It is used to construct run sequences with minimum changes for half replicate of two level factorial run order. Experimenter can save time and resources by minimizing the number of changes in levels of individual factor and therefore the total number of changes. It consists of the function minimal_hrtlf(). This technique can be employed to any half replicate of two level factorial run order where the number of factors are greater than two. In Design of Experiments (DOE) theory, two level of a factor can be represented as integers e.g. - 1 for low and 1 for high. User is expected to enter total number of factors to be considered in the experiment. minimal_hrtlf() provides the required run sequences for the input number of factors.  The output also gives the number of changes of each factor along with total number of changes in the run sequence. Due to restricted randomization the minimally changed run sequences of half replicate of two level factorial run order will be affected by trend effect. The output also provides the Trend Factor value of the run order. Trend factor value will lies between 0 to 1. Higher the values, lesser the influence of trend effects on the run order.

The presence of outliers in a dataset can substantially bias the
    results of statistical analyses. To correct for outliers, micro edits are 
    manually performed on all records. A set of constraints and decision rules
    is typically used to aid the editing process. However, straightforward
    decision rules might overlook anomalies arising from disruption of linear
    relationships. Computationally efficient methods are provided to 
    identify historical, tail, and relational anomalies at the data-entry 
    level (Sartore et al., 2024; <doi:10.6339/24-JDS1136>). A score statistic
    is developed for each anomaly type, using a distribution-free approach
    motivated by the Bienaymé-Chebyshev's inequality, and fuzzy logic is used
    to detect cellwise outliers resulting from different types of anomalies.
    Each data entry is individually scored and individual scores are combined
    into a final score to determine anomalous entries. In contrast to fuzzy
    logic, Bayesian bootstrap and a Bayesian test based on empirical 
    likelihoods are also provided as studied by Sartore et 
    al. (2024; <doi:10.3390/stats7040073>). These algorithms allow for a more 
    nuanced approach to outlier detection, as it can identify outliers at 
    data-entry level which are not obviously distinct from the rest of the 
    data.
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    This research was supported in part by the U.S. Department of Agriculture,
    National Agriculture Statistics Service. The findings and conclusions in
    this publication are those of the authors and should not be construed to
    represent any official USDA, or US Government determination or policy.