configured using 'NMdataConf()'.

If TRUE, the data rows that are dismissed by the Nonmem data filters (ACCEPT and IGNORE) and only this will be returned. Only used if 'apply.filters' is 'TRUE'.

Only affects meta data table. The model name to be stored if col.model is not NULL. If not supplied, the name will be taken from the control stream file name by omitting the directory/path and deleting the .lst extension (path/run001.lst becomes run001). This can be a character string or a function which is called on the value of file (file is another argument to NMscanData). The function must take one character argument and return another character string. As example, see NMdataConf()$modelname. The default can be configured using NMdataConf.

Only affects meta data table. A column of this name containing the model name will be included in the returned data. The default is to store this in a column called "model". See argument "modelname" as well. Set to NULL if not wanted. Default can be configured using NMdataConf.

The default is to return data as a data.frame. Pass a function (say tibble::as_tibble) in as.fun to convert to something else. If data.tables are wanted, use as.fun="data.table". The default can be configured using NMdataConf.

Deprecated - use apply.filters.

Deprecated - use formats.read instead. If provided (though not recommended), this will overwrite formats.read, and only formats rds and csv can be used.

File paths to the models (control stream) to edit. See file.pattern too.

The directory in which to find the models. Passed to list.files(). See file.pattern argument too.

The pattern used to match the filenames to read with NMscanData. Passed to list.files(). If dir is supplied and files is not (or is NULL), the default is ".*\.lst" which means all files ending in '.lst'. See dir argument too.

The default is to return data as a data.frame. Pass a function (say tibble::as_tibble) in as.fun to convert to something else. If data.tables are wanted, use as.fun="data.table". The default can be configured using NMdataConf.

Additional arguments passed to NMscanData.

the Nonmem file to read (normally .mod or .lst)

The default is to return data as a data.frame. Pass a function (say tibble::as_tibble) in as.fun to convert to something else. If data.tables are wanted, use as.fun="data.table". The default can be configured using NMdataConf.

The default is to give some information along the way on what data is found. But consider setting this to TRUE for non-interactive use. Default can be configured using NMdataConf.

col.nmrep If tables are repeated, include a counter? It does not relate to the order of the $TABLE statements but to cases where a $TABLE statement is run repeatedly. E.g., in combination with the SUBPROBLEMS feature in Nonmem, it is useful to keep track of the table (repetition) number. If col.nmrep is TRUE, this will be carried forward and added as a column called NMREP. This is default behavior when more than one $TABLE repetition is found in data. Set it to a different string to request the column with a different name. The argument is passed to NMscanTables.

Nonmem includes a counter of tables in the written data files. These are often not useful. However, if col.tableno is TRUE (not default), this will be carried forward and added as a column called NMREP. Even if NMREP is generated by NMscanTables, it is treated like any other table column in meta (?NMinfo) data.

name of the subject ID column. Used for calculation of the number of subjects in each table.

The name of the row counter column. Optional and only used to check whether the row counter is in the data.

If TRUE, metadata is added to output. In this case, you get a list. Typically, this is mostly useful if programming up functions which behavior must depend on properties of the output.

Skip missing output table files with a warning? Default is FALSE in which case an error is thrown.

If TRUE, tables are not read, only a table is returned showing what tables were found and some available meta information. Notice, not all meta information (e.g., dimensions) are available because the tables need to be read to derive that.

Only affects meta data table. The model name to be stored if col.model is not NULL. If not supplied, the name will be taken from the control stream file name by omitting the directory/path and deleting the .lst extension (path/run001.lst becomes run001). This can be a character string or a function which is called on the value of file (file is another argument to NMscanData). The function must take one character argument and return another character string. As example, see NMdataConf()$modelname. The default can be configured using NMdataConf.

Only affects meta data table. A column of this name containing the model name will be included in the returned data. The default is to store this in a column called "model". See argument "modelname" as well. Set to NULL if not wanted. Default can be configured using NMdataConf.

