Description Usage Arguments Details Value

`makeR`

stands for making/generating/simulating responses from
a LBA model. `make_r`

and `make.r`

use C++ function. These
make `r`

, `_r`

, `.r`

functions are essentially `rLBA`

,
including `rlba_norm`

. They uses a LBA model with parameters, b, A,
mean_v, sd_v and t0 (no st0) to generate choice RT random deviates.

1 2 |

`drifts` |
a n x n_v drift rate matrix. It can be a vector with 2 or more elements. n is the numbers of observation. n_v is the numbers of response/accumulator. |

`n` |
numbers of observation/model simulations. This must be a scalar. |

`b` |
decision threshold, a vector or a scalar. |

`A` |
start point upper bound, a vector of a scalar. |

`n_v` |
numbers of response/accumulator, an integer. Note n_v must match
the length/size of |

`t0` |
nondecision time, a vector or a scalar. |

`st0` |
nondecision time variation, a vector of a scalar. It is the upper bound of a uniform distribution for t0 variability. |

`seed` |
an integer specifying if and how the random number generator should be initialized. |

`return_ttf` |
a boolean switch indicating if return RTs for all
accumulators. When |

`make_v`

draws drift rate from normal or truncated normal distribution.
Each trial is stored as a row and each column is a drift rate for an
accumulator. You need to transpose drift rates generated by make_v for
`makeR`

.

`make.r`

is a wrapper function of `make_r`

. You may
need to use ":::" to call make.r, because of S3 method naming convention. If
you call `make_r`

directly, beware it returns C index and is only a
numeric matrix. It does not carry a string vector for the column names, RTs
and responses. See timing test to see why it might be a good idea not to
return it as a data frame. `rlbaCnorm`

is R version of correlated LBA
model.

`rlba_norm`

adds checks and calls `make_v`

and `make_r`

.
`rlba_norm`

is only slightly quicker than `make_r`

.

`n1PDFfixedt0`

is defective density function for the fisrt node LBA
model. Defective means its probability does not normally normalize to 1.
Only the probabilities from all nodes/accumulators add to 1.
`n1PDFfixedt0`

is equation (3) on page 159 in Brown and
Heathcote (2008). This equation assumes st0 is 0.

`fptcdf`

and `fptpdf`

are distribution and density functions with
four parameters A, b, v and sv, assuming t0 is zero. `fptcdf`

and
`fptpdf`

are respectively equation (1) and equation (2) on page 159 in
Brown and Heathcote (2008).

`make_r`

gives either a time-to-finish (ttf) matrix or a n x 2
matrix, storing RTs (first column) and responses (second column). `n`

equals to number of model simulations. ttf is a n_v x n matrix with RTs from
all accumulators.

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