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MixFrac - Fractional Factorial Designs with Alias and Trend-Free Analysis

Constructs mixed-level and regular fractional factorial designs using coordinate-exchange optimization and automatic generator search. Design quality is evaluated with J2 and balance (H-hat) criteria, alias structures are computed via correlation-based chaining, and deterministic trend-free run orders can be produced following Coster (1993) <doi:10.1214/aos/1176349410>. Mixed-level design construction follows the NONBPA approach of Pantoja-Pacheco et al. (2021) <doi:10.3390/math9131455>. Regular fraction identification follows Guo, Simpson and Pignatiello (2007) <doi:10.1080/00224065.2007.11917691>. Alias structure computation follows Rios-Lira et al.(2021) <doi:10.3390/math9233053>.

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2.70 score 129 downloads

MFRCD - Optimal Row-Column Designs for Asymmetrical Factorial Experiments

Constructs and analyzes optimal row-column designs for mixed-level factorial experiments under square and rectangular field layouts. For square field layouts, the package implements direct common-factor constructions by first forming two component treatment arrays, one for each factor or super-factor, and then combining them through a symbolic cell-wise product following Gopinath, Parsad and Mandal (2018) <doi:10.1080/03610926.2017.1376091>. For rectangular field layouts, the package constructs designs by extracting a balanced principal block from a mixed-level block design, treating it as the principal column, taking the complete treatment set as the principal row, and generating the full row-column design by cyclic modular development. The package also includes repair utilities for improving disconnected or partially connected row-column designs through bounded treatment-swap searches while preserving the row-column layout structure. The package provides diagnostic tools for connectedness, orthogonal factorial structure, balance, estimability, and selected optimality criteria for row-column designs.

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1.30 score 66 downloads

lfebd3 - Generation and Analysis of 3-Level and 5-Level Factorial Block Designs

Provides tools to generate and analyze 3-level and 5-level linear factorial block designs, including complete factorial layouts, fractional factorial layouts, confounded factorial layouts, and design-characteristic summaries. The package includes utilities for recursive construction, defining-contrast identification, alias and confounding summaries, incidence matrix construction, and selected design-characteristic diagnostics. The methodological framework follows foundational work on factorial block designs, including Gupta (1983) <doi:10.1111/j.2517-6161.1983.tb01253.x>.

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1.30 score 228 downloads

GRCFE - Generate Optimal Row-Column Factorial Experiments

Provides tools for constructing row-column factorial experiment layouts for the estimation of main effects and two-factor interactions in factorial and fractional factorial experiments. The package implements generator-matrix based design construction methods motivated by 2fi-optimal row-column designs, where all main effects are estimable and as many two-factor interactions as possible are unconfounded; see Zhang, Pan and Shi (2025) <doi:10.1016/j.jspi.2024.106192>. It also includes theorem-based constructions, heuristic D-optimal search routines for unsupported or composite-level cases, utilities for building generator matrices, and diagnostic functions for evaluating aliasing and estimability properties of the generated designs.

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1.00 score 406 downloads

FactChar - Characterization and Diagnostic Tools for Factorial Block Designs

Description: Provides comprehensive tools for analysing and characterizing mixed-level factorial designs arranged in blocks. Includes construction and validation of incidence structures, computation of C-matrices, evaluation of A-, D-, E-, and MV-efficiencies, checking of orthogonal factorial structure (OFS), diagnostics based on Hamming distance, discrepancy measures, B-criterion, Es^2 statistics, J2-distance and J2-efficiency, Phi-p optimality, and symmetry conditions for universal optimality. The methodological framework follows foundational work on factorial and mixed-level design assessment by Xu and Wu (2001) <doi:10.1214/aos/1013699993>, and Gupta (1983) <doi:10.1111/j.2517-6161.1983.tb01253.x>. These methods assist in selecting, comparing, and studying factorial block designs across a range of experimental situations.

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1.00 score 105 downloads

mixedfact - Generate and Analyze Mixed-Level Blocked Factorial Designs

Generates blocked designs for mixed-level factorial experiments for a given block size. Internally, it uses finite-field based, collapsed, and heuristic methods to construct block structures that minimize confounding between block effects and factorial effects. The package creates the full treatment combination table, partitions runs into blocks, and computes detailed confounding diagnostics for main effects and two-factor interactions. It also checks orthogonal factorial structure (OFS) and computes efficiencies of factorial effects using the methods of Nair and Rao (1948) <doi:10.1111/j.2517-6161.1948.tb00005.x>. When OFS is not satisfied but the design has equal treatment replications and equal block sizes, a general method based on the C-matrix and custom contrast vectors is used to compute efficiencies. The output includes the generated design, finite-field metadata, confounding summaries, OFS diagnostics, and efficiency results.

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1.00 score 139 downloads

blockedFF - Generation of Blocked Fractional Factorial Designs (Two-Level and Three-Level)

Provides computational tools to generate efficient blocked and unblocked fractional factorial designs for two-level and three-level factors using the generalized Minimum Aberration (MA) criterion and related optimization algorithms. Methodological foundations include the general theory of minimum aberration as described by Cheng and Tang (2005) <doi:10.1214/009053604000001228>, and the catalogue of three-level regular fractional factorial designs developed by Xu (2005) <doi:10.1007/s00184-005-0408-x>. The main functions dol2() and dol3() generate blocked two-level and three-level fractional factorial designs, respectively, using beam search, optimization-based ranking, confounding assessment, and structured output suitable for complete factorial situations.

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1.00 score 224 downloads

rcd3 - Efficient Row-Column Designs for 3 Level Factorial Experiments in 3 Rows

Provides functions to construct efficient row-column designs for 3-level factorial experiments in 3 rows. The designs ensure the estimation of all main effects (full efficiency) and two factor interactions in minimum replications. For more details, see Dey, A. and Mukerjee, R. (2012) <doi:10.1016/j.spl.2012.06.014> and Dash, S., Parsad, R., and Gupta, V. K. (2013) <doi:10.1007/s40003-013-0059-5>.

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1.00 score 175 downloads

bdf3 - Efficient Block Designs for 3-Level Factorial Experiments in Block Size 3

Provides functions to construct efficient block designs for 3-level factorial experiments in block size 3. The designs ensure the estimation of all main effects and two-factor interactions in minimum number of replications. For more details, see Dey and Mukerjee (2012) <doi:10.1016/j.spl.2012.06.014> and Dash, S., Parsad, R. and Gupta, V.K. (2013) <doi:10.1007/s40003-013-0059-5>.

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1.00 score 512 downloads