EQS 是廣泛使用的結構方程模型(Structural Equation Modeling)的軟體,具有易於使用的特性。用戶可使用簡單的數據錄入與輸出操作就可進行統計檢驗和繪圖工作。EQS以便利的數據處理和統計預先處理為特色。
EQS 提供研究者和統計學家一個用於結構方程式模型的簡易工具,其功能包含多元迴歸、驗證性因子分析、結構化平均分析、因徑分析和多群體比較等等。使用過EQS的學者一致認同EQS提供完整且簡單操作接口。使用EQS,不必具備任何矩陣代數的知識就也可輕易操作。
更新介紹
» EQS’ comprehensive data management capabilities allows users to work with data without having to use other programs such as SPSS®. Imports SPSS files.
» EQS tests the full range of SEM including latent regression, confirmatory factor analysis, structured means, multiple population, latent growth curve, multilevel analysis, correlation structure & categorical data models.
» EQS’ improved DIAGRAMMER makes model setup easy without use of command language or knowledge of matrix algebra! Yet, commands can be used and are useful for new features.
» EQS is preferred by thousands of researchers worldwide providing them with a wide range of easy to use statistical and data exploration tools.
» Arruda-Bentler Regularized GLS Test
» Jalal-Bentler Monte Carlo Test
» Nesting and Equivalence Tests
» Automatic Difference Tests
» Exploratory Factor Analysis
» (with Parallel Analysis & Bifactor Rotation)
» Smoothing a Correlation Matrix
» New Formula for Robust RMSEA
» Satorra-Bentler Mean and Variance Adjusted Test Statistic
» Robust Statistics for Clustered Data
EViews 因為其創新、易於使用的界面,可提供學術研究者、公司、政府機關及學生獲得強有力的統計、預測和建模工具。EViews 是結合強大功能和易於使用的套裝軟體,使用於時間序列、橫斷面或縱向數據 。藉由EViews,你可以快速有效地管理你的數據,進行統計分析和計量、生成預測或模擬測試,並生產出高質量圖表或列入其他應用 。
EViews 有兩種版本,標準版(Standard)和企業版(Enterprise),企業版版本中支援ODBC和幾個商業專有數據格式數據和資料庫,介紹如下。
EViews 應用領域
應用經濟計
量學
總體經濟的研究和預測
銷售預測
財務分析
成本分析和預測
蒙地卡羅模擬
經濟模型的估計和模擬
利率與外匯預測
更新介紹
EViews 14 offers more of the power and ease-of-use that you've come to expect. Enhancements include:
JDemetra+ seasonal adjustment
Facebook™ Prophet
Quantile ARDL estimation
MIDAS GARCH estimation
Elastic net enhancements
Outlier Detection
Boosted Hodrick-Prescott Filter
Tests for series trends and break points
Tests for financial bubbles
Local projection impulse response (LPIRF) analysis
Numerous improvements to VAR and impulse response confidence intervals
And much more!
澳洲 Victoria University 大學研發的GEMPACK (General Equilibrium Modelling PACKage) 軟體,是一套多用途的經濟建模套裝軟體,特別適用於一般和局部均衡模型,並可以處理廣泛的經濟行為。GEMPACK 可以建立並運用可計算一般均衡模型(computable general equilibrium, CGE, model),並且具有求解過程簡單、可變動模組,及適用於政策分析等特點。
該方法在經歷 60 年代的短暫沉寂後,自90 年代開始,迅速成為政策分析的重要方法,廣泛應用於貿易政策、稅制改革、環境能源、產業政策 等方面的分析。在國外,對這種分析方法的關注正呈上升趨勢,一些政府機構、科研單位和高等院校正努力加強這方面的研究。
GEMPACK使建模者能夠求解非常大型的非線性方程式。
一旦模型方程式被使用一種類似符號的語言指定後,建模者就可以擺脫求解過程的計算細節。
GEMPACK軟體計算經濟模型的準確的解答。
所有的功能(基礎和高級)都有完整的文字說明、詳細的指令(包括許多手工操作示例)幫助新用戶快速入門。
GEMPACK Windows套裝程式可以幫助您視覺化、探索編碼、資料和結果。
GEMPACK 包含了強大的用於求解recursive-dynamic 和 fully-intertemporal 的能力
當出現問題會提示您發送回饋。
GEMPACK是非常有用的教學工具。
GEMPACK被超過90個國家的超過400個組織使用。
GEMPACK會不斷地持續改善。
新版介紹
Release 12.1 includes new automatic homogeneity testing, further improvements to LU decomposition performance, faster GEMSIM, enhanced searching in the Tabmate editor, improvements to ViewHAR and ViewSOL and faster start times for graphical interface (GUI) programs. As well many small bugs introduced in release 12.0 have been fixed.
Automatic Homogeneity Testing
Most CGE models are homogeneous degree zero in prices and quantities. The automatic homogeneity testing is one method for checking the homogeneity, real or nominal, of a model. Two kinds of tests are available, a pre-simulation homogeneity check of equations and a homogeneity simulation for testing simulation results. In both cases results of the test are summarized for the user.
To prepare a model for automatic homogeneity testing the user:
Specifies the VPQType (Value, Price, Quantity, None or Unspecified) of variables. Several methods are provided which minimize the effort required by the user.
Specifies the original level (ORIG_LEVEL=...) for ordinary change variables.
