atkins | Assuming a linear model for the response, allocate treatment... |

calc.A | Given a design matrix, calculate the A optimality for a... |

calc.bias | Given a design, the current estimate of beta (or true value... |

calc.D | Given a design matrix, calculate the D optimality for a... |

calc.DA | Given a design matrix, calculate the D-A optimality for a... |

calc.G | Given a design matrix, calculate the G optimality for a... |

calc.logit.wDA | Calculate DA-optimal criterion assuming a logistic model... |

calc.logit.wL | Calculate L-optimal criterion assuming a logistic model (with... |

calc.loss | Given a design matrix, calculate the loss for a linear model |

calc.mse | Given a design, the current estimate of beta (or true value... |

calc.tz | Given a design matrix, calculate the covariate balance for a... |

calc.vcov | Given a design, the current estimate of beta (or true value... |

calc.wDA | Calculate DA-optimal criterion assuming a linear model (with... |

calc.wL | Calculate L-optimal criterion assuming a linear model (with... |

calc.y.D | Compute the D-optimality criterion of the information matrix... |

calc.y.DA | Compute the DA-optimality criterion of the information matrix... |

calc.y.G | Compute the G-optimality criterion of the information matrix... |

coord.cr | Allocate treatment using a coordinate exchange algorithm with... |

coordex | Allocate treatment using coordinate exchange algorithm,... |

cr.bayes.des | Assuming a Bayesian linear model for the response with a... |

cr.des | Assuming a linear model for the response, allocate treatment... |

cr.des.pseudo | Allocate treatments according to a (weighted) L-optimal... |

cr.exp.loss | Break down expected future optimality into two components: 1)... |

cr.future | Calculate (weighted) L-optimal expected optimality for a... |

cr.future.loss | Assuming the currrent response, future covariate value and... |

cr.future.y | Assuming a current response, break down the expected future... |

cr.logit.cont | Assuming a logistic model for the response, allocate... |

cr.logit.des | Assuming a logistic model for the response, allocate... |

cr.rand | Assuming a logistic model for the response, allocate... |

cr.simfuture.logis | Allocate treatments according to an information matrix based... |

cr.simfuture.logis.cont | Allocate treatments according to weighted L-optimal objective... |

discrete | discretize a vector of continuous values into binary (0 and... |

Dopt.y.t | Compute the D-optimality criterion of the information matrix... |

Dopt.y.t.init | Compute the D-optimality criterion of of an initial design... |

efron | Allocate treatment basead on Efron's biased coin |

exp.loss | Break down expected future optimality into two components: 1)... |

exp.loss.k | Break down the expected future optimality by cases for every... |

future | Calculate expected optimality for a given trajectory using... |

future.coordex | Calculates expected optimality for a given trajectory after... |

future.coordex.logis | Calculate expected optimality for a given trajectory using... |

future.logis | Calculate expected optimality for a given trajectory using... |

future.logis.cont | Calculate expected optimality for a given trajectory using... |

future.loss | Assuming the currrent response, future covariate value and... |

future.loss.k | Assuming the currrent response, future covariate value and... |

future.y | Assuming a current response, break down the expected future... |

gencov | Create a set of binary covariates with a specific covariate... |

Imat.beta | Compute the information matrix, given beta, for logistic... |

learn.zprobs | Find the empirical distribution of the covariates |

linear.nonmyop | Allocate treatments according to an information matrix based... |

linear.nonmyop.dyn | Allocate treatments according to an information matrix based... |

linear.rand | Assuming a linear model for the response, allocate treatment... |

logit.cont | Assuming a logistic model for the response, allocate a... |

logit.coord | Assuming a logistic model for the response, allocate... |

logit.des | Assuming a logistic model for the response, allocate... |

logit.Lbnon | Allocate treatments according to a weighted L-optimal... |

logit.mse | Allocate treatments according to the MSE matrix when a... |

logit.nonmy | Allocate treatments according to an information matrix based... |

logit.rand | Assuming a logistic model for the response, allocate... |

max.imb | Allocate treatment based on Hu and Hu (2012)'s version of... |

min.classic | Classic version of Minimization, appropriate for binary... |

min.cont | Allocate treatment using a version of minimization,... |

min.gen | Allocate treatment based on an imbalance criterion - most... |

min.sen | Allocate treatment using Senn's version of Minimization,... |

probi | Compute P(y=1) for logistic regression |

rand | Randomly allocate treatment |

shadeplot | Plot distribution |

simfuture | Allocate treatments according to an information matrix based... |

simfuture.logis | Allocate treatments according to an information matrix based... |

simfuture.logis.cont | Allocate continuous treatments according to an information... |

wLopt.pseudo.t | Calculate average L-optimal criterion assuming a logistic... |

wLopt.t | Calculate L-optimal criterion assuming a logistic model (with... |

wLopt.t.init | Calculate L-optimal criterion assuming a logistic model (with... |

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