| atkins | Assuming a linear model for the response, allocate treatment... |
| calc.A | Given a design matrix for a linear model, compute the... |
| calc.bias | Given a design, the current estimate of beta (or true value... |
| calc.D | Given a design matrix for a linear model, compute the... |
| calc.DA | Given a design matrix for a linear model, compute the D-A... |
| calc.G | Given a design matrix for a linear model, compute the... |
| calc.Gint | Given a design matrix for a linear model wuth... |
| calc.logit.wDA | Compute the DA-optimal objective function assuming a logistic... |
| calc.logit.wL | Compute the L-optimal objective function assuming a logistic... |
| calc.loss | Given a design matrix for a linear model, compute the loss |
| calc.mse | Given a design, the current estimate of beta (or true value... |
| calc.tz | Given a design matrix for a linear model, calculate the... |
| calc.vcov | Given a design, the current estimate of beta (or true value... |
| calc.wDA | Compute the DA-optimal objective function assuming a linear... |
| calc.wL | Compute the L-optimal objective function assuming a linear... |
| calc.y.A | Given an information matrix and assuming logistic regression,... |
| calc.y.D | Given an information matrix and assuming logistic regression,... |
| calc.y.DA | Given an information matrix and assuming logistic regression,... |
| calc.y.G | Given an information matrix and assuming logistic regression,... |
| 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.coord | 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.pseudo.t | Calculate average D-optimal criterion assuming a logistic... |
| Dopt.y.t | Given a design matrix with an additional row, and assuming... |
| Dopt.y.t.init | Given an initial design and assuming logistic regression,... |
| 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 objective function by cases... |
| 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 treatments... |
| 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 | Compute the L-optimal objective function assuming a logistic... |
| wLopt.t.init | Compute the L-optimal objective function assuming a logistic... |
| zpfunc | Given z.probs which takes a specific form, convert it to an... |
