Statistical Packages

SPAD: Statistical Package for Augmented Designs (SPAD) is useful for designing agricultural experiments conducted for comparing existing practices / check varieties, called controls, with new practices / varieties / germplasm collections, called tests, where the experimental material for the tests is limited and it is not possible to replicate them in the design. The package generates a randomized layout of an augmented randomized complete block (RCB) design and augmented complete block design with equal or unequal block sizes. The optimal replication number of the control treatments in every block is obtained by maximizing the efficiency per observation for making tests vs controls comparisons. User has a flexibility to choose the replication number of the control(s) in each of the blocks. The package generates randomized layout of the design as per the procedure of Federer (1956), which is generally overlooked while conducting such experiments. The package also performs the analysis of data generated from augmented block designs (complete or incomplete). The treatment sum of squares is partitioned into different components of interest viz. (i) among test treatments, (ii) among control treatments and (iii) among test treatments and control treatments. Multiple comparison procedures for making all possible pairwise treatment comparisons can also be employed through this package. A null hypothesis on any other contrast of interest can also be tested

SPFE 1.0: Statistical Package for Factorial Experiments generates the designs for symmetrical and asymmetrical factorial experiments with and without confounding. It also generates the randomized layout of the designs for factorial experiments. The design is generated on listing the independent interactions to be confounded. It also generates fractional factorial plans for symmetrical factorial experiments. The data generated through these designs are analyzed as per usual procedure of designs for single factor experiments. Contrast analysis is carried out to obtain the sum of squares of main effects and interactions. A null hypothesis on any other contrast of interest can also be tested.       This package, besides being useful for the experimenters in the NARS, will be quite useful for teaching of Design and Analysis of Factorial Experiments in the classroom

SPBD Release 1.0: Statistical Package for Balanced Incomplete Block Designs enables a user to select and generate a randomized layout of Balanced Incomplete Block (BIB) Design. The package generates BIB designs with replication numbers up to a maximum of 20 for asymmetric BIB designs and 30 for symmetric BIB designs. The package also provides the analysis of variance with both treatments adjusted and blocks adjusted sum of squares, adjusted treatment means, variance of the estimated treatment contrasts and the contrast sum of squares, etc. The definitions of the terminology used are available on-line. The package is useful for the experimenters, classroom teaching as well as for the researchers in Statistics with special interest in Design of Experiments.  or computing genetic parameters for one-way and two-way classified data

SPAB2.0: Statistical Package for Animal Breeding (SPAB2.0) has been developed keeping in view, the computing requirements of scientists/students, mainly working in Animal Breeding and Animal Genetics research. The package is Window based, Menu driven and works in a User friendly manner. In the present version of the package, 37 useful programs of maximum utility are included. These programs have been grouped into ten modules. It has provisions for Analysis of Mixed Model Data as provided in LSML Package developed by Walter R. Harvey, Best linear unbiased prediction (BLUP) for Single traits, Best linear unbiased prediction (BLUP) for multiple traits, Adjustment for different non-genetic effects, Sire evaluation using SRLS and Sire evaluation using REML. Computation of Mean and SE for different classifications, Genetic parameters for half sib data, Genetic parameters for Full sib data, Coefficient of Repeatability and Producing Ability. It provides computation of Selection Index (Hazel’s Method) Restricted Selection Index, Sire Indices for different models, Osborne’s index, Cunningham’s Selection Index. Diallel analysis can be performed for data with unequal classifications, for different modeling situations viz., Analysis of complete 3 x 3 non-orthogonal Diallel crosses data, Analysis of complete 4 X 4 or above non-orthogonal Diallel cross data with or without pure breds, Analysis of 4 X 4 or above non-orthogonal Diallel cross data without reciprocals and pure breeds. One can calculate Inbreeding coefficient, Genetic Gain and Genetic trend. Multivariate Analysis has programs for D Square analysis, Multiple Regression Analysis (Step-up / Step-down methods, all possible combinations) and Principal Component Analysis. Most of the Non-Parametric tests can also provided in the package. Help is provided and it is having a User Manual which one can study and use. This package can aid in teaching the subject of statistical genetic to the post-graduate students and helpful for the researchers in statistics with interest in animal sciences.

SSDA1.0: Software for Survey Data Analysis (SSDA 1.0) is useful for the analysis of survey data. SSDA analyzes the data collected using systematic, simple random sampling (SRS), probability proportional to size (PPS), stratified, cluster, two stage and stratified two stage sampling schemes. It provides the estimates of population mean, variance and design efficiency of the sampling scheme in comparison to the simple random sampling without replacement. It also provides descriptive statistics of the data without consideration of sampling design i.e. measures of central tendency like mean and median and measures of dispersion like range, variance, coefficient of variation, skewness and kurtosis. The software is completely menu driven and guides users step-by-step through data analysis process. It also has the facility to impute missing data, if any, using commonly used imputation methods. This package is an aid in teaching the subject of analysis of sample survey data to the post-graduate students and is also helpful to the researchers in statistics with interest in sample surveys.