The dataset to stamp.

path to the script where the dataset was generated.

the time stamp to attach. Default is to use cpu clock.

other named metadata elements to add to the dataset. Example: Description="PK data for phase 1 trials in project".

the data to translate

the list file or control stream

logical. Do translation according to Nonmem code or not? If not, an overview of column names in data and in Nonmem code is still returned with the data.

recover columns that were not used in the NONMEM control stream? Default is TRUE. Can only be negative when translate=FALSE.

Suppress warnings about data columns?

The dataset to write to file for use in Nonmem.

The file to write to. The extension (everything after and including last ".") is dropped. csv, rds and other standard file name extensions are added.

character vector of formats.write. Default is c("csv","rds"). "fst" is possible too. Default can be modified with NMdataConf().

If provided, the object will be stamped with this script name before saved to rds or RData. See ?NMstamp.

A list of arguments to be passed to NMstamp.

List of arguments passed to fwrite. Notice that except for "x" and "file", you need to supply all arguments to fwrite if you use this argument. Default values can be configured using NMdataConf.

A list of arguments to be passed to saveRDS.

A list of arguments to be passed to save. Please note that writing RData is deprecated.

An optional list of arguments to be passed to write_fst.

The default is to give some information along the way on what data is found. But consider setting this to TRUE for non-interactive use. Default can be configured using NMdataConf.

List of arguments to pass to NMgenText - the function that generates text suggestion for INPUT and DATA sections in the Nonmem control stream. You can use these arguments to get a text suggestion you an use directly in Nonmem - and NMwriteSection can even update multiple Nonmem control streams based on the result. This will update your control streams to match your new data file with just one command.

If TRUE, csv file will be truncated horizontally (columns will be dropped) to match the $INPUT text generated for Nonmem (genText must be TRUE for this option to be allowed). This can be a great advantage when dealing with large datasets that can create problems in parallellization. Combined with write.rds=TRUE, the full data set will still be written to an rds file, so this can be used when combining output and input data when reading model results. This is done by default by NMscanData. This means writing a lean (narrow) csv file for Nonmem while keeping columns of non-numeric class like character and factor for post-processing.

Run and report results of NMgenText? Default is 'TRUE' if a csv file is written, otherwise 'FALSE'. You may want to disable this if data set is not for Nonmem.

Save defined files? Default is TRUE. If a variable is used to control whether a script generates outputs (say writeOutputs=TRUE/FALSE), if you use save=writeOutputs to comply with this.

Write to csv file? Deprecated, use 'formats.write' instead.

write an rds file? Deprecated, use 'formats.write' instead.

Deprecated and not recommended - will be removed. RData is not a adequate format for a dataset (but is for environments). Please use write.rds instead.

Deprecated, use args.NMgenText=list(drop=c("column")) instead.

Deprecated, use args.NMgenText=list(dir.data="your/path") instead.

Deprecated, use args.NMgenText=list(col.flagn="column.name"). Name of a numeric column with zero value for rows to include in Nonmem run, non-zero for rows to skip. The argument is only used for generating the proposed $DATA text to paste into the Nonmem control stream. To skip this feature, use 'col.flagn=FALSE'.

Deprecated, use args.NMgenText=list(rename=c(newname="existing")) instead.

Deprecated, use args.NMgenText=list(copy=c(newname="existing")) instead.

Deprecated, use args.NMgenText=list(capitalize=TRUE) instead.

Deprecated, use args.NMgenText=list(allow.char.TIME=TRUE) instead.

File paths to the models (control stream) to edit. See file.pattern too.

Alternatively to files, you can supply a regular expression which will be passed to list.files as the pattern argument. If this is used, use 'dir' argument as well. Also see data.file to only process models that use a specific data file.

If file.pattern is used, 'dir' is the directory to search in.

The name of the section to update with or without "$". Example: 'section="EST"' or 'section="$EST"' to edit the sections starting by '$EST'. Section specification is not case-sensitive. See '?NMreadSection' too.

The new text (including "$SECTION"). Better be broken into lines in a character vector since this is simply past to writeLines().

