• 管理Genstat自己的電子表格上的數據
• 兼容Excel電子表格（導入/導出）
• 使用直方圖，箱線圖，散點圖，折線圖，格子圖，輪廓和三維曲面圖等圖形說明數據
• 將數據與表格報告，擬合分佈和標準測試（如t檢驗和各種非參數檢驗）進行匯總和比較
• 使用具有廣泛數學和統計功能的通用計算工具轉換數據
• 線性或非線性回歸變量之間的模型關係，廣義線性模型，廣義加性模型，廣義線性混合模型或分層廣義線性模型
• 使用平衡ANOVA或REML方法（包括相關結構的建模）分析實驗，從單因素方差分析到具有多個誤差變化源的複雜設計
• 設計調查決定檢測預期治療效果所需的樣本量或重複次數;
• 通過多變量技術識別數據模式，如規範變量分析，主成分分析，主坐標分析，對應分析，偏最小二乘法，分類樹和聚類分析;
• 分析分層或非結構化調查的結果;
• 繪製控製圖，打印Pareto表併計算能力統計;
• 使用Box-Jenkins模型或光譜分析分析時間序列;
• 分析重複測量，分析方差，或使用前瞻性依賴結構，或通過建模隨時間的相關性;
• 使用克里金法或空間點過程分析空間模式。這些技術在農業，生態學，遺傳學，醫學研究和其他生物學領域，以及工業研究和質量控制以及經濟和社會調查中都很有用。事實上，在使用統計數據的任何研究，商業，政府或教育領域。

Minimum requirements for running Genstat

• An Internet connection to activate your software license
• 64 bit processor
• Windows 10 or higher operating system
• Administrative rights
• Sufficient free space on your hard disk (this varies by product but 500 MB will be sufficient)
• For our concurrent licenses, you will need 2 open outgoing ports, which on most networks are open by default. You will be emailed these designated port numbers at the time of purchase. If your organization has a network security policy / firewall you may see error messages during installation and will need to contact your IT department / network administrator. (Also contact our support team if you require more help).

CycDesigN

CycAnalysis is a separate module in CycDesigN that allows output from a CycDesigN session to be tailored in a form ready for analysis. For example, a spreadsheet of the design blocking and treatment structures is automatically generated. CycDesigN also lets you generate Genstat and SAS code for the analysis of most designs.

CycDesigN provides the most comprehensive design generation package yet available for experimenters. In particular, resolvable and partially replicated designs are used by experimenters involved in field variety trials. For smaller experiments, say in the glasshouse or laboratory, non-resolvable row-column designs are often employed and can typically include spatial enhancement. Crossover designs are frequently used in areas such as clinical trials, taste and psychological testing and psycho-physical experiments. The authors are leading researchers and, as a result, the algorithms incorporate the most recent developments in the construction of experimental designs.

ASReml
Fit linear mixed models using advanced restricted maximum likelihood (reml) techniques.

Using the Average Information (AI) algorithm and sparse matrix methods ASReml handles large or extremely large and complex data analysis (of 500,000 or more observations / effects). It provides flexible methods to model a wide range of variance models for random effects or error structures.

Change the text below to read – The typical applications of ASReml include the analysis of:

• (un)balanced longitudinal data
• (un)balanced designed experiments
• multi-environment trials
• both univariate and multivariate animal breeding
• genetics data
• regular or irregular spatial data
• repeated measures analysis

ASReml R
ASReml-R, the powerful statistical package that fits linear mixed models (LMMs) using Residual Maximum Likelihood (REML) in the R environment is now at version 4.

This version offers a more unified framework and extended functionality for LMM analysis, particularly for large and complex data sets. New features with Version 4 include:

• new and improved licensing
• a simplified and more meaningful syntax for model specification:
  • rcov becomes residual and direct sum structures for residual models with data partitioned into sections to which separate variance structures are applied are now logically specified using a dsum() function
  • rarely used options are now listed externally to the model call in a newasreml.options() function
• a more unified framework for related arguments in the call to asreml()and the output object , e.g.
  • na.action() for dealing with missing values in both the response and explanatory variables
  • a formulae component of the ASReml-R object which lists the fixed, random, sparse and residual model formulae
• improved updates in factor analytic models and a reduced rank rr() variant of the fa() variance model function
• a simpler, more consistent specification of known variance models (including relationship matrices) through the vm() function, which also caters for known singular matrices
• computationally efficient fitting of random regression models when there are more variables than observations – motivated by the use of SNP marker data to explain genotypes
• more informative warning and error messages
• improved graphics powered by ggplot2.

More advanced functionality new with Version 4 includes:

• introduction of the own() variance model to allow the specification of a user-defined variance structure
• extensions to generalized linear models including threshold models and bivariate models with one variate having a normal distribution and the other variate distributed from an exponential family distribution
• generating design matrices to allow use of derived model terms and functions; design argument to asreml.options()
• functions to generate factors that either combine levels of a factor or use a subset of levels to allow easier prediction of models; combine, prune, gpf(), sbs().

Also included:

• computing functions of variance components and their approximate standard errors; vpredict()
• ulating information criteria including AIC and BIC.calc

This is just a selection from the full set of new features available with ASReml-R Version 4. A detailed account of the new functionality is presented in a navigation guide which guides existing users in transitioning from Version 3 to Version 4.