vinaymandya123-collab.r-universe.dev

[vinaymandya123-collab] NeutroIBDAnalysis 0.1.2

vinaymandya123@gmail.com (Vinaykumar L.N.) — Sat, 26 Sep 2026 06:10:02 GMT

Provides methods for neutrosophic analysis of variance (NANOVA) and neutrosophic analysis of covariance (NANCOVA) for interval-valued data arising from incomplete block design experiments. The package supports balanced incomplete block designs (BIBDs), partially balanced incomplete block designs (PBIBDs), and lattice designs. Functions are included for treatment comparisons, least significant difference (LSD) tests, and interval-based statistical inference under neutrosophic environments.

[vinaymandya123-collab] NeutroCODsAnalysis 0.2.1

vinaymandya123@gmail.com (Vinaykumar L.N.) — Thu, 24 Sep 2026 08:30:02 GMT

Provides methods for Neutrosophic Analysis of Variance (NANOVA) and Neutrosophic Analysis of Covariance (NANCOVA) for crossover designs, as well as NANOVA for multi-session designs with direct and residual effects using interval-valued observations. For crisp data, users can enter identical lower and upper values for the response and covariate variables to obtain results equivalent to classical Analysis of Variance (ANOVA) and Analysis of Covariance (ANCOVA), respectively. The basic concepts of neutrosophic statistics are based on Smarandache (2014) , while the analysis procedures implemented in this package are newly developed.

[vinaymandya123-collab] TCpRepDesigns 0.0.2

vinaymandya123@gmail.com (Vinaykumar L.N.) — Thu, 20 Aug 2026 11:52:04 GMT

Provides functions for generating partially replicated (p-rep) test-control designs for early generation varietal trials conducted across multiple environments. The package implements three construction methods for obtaining p-rep test-control designs with one or more control treatments. The package extends the partially replicated design framework of Vinaykumar et al. (2026) to accommodate test-control comparisons in breeding trials. Functions are provided for generating randomized and non-randomized layouts and for displaying the design parameters and treatment allocations for each environment. The proposed designs are useful for large-scale varietal evaluation trials where a large number of test lines are assessed under limited experimental resources.

[vinaymandya123-collab] NeutroBasicDesignsAnalysis 0.0.1

vinaymandya123@gmail.com (Vinaykumar L.N.) — Sun, 09 Aug 2026 08:40:13 GMT

Provides neutrosophic statistical methods for interval-valued data from completely randomized and randomized complete block designs. Methods include neutrosophic analysis of variance, analysis of covariance, multivariate analysis of variance, pooled analysis of variance, Levene's test, and Aitken transformation. When the lower and upper bounds are equal (crisp data), the methods reduce to their corresponding classical statistical analyses. The basic concept of neutrosophic statistics is based on Smarandache (2014) , while the statistical analysis procedures implemented in this package are newly developed.

[vinaymandya123-collab] NeutroCrdRcbdAnalysis 0.0.1

vinaymandya123@gmail.com (Vinaykumar L.N.) — Wed, 22 Jul 2026 08:30:26 GMT

Provides neutrosophic analysis of variance (NANOVA) and analysis of covariance (NANCOVA) for Completely Randomized Designs (CRD) and Randomized Complete Block Designs (RCBD) using interval-valued observations. Computes interval sums of squares, mean squares, F-statistics, significance tests, and interval-based least significant difference (LSD) comparisons. When lower and upper observations are identical (crisp data), the methods reduce to the corresponding classical ANOVA and ANCOVA.

[vinaymandya123-collab] NeutroSplitStripAnalysis 0.0.1

vinaymandya123@gmail.com (Vinaykumar L.N.) — Mon, 22 Jun 2026 18:30:56 GMT

Provides methods for Neutrosophic Analysis of Variance (NANOVA) for split-plot and strip-plot experimental designs using interval-valued observations. The package computes neutrosophic sums of squares, mean squares, interval-valued F-statistics, significance tests, and Least Significant Difference (LSD) based multiple comparisons for main plot, sub plot, horizontal factor, vertical factor, and interaction effects. For crisp data, users may provide identical lower and upper response values to obtain results equivalent to classical analysis of variance. The basic idea of neutrosophic statistics is obtained from Smarandache (2014) , while the analysis procedures implemented in this package are newly developed.

[vinaymandya123-collab] NeutroRCDsAnalysis 0.1.1

vinaymandya123@gmail.com (Vinaykumar L.N.) — Wed, 10 Jun 2026 12:10:17 GMT

Description: Provides methods for Neutrosophic Analysis of Variance (NANOVA) and Neutrosophic Analysis of Covariance (NANCOVA) for row-column designs, including Latin square designs and Youden square designs, using interval-valued observations. The package computes neutrosophic sums of squares, mean squares, interval-valued F-statistics, significance tests, and multiple comparisons using Least Significant Difference (LSD) procedures. For crisp data, users may enter identical lower and upper values of responses to obtain classical Analysis of Variance (ANOVA) results. Similarly, users may enter identical lower and upper values for both responses and covariates to obtain classical Analysis of Covariance (ANCOVA) results.

[vinaymandya123-collab] pRepDesigns 1.2.0

vinaymandya123@gmail.com (Vinaykumar L.N.) — Wed, 03 Apr 2024 07:13:56 GMT

Early generation breeding trials are to be conducted in multiple environments where it may not be possible to replicate all the lines in each environment due to scarcity of resources. For such situations, partially replicated (p-Rep) designs have wide application potential as only a proportion of the test lines are replicated at each environment. A collection of several utility functions related to p-Rep designs have been developed. Here, the package contains six functions for a complete stepwise analytical study of these designs. Five functions pRep1(), pRep2(), pRep3(), pRep4() and pRep5(), are used to generate five new series of p-Rep designs and also compute average variance factors and canonical efficiency factors of generated designs. A fourth function NCEV() is used to generate incidence matrix (N), information matrix (C), canonical efficiency factor (E) and average variance factor (V). This function is general in nature and can be used for studying the characterization properties of any block design. A construction procedure for p-Rep designs was given by Williams et al.(2011) which was tedious and time consuming. Here, in this package, five different methods have been given to generate p-Rep designs easily.

[vinaymandya123-collab] ResPBIBD 0.1.0

vinaymandya123@gmail.com (Vinaykumar L.N.) — Tue, 01 Nov 2022 13:33:03 GMT

A collection of several utility functions related to resolvable and affine resolvable Partially Balanced Incomplete Block Designs (PBIBDs), have been developed. In the class of resolvable designs, affine resolvable designs are said to be optimal, Bailey (1995) . Here, the package contains three functions to generate and study the characterization properties of these designs. Developed functions are named as PBIBD1(), PBIBD2() and PBIBD3(), in which first two functions are used to generate two new series of affine resolvable PBIBDs and last one is used to generate a new series of resolvable PBIBDs, respectively. In addition, these functions can also be used to generate design parameters (v, b, r and k), canonical efficiency factors, variance factor between associates and average variance factors of the generated designs. Here v is the number of treatments, b (= b1 + b2, in case of non-proper design) is the number of blocks, r is the number of replications and k (= k1 + k2; k1 is the size of b1 and k2 is the size of b2) is the block size.

[vinaymandya123-collab] Design.parameters 0.1.0

vinaymandya123@gmail.com (Vinaykumar L.N.) — Mon, 03 Oct 2022 06:40:08 GMT

Here, a function has been developed to generate parameters of the input designs, as well as incidence matrices. This is a general function that can be used to investigate the characterization properties of any block design.