- Restriktor is an open-source project, which means that the R code is no black-box and available online at GitHub.

Restriktor is developed for applied users. This means that we have tried to come up with a user-friendly constraint syntax. In R, categorical predictors are represented by 'factors'. For example, the ‘Group’ variable with three factor levels: 'Low', 'Medium', and 'High'. Then, the constraints can be specified using the factor level names. In case of a continuous variable the constraints can be specified using the variable name. For example, the categorical variable 'Group' and one continuous predictor 'x1' the constraint syntax might look as follows:

`myConstraints <- ' GroupLow < GroupMedium GroupMedium < GroupHigh x1 > 0 '`

In addition, we have tried to provide all the necessary tools and output to evaluate the order-constrained hypothesis. The main tools are the

`restriktor()`

function and the`iht()`

function. The restriktor() function is used for estimating the restricted estimates and the iht() function is for testing the informative hypothesis. For example, the output of the restriktor() function might look as follows:`Restriktor: restricted linear model: Residuals: Min 1Q Median 3Q Max -2.877222 -0.773776 0.005096 0.755305 2.978666 Coefficients: Estimate Std. Error t value Pr(>|t|) group1 0.945747 0.075464 12.533 < 2.2e-16 *** group2 0.945747 0.075464 12.533 < 2.2e-16 *** group3 1.058034 0.106722 9.914 < 2.2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 1.0672 on 297 degrees of freedom Standard errors: standard Multiple R-squared reduced from 0.4656 to 0.4623 Generalized Order-Restricted Information Criterion: Loglik Penalty Goric -443.6900 2.8333 893.0466`

- Support for linear inequality constraints, linear equality constraints, or a combination of both.
- Support for the robust estimation of the linear model (rlm).
- Support for the generalized linear model (glm).
- (Robust) Standard error under the constraints (sandwich package).
- Bootstrapped standard errors (standard and model-based).
- Hypothesis tests: global/omnibus, Type A, Type B, and Type C.
- Various test-statistics: F/Wald, score and likelihood ratio.
- Parametric and model-based bootstrapped p-values.
- Two methods to compute the chi-square-bar mixing weights: (1) based on the multivariate normal distribution function with additional Monte Carlo steps, or (2) based entirely on Monte Carlo simulation.
- Model selection under (in)equality constraints (GORIC).
- Evaluating an order-constrained hypothesis against its complement using the GORIC.