Named list of new sections, each element containing a section. Names must be section names, contents of each element are the new section lines for each section.

In combination with 'section', this determines where the new section is inserted. Possible values are "replace" (default), "before", "after", "first", "last".

path and filename to new run. If missing, the original file (from files or file.pattern) is overwritten (see the backup option below). If NULL, output is returned as a character vector rather than written.

In case you are overwriting the old file, do you want to backup the file (to say, backup_run001.mod)?

Append a blank line to output?

Use this to limit the scope of models to those that use a specific input data data file. The string has to exactly match the one in '$DATA' or '$INFILE' in Nonmem.

Default is to write to file. If write=FALSE, 'NMwriteSection()' returns the resulting input.txt without writing it to disk. Default is 'TRUE'.

The default is to give some information along the way on what data is found. But consider setting this to TRUE for non-interactive use. Default can be configured using 'NMdataConf()'.

If TRUE (default) and only one file is edited, the resulting rows are returned directly. If more than one file is edited, the result will always be a list with one element per file.

The parameter, as returned by 'NMreadExt()'

Name of the model name column.

A parameter table, like returned by 'NMreadExt()'.

The name of a column, as a string. Calculate the correlations within a grouping variable? This will often be a column containing the model name.

See '?NMdataConf'

The name of the column from which to take the 'OMEGA' values. Default is "value" in alignment with the output from 'NMreadExt()'.

Passed to addCor

The data set to add the variables to.

The name of the column with the subject identifier. All calculations are by default done by subject, so this column name must be provided. Default is controlled by '?NMdataConf()'.

Name of time column on which calculations of relative times will be based. Default it "TIME". Default is controlled by '?NMdataConf()'.

The name of the event ID column. This must exist in data. Default is EVID.

col.evid The name of the dose amount column. This must exist in data. Default is AMT.

Name of the time of previous dose column (created by addTAPD()). Default is "TPDOS". Set to NULL to not create this column.

Name of the time of previous dose column (created by addTAPD()). Default is "TAPD". Set to NULL to not create this column.

The name of the column to be created holding the previous dose amount. Set to NULL to not create this column.

The name of the column to be created holding the cumulative dose amount. Set to NULL to not create this column.

The name of the column (created by addTAPD) that holds the cumulative number of doses administered to the subject. Set to NULL to not create this column.

String to be prepended to all generated column names, that is each of col.tpdos, col.tapd, col.ndoses, col.pdosamt, col.doscuma that are not NULL.

String to be appended to all generated column names, that is each of col.tpdos, col.tapd, col.ndoses, col.pdosamt, col.doscuma that are not NULL.

A string that will be evaluated as a custom expression to identify relevant events. See subset.is.complete as well.

Only used in combination with non-missing subset.dos. By default, subset.dos is used in addition to the impact of col.evid (must be 1 or 4) and col.amt (greater than zero). If subset.is.complete=TRUE, subset.dos is used alone, and col.evid and col.amt are completely ignored. This is typically useful if the events are not doses but other events that are not expressed as a typical dose combination of EVID and AMT columns.

Order of events. This will only matter if there are simultaneous events of different event types within subjects. Typically if using nominal time, it may be important to specify whether samples at dosing times are pre-dose samples. The default is 'c(3,0,4,1,2)' - i.e. samples and simulations are pre-dose. See details.

Columns to do calculations within. Default is ID.

Scaling value for columns related to dose amount, relative to AMT values. col.pdosamt and col.doscuma are affected and will be derived as AMT/SDOSE.

Suppress messages? Default can be set using 'NMdataConf()'.

The default is to return data as a data.frame. Pass a function (say 'tibble::as_tibble') in as.fun to convert to something else. If data.tables are wanted, use 'as.fun="data.table"'. The default can be configured using NMdataConf.

Deprecated. Use col.doscumn instead.

The unquoted names that will become character values in the returned vector.

The name of the unique row identifier (say "ROW").

a list file or input control stream file path.

unique elements or vectors with unique elements

A vector of character strings to modify

Replace any number of consecutive blank spaces by a single blank. Default is TRUE.