Includes one of the following commands in a CMF file
homogeneity check = real ;
homogeneity check = nominal ;
homogeneity simulation = real ;
homogeneity simulation = nominal ;
ViewHAR and ViewSOL improvements
when displaying mappings ViewHAR now shows elements of the domain and codomain sets
in ViewHAR keyboard combinations allow the user to select active sets (CTRL + LEFT, CTRL + RIGHT) and elements of active set (CTRL + UP, CTRL + DOWN)
new menu items in ViewHAR and ViewSOL open explorer or CMD exe in current folder
in ViewHAR and ViewSOL, while viewing the Contents screen or data, typing activates a quick search in the current column
CTRL + X copies the contents of a single cell from ViewHAR and ViewSOL
ViewSOL...
GenStat 是個數據分析工具,也是統計軟體。事實上,GenStat 可以被稱為「我工作中不可或缺的幫手」。GenStat 是一款功能強大的統計系統軟體。靈活而完全交互式系統,最先進的圖形化工具,友好的圖形化用戶界面,以及強大的統計學程式編製功能。GenStat幫助你處理工程上的數據統計,科學研究中的數據分析...等。GenStat 具有悠久的成功歷史,並且不斷更新發展,使其活躍在統計學技術的最前沿。被廣泛應用於學術界、科研探索以及工業領域。
GenStat 是透過一個視窗選單介面讓初學用戶便於使用的全面化統計系統, 也可透過一個強有力的命令語言介面讓有經驗的用戶有更多的權力和靈活性。
完全互動式系統,最先進的圖形化工具,友好的圖形化用戶介面,還有強大的統計學程式編制功能。GenStat 具有30年悠久的成功歷史,並且不斷更新發展,使其活躍在統計學技術的最前端。GenStat的主要優點之一是已經透過培養訓練統計員重複被測試的可提供的統計方法的巨大的範圍,穿過很多應用和訓練。
basic statistics 基本的統計
design and analysis of designed experiments 實驗設計
analysis of linear and generalized linear mixed models
microarray analysis
regression (linear, nonlinear and generalized linear)
hierarchical generalized linear models
spatial analysis 空間分析
multivariate analysis techniques 多變量的分析技術
time series 時間序列
statistical process control methods 統計過程式控制制方法
survival analysis 生存分析
sample size calculations and resampling methods
更新介紹
23 new procedures
4 new functions
Analysis of variance for censored data (ATOBIT, AUTOBIT)
Analysis of censored regression analyses with Normal, gamma and negative binomial data (RGTOBIT, RNTOBITRNBTOBIT)
REML analysis of field trials using a two-dimensional spline model (V2DSPLINE)
Design and analysis of sets of n-of-1 trials (N1ANOVA, N1DESIGN, N1PLOT, N1SIMULATE, N1TTEST)
Negative binomial probabilities and random numbers (CLNEGATIVEBINOMIAL, CUNEGATIVEBINOMIAL, EDNEGATIVEBINOMIAL, PRNEGATIVEBINOMIAL, ELNEGBINOMIAL, EUNEGBINOMIAL, GRANDOM)
Gamma distribution expected values (ELGAMMA, EUGAMMA)
Improved default settings for the symbols, colours and line-styles used in graphs
At the start of each job, the server now loads the default graphics environment that is specified in the computer’s Registry, and that Registry setting can be accessed and defined (GET, SET)
Heatmaps (DBANDCOLOURS, DHEATMAP, FHEATCOLOURS)
Graphical displays of data in a table, so that you can create PDF reports that include graphs (DPRINT)
Latent GOLD is a powerful latent class and finite mixture program with a very user-friendly point-and-click interface (GUI). Two add-on options are available to extend the basic version of the program.
The Advanced/Syntax add-on enables more control for advanced users via use of a Syntax command language including intuitive LG-equations™. This add-on also contains more advanced GUI modeling features such as Latent (Hidden) Markov and Multilevel models.
The Choice add-on allows estimation of discrete choice models via the point-and-click interface. When obtaining both the Choice and the Advanced/Syntax add-on, various advanced choice models can be estimated and the Syntax can also be used to further the customize discrete choice models.
LatentGold最主要的功能為:
潛類聚類分析(latentclass clusteranalysis)
潛類因子分析(latentclassfactor analysis)
潛類回歸模型(latent classregression)
Basic version Includes GUI for
LC Cluster
Latent GOLD®'s cluster module provides the state-of-the-art in cluster analysis based on latent class models. Latent classes are unobservable (latent) subgroups or segments. Cases within the same latent class are homogeneous on certain criteria (variables), while cases in different latent classes are dissimilar from each other in certain important ways.
The traditional latent class model can be used to handle measurement and classification errors in categorical variables, and can accomodate avriables that are nominal, ordinal, continuous, counts, or any combination of these. Covariates can be included directly in the model as well for improved cluster description.
Latent GOLD® improves over traditional ad-hoc types of cluster analysis methods by including model selection criteria and probability-based classification. Posterior membership probabilities are estimated directly from the model parameters and used to assign cases to the classes.
Discrete Factor (DFactor)
A DFactor model is often used for variable reduction or to define an ordinal attitudinal scale. It contains one or more DFactors which group together variables sharing a common source of variation. Each DFactor is either dichotomous (the default option) or consists of 3 or more ordered levels (ordered latent classes).
In this way, Latent GOLD®’s factor module has several advantages over traditional factor analysis:
Solutions are immediately interpretable and don’t require rotation
The factors are assumed to be ordinal and not continuous
No additional assumptions are required to estimate factor scores
The observed variables can be nominal, ordinal, continuous, or counts, or any combination of these
LC Regression and Growth
A Regression model is used to predict a dependent variable as a function of predictor variables in a homogeneous population.
Latent GOLD® makes it possible to estimate a regression model in a heterogeneous popu...