Finds Locally D-optimal designs for Inverse Quadratic regression model which is defined as E(y)=ax/(b+x+cx^2) or E(y)=x/(a+bx+cx^2) with Var(y) = σ^2, where a, b, c and σ are unknown parameters.
Finds Locally D-optimal designs for Michaelis-Menten model which is defined as E(y) = (ax)/(1+bx) or E(y) = (ax)/(b+x) or E(y) = "x/(a+bx) with Var(y) = σ^2, where a, b and σ are unknown parameters.
Auto-constructs Frechet derivative of D-criterion at M(ξ, β) and in direction M(ξ_x, β) where M is Fisher information matrix, β is vector of parameters, ξ is the interested design and ξ_x is a unique design which has only a point x. The constructed Frechet derivative is an R function with argument x.
Finds Locally D-optimal designs for Poisson and Poisson dose-response models which are defined as E(y) = exp(a+bx) and E(y) = aexp(-bx) with Var(y) = E(y), respectively, where a and b are unknown parameters.
Finds Locally D-optimal designs for Logistic and Logistic dose-response models which are defined as E(y) = 1/(1+exp(-a-bx)) and E(y) = 1/(1+exp(-b(x-a))) with Var(y) = E(y)(1-E(y)), respectively, where a and b are unknown parameters.
Finds Locally D-optimal designs for Richards regression model which is defined as E(y) = a/(1+bexp(-λ*x))^h with Var(y) = σ^2, where a, b, λ, h and σ are unknown parameters.
Finds Locally D-optimal designs for Negative Binomial regression model which is defined as E(y) = λ(x) with Var(y) = σ^2λ(x)(1+(λ(x)/θ)), where y ~ NB(θ, λ(x)), λ(x) = aexp(-bx) and a, b and σ are unknown parameters.
Finds Locally D-optimal designs for Weibull regression model which is defined as E(y) = a-bexp(-λ*x^h) with Var(y) = σ^2, where a, b, λ, h and σ are unknown parameters.
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