Drop spaces before first non-empty character. Default is TRUE.

Drop spaces after last non-empty character. Default is TRUE.

See '?compareCols'

objects which element names to compare

As alternative to ..., you can supply the data sets in a list here.

If TRUE, the original dataset names are used in reported table. If not, generic x1, x2,... are used. The latter may be preferred for readability.

Do you just want a TRUE/FALSE to whether the names of the two objects are the same? Default is FALSE which means to return an overview for interactive use. You might want to use TRUE in programming. However, notice that this check may be overly rigorous. Many classes are compatible enough (say numeric and integer), and compareCols doesn't take this into account.

If TRUE, don't report columns where no difference found. Default is TRUE if number of data sets supplied is greater than one. If only one data set is supplied, the full list of columns is shown by default.

Columns of special interest. These will always be included in overview and indicated by a prepended * to the column names. This argument is often useful when you start by defining a set of columns that you want to end up with by combining a number of data sets.

the function that will be run on each column to check for differences. base::class is default. Notice that the alternative 'base::typeof' is different in certain ways. For instance, 'typeof' will not report a difference on numeric vs difftime. You could basically submit any function that takes a vector and returns a single value.

The default is to give some information along the way on what data is found. But consider setting this to TRUE for non-interactive use. Default can be configured using NMdataConf.

A function that will be run on the result before returning. If first input data set is a data.table, the default is to return a data.table, if not the default is to return a data.frame. Use whatever to get what fits in with your workflow. Default can be configured with NMdataConf.

Deprecated. Use keep.names instead.

Deprecated. Use test.equal instead.

elements as detected by 'NMreadInits()'

data set to transpose (widen)

left-hand side variables as character vector. Result will be long/vertical in these variables.

left-hand side variables as character vector. Result will be wide in these variables.

Additional arguments paseed to 'data.table::dcast()'.

The deprecated argument name (a character string).

The non-deprecated argument name (a character string).

List of arguments in the function call to look for oldarg and newarg. See '?getArgs'. If missing, 'getArgs()' will be called from within 'deprecatedArg'. See 'which' too.

If calling 'getArgs' this is passed along, referring to how many environments to jump to look at arguments.

data sets

As alternative to ..., you can supply the data sets in a list here.

If TRUE, the original dataset names are used in reported table. If not, generic x1, x2,... are used. The latter may be preferred for readability in some cases.

A function that will be run on the result before returning. If first input data set is a data.table, the default is to return a data.table, if not the default is to return a data.frame. Use whatever to get what fits in with your workflow. Default can be configured with NMdataConf.

Deprecated. Use keep.names instead.

A data.table with parameters. Must contain columns 'i' and 'j' with row and column indexes and 'est' with parameter (matrix) values.

Specifies whether pars contains only a lower or upper triangle of an assumed symmetric matrix (most often the case for variance-covariance matrices), or it contains the full matrix. 'dt.subset="unique"' (default) means that 'pars' only contains either upper or lower diagonal matrix (including diagonal), 'dt.subset="all"' means 'pars' contains both upper and lower triangles. See details.

By default, the maximum row number is derived as he maximum value in the 'i' column. If more (empty ones) are needed, specify the maximum row number with 'max.i'. This can be necessary in cases where only estimated elements are available but a full matrix including elements related to fixed parameters is needed.

Value to insert for missing elements

The name of the column from which to take the 'OMEGA' values. Default is "value" in alignment with the output from 'NMreadExt()'.

The data set to edit.

Pattern to search for in character columns. Passed to 'gsub()'. By default, 'gsub()' works with regular expressions. See ... for how to disable this if you want to replace a specific string.

pattern or string to replace with. Passed to 'gsub()'.

The default is to return data as a data.frame. Pass a function (say tibble::as_tibble) in as.fun to convert to something else. If data.tables are wanted, use as.fun="data.table". The default can be configured using NMdataConf.

Additional arguments passed to 'gsub()'. Especially, notice fixed=TRUE will disable interpretation of 'pattern' and 'replace' as regular expressions.

a data.table.

another data.table.

The default is to give some information along the way on what data is found. But consider setting this to TRUE for non-interactive use. Default can be configured using NMdataConf.

additional arguments passed to file.path().

data.frame in which to look for covariates

covariates will be searched for in combinations of values in these columns. Often by will be either empty or ID. But it can also be both say c("ID","DRUG") or c("ID","TRT").

Deprecated. Use by instead.

The default is to return a data.table if data is a data.table and return a data.frame in all other cases. Pass a function in as.fun to convert to something else. If data is not a data.table, the default can be configured using NMdataConf.

data.frame in which to look for covariates

optional covariates will be searched for in combinations of values in these columns. Often by will be either empty or ID. But it can also be both say c("ID","DRUG") or c("ID","TRT").

Deprecated. Use by instead.

The default is to return a data.table if data is a data.table and return a data.frame in all other cases. Pass a function in as.fun to convert to something else. If data is not a data.table, the default can be configured using NMdataConf.

The dataset to assign flags to.

A data.frame containing at least these named columns: FLAG, flag, condition. Condition is disregarded for FLAG==0. FLAG must be numeric and non-negative, flag and condition are characters.

An optional string that provides a subset of data to assign flags to. A common example is subset=\"EVID==0\" to only assign to observations. Numerical and character flags will be missing in rows that are not matched by this subset.

The name of the column containing the numerical flag values in tab.flags. This will be added to data. Default value is FLAG and can be configured using NMdataConf.

The name of the column containing the character flag values in tab.flags. This will be added to data. Default value is flag and can be configured using NMdataConf.

The flags are applied by either decreasing (default) or increasing value of col.flagn. Decreasing order means that conditions associated with higher values of col.flagn will be evaluated first. By using decreasing order, you can easily adjust the Nonmem IGNORE statement from IGNORE(FLAG.NE.0) to say IGNORE(FLAG.GT.10) if BLQ's have FLAG=10, and you decide to include these in the analysis.

Column(s) that distinct incompatible subsets of data. Default is "EVID" meaning that if different values of EVID are found in data, the function will return an error. This is a safeguard not to mix data unintentionally when counting flags.

The character flag to assign to rows that are not matched by exclusion conditions (numerical flag 0).

The default is to return data.tables if input data is a data.table, and return a data.frame for all other input classes. Pass a function in as.fun to convert to something else. If return.all=FALSE, this is applied to data and tab.flags independently.

The dataset including both FLAG and flag columns.

A data.frame containing at least these named columns: FLAG, flag, condition. Condition is disregarded for FLAG==0.

A file to write the table of flag counts to. Will probably be removed and put in a separate function.

The name of the subject ID column. Default is "ID".

The name of the column containing the numerical flag values in tab.flags. This will be added to data. Use the same as when flagsAssign was called (if that was used). Default value is FLAG and can be configured using NMdataConf.

The name of the column containing the character flag values in data and tab.flags. Use the same as when flagsAssign was called (if that was used). Default value is flag and can be configured using NMdataConf.

An optional column to group the counting by. This could be "STUDY", "DRUG", "EVID", or a combination of multiple columns.

The flags are applied by either decreasing (default) or increasing value of col.flagn. By using decreasing order, you can easily adjust the Nonmem IGNORE statement from IGNORE(FLAG.NE.0) to say IGNORE(FLAG.GT.10) if BLQ's have FLAG=10, and you decide to include these in the analysis.

The character flag to assign to rows that are not matched by exclusion conditions (numerical flag 0).

What to call the total set of data before applying exclusion flags. Default is "All available data".

Column(s) that distinct incompatible subsets of data. Default is "EVID" meaning that if different values of EVID are found in data, the function will return an error. This is a safeguard not to mix data unintentionally when counting flags.

Save file? Default is TRUE, meaning that a file will be written if file argument is supplied.

Suppress non-critical messages? Default is 'FALSE'.

The default is to return a data.table if input data is a data.table, and return a data.frame for all other input classes. Pass a function in as.fun to convert to something else. If data is not a data.table, default can be configured using NMdataConf.

The file name or file names to modify.

A character string or a numeric to add to the file name

In case x is numeric, a number of zeros to pad before the appended number. This is useful if you are generating say more than 10 files, and your counter will be 01, 02,.., 10,... and not 1, 2,...,10,...

The separator between the existing file name (until extension) and the addition.

If 'x' is of length greater than 1, the default is to collapse the elements to a single string using 'sep' as separator. See the 'collapse' argument to '?paste'. If you want to treat them as separate strings, use 'collapse=NULL' which will lead to generation of separate file names. However, currently 'fn' or 'x' must be of length 1.

Allow 'fn' to be string(s) without extensions? Default is 'FALSE' in which case an error will be thrown if 'fn' contains strings without extensions. If 'TRUE', 'x' will be appended to fn in these cases.

file name. Often ending in an extension after a period but the extension is not needed.

new file name extension. If omitted or NULL, the extension of fn is returned.

Function call as provided by sys.call().

Environment in which to evaluate the arguments.

character vector of full file paths. Specify either files or both file.pattern and dir.

A regular expression to look for in dir. If used, dir must also be supplied.

The directory i which to look for file.pattern. dir is passed to list.files as pattern. If supplied, file.pattern must also be supplied.

The default is not to be quiet.

A file path to a a text file to read.

Text lines if file was already read.

If the 'file' argument is used and if 'as.one' is TRUE, the file(s) are read and put into a 'data.table' with a model column and a 'text' column. Default is FALSE. Be careful with this, as it returns different formats whether the 'file' or the 'lines' argument is used.

The elements object produced by 'NMreadInits()'.

Any object

The data to look into.

The columns to look for missings in.

If supplied, we are keeping track of the missings within the values of the by columns. In summary, by is included too.

Strings that should be interpreted as missing. All spaces will be removed before we compare to na.strings. The default is c("",".") so say " . " is a missing by default.

Keep quiet? Default is not to.

A function that will be run on the result before returning. If first input data set is a data.table, the default is to return a data.table, if not the default is to return a data.frame. Use whatever to get what fits in with your workflow. Default can be configured with NMdataConf.

path to output control stream

The time zone of the time stamp from Nonmem. The default ("as.is") is to try to extract it or take it from the system on which this function is run. See details.

A matrix

Either '"lower"' (default) or '"upper"', or '"all"' for which triangle to return. '"lower"' and '"upper"' are equivalent for covariance or correlation matrices but the returned indexes will differ. '"all"' will return the full matrix which mostly makes sense if matrix is not a covariance or correlation matrix.

See '?NMdataConf'

A data.frame with the number of rows must should be obtained from the merge. The resulting data.frame will be ordered like x.

A data.frame that will be merged onto x.

The column(s) to merge by. Character string (vector). by or by.x and by.y must be supplied.

If the columns to merge by in x and y are named differently. by or by.x and by.y must be supplied.

If the columns to merge by in x and y are named differently. by or by.x and by.y must be supplied.

If common columns are found in x and y, and they are not used in 'by', this will by default create columns named like col.x and col.y in result (see ?merge). Often, this is a mistake, and the default is to throw a warning if this happens. If using 'mergeCheck' in programming, you may want to make sure this is not happening and use common.cols=stop. If you want nothing to happen, you can do common.cols=NULL. You can also use 'common.cols="drop.x"' to drop "non-by" columns in 'x' with identical column names in 'y'. Use "drop.y" to drop them in 'y' and avoid the conflicts. The last option is to use 'common.cols="merge.by"' which means 'by' will automatically be extended to include all common column names.

If you want to include a check of the number of columns being added to the dimensions of 'x'. So if ncols.expect=1, the resulting data must have exactly one column more than 'x' - if not, an error will be returned.

If using 'mergeCheck' inside other functions, it can be useful to use track.msg=TRUE. This will add information to messages/warnings/errors that they came from 'mergeCheck()'.

If FALSE, the names of the added columns are reported. Default value controlled by NMdataConf.

Deprecated. Use x.

Deprecated. Use y.

Not implemented.

If NA's are found in (matched) by columns in both x and why, what should we do? This could be OK, but in many cases, it's because something unexpected is happening. Use fun.na.by=NULL if you don't want to be notified and want to go ahead regardless.

The default is to return a data.table if x is a data.table and return a data.frame in all other cases. Pass a function in as.fun to convert to something else.

Deprecated. Please use 'common.cols'.

additional arguments passed to data.table::merge. If all is among them, an error will be returned.

parameters to pass to strwrap

The function to pass the text through. Typically, message, warning, or stop. If NULL, nothing will happen, and NULL is invisibly returned.

Passed to strwrap. Default is "\n".

Passed to strwrap. Default is an empty string.

Passed to strwrap. Default is 80.

If TRUE, the name of the function throwing the message/warning/error is mentioned. This is not default but useful when using function inside other functions.

a data.table or something to be converted to a data.table.

passed to print.data.table.

The summary object to be printed. See ?summary.NMdata

Arguments passed to other print methods.

Object from NMscanTables

The name of the column holding logical of whether row was found in output tables.

data.frame in which to rename columns

Function that returns TRUE for columns to be renamed.

Function that takes the existing column name and returns the new one.

Rename those where FALSE is returned from fun.test.

The default is to return data as a data.frame. Pass a function (say tibble::as_tibble) in as.fun to convert to something else. If data.tables are wanted, use as.fun="data.table". The default can be configured using NMdataConf.

a .lst (output) or a .mod (input) control stream file. The filename does not need to end in .lst. It is recommended to use the output control stream because it reflects the model as it was run rather than how it is planned for next run. However, see file.mod and dir.data.

The input control stream file path. Default is to look for \"file\" with extension changed to .mod (PSN style). You can also supply the path to the file, or you can provide a function that translates the output file path to the input file path. If dir.data is missing, the input control stream is needed. This is because the .lst does not contain the path to the data file. The .mod file is only used for finding the data file. How to interpret the datafile is read from the .lst file. The default can be configured using NMdataConf. See dir.data too.

The data directory can only be read from the control stream (.mod) and not from the output file (.lst). So if you only have the output file, use dir.data to tell in which directory to find the data file. If dir.data is provided, the .mod file is not used at all.

Specification of the data file path. When this is used, the control streams are not used at all.

If TRUE (default), data columns are named as interpreted by Nonmem (in $INPUT). If data file contains more columns than mentioned in $INPUT, these will be named as in data file (if data file contains named variables).

List of arguments passed to fread. Notice that except for "input" and "file", you need to supply all arguments to fread if you use this argument. Default values can be configured using NMdataConf.

The name of the subject ID column. Optional and only used to calculate number of subjects in data. Default is modified by NMdataConf.

row-level data

An NMdata object (from NMscanData).

Only passed to the summary generic if object is missing NMdata meta data (this should not happen anyway).

The dataset to find a new element name for

Character vector of names that must not be matched. Only one of data and names can be supplied.

The base name of the new element. A number will appended to this string that will ensure that the new element name is not already in use.

Where to start the search for a smallest available index number to add to 'base' if necessary.

Maximum number of iterations on element name.

If base isn't in use already, use it without a digit appended?

The length of the diagonal. Same as number of rows or columns.

The expression to catch errors, warnings, messages from

Catch messages? Default is 'FALSE'.

Catch warnings? Default is 'TRUE'.

Catch errors? Default is 'TRUE'.

An 'NMdata' object.

A vector, either numeric or character.

Require one unique value? If 'TRUE' (default), an error is thrown if non-unique values found. If 'FALSE', all the unique values are returned.

In addition to NA-elements, what text strings should be considered missing? Default is empty strings and strings only containing white spaces ('na.pattern="^ *$"').

A data set

The meta data to attach

If FALSE, the existing meta data will be removed. If TRUE, metadata will be appended to existing metadata. However, this will not work recursively.

Should always be TRUE.