| g01 |
Simple Calculations on Statistical Data |
| Routine Name | Purpose |
| g01aac | nag_summary_stats_1var
Mean, variance, skewness, kurtosis, etc., one variable, from raw data
|
| g01adc | nag_summary_stats_freq
Mean, variance, skewness, kurtosis, etc., one variable, from frequency table
|
| g01aec | nag_frequency_table
Frequency table from raw data
|
| g01alc | nag_5pt_summary_stats
Five-point summary (median, hinges and extremes)
|
| g01bjc | nag_binomial_dist
Binomial distribution function
|
| g01bkc | nag_poisson_dist
Poisson distribution function
|
| g01blc | nag_hypergeom_dist
Hypergeometric distribution function
|
| g01cec | nag_deviates_normal_dist
Deviate of Normal distribution function
|
| g01dac | nag_normal_scores_exact
Normal scores, accurate values
|
| g01dcc | nag_normal_scores_var
Normal scores, approximate variance-covariance matrix
|
| g01ddc | nag_shapiro_wilk_test
Shapiro and Wilk's W test for Normality
|
| g01dhc | nag_ranks_and_scores
Ranks, Normal scores, approximate Normal scores or exponential (Savage) scores
|
| g01eac | nag_prob_normal
Probabilities for the standard Normal distribution
|
| g01ebc | nag_prob_students_t
Probabilities for Student's t-distribution
|
| g01ecc | nag_prob_chi_sq
Probabilities for χ2 distribution
|
| g01edc | nag_prob_f_dist
Probabilities for F-distribution
|
| g01eec | nag_prob_beta_dist
Upper and lower tail probabilities and probability density function for the beta distribution
|
| g01efc | nag_gamma_dist
Probabilities for the gamma distribution
|
| g01emc | nag_prob_studentized_range
Computes probability for the Studentized range statistic
|
| g01epc | nag_prob_durbin_watson
Computes bounds for the significance of a Durbin–Watson statistic
|
| g01erc | nag_prob_von_mises
Computes probability for von Mises distribution
|
| g01etc | nag_prob_landau
Landau distribution function Φ (λ)
|
| g01euc | nag_prob_vavilov
Vavilov distribution function ΦV (λ;κ,β2)
|
| g01eyc | nag_prob_1_sample_ks
Computes probabilities for the one-sample Kolmogorov–Smirnov distribution
|
| g01ezc | nag_prob_2_sample_ks
Computes probabilities for the two-sample Kolmogorov–Smirnov distribution
|
| g01fac | nag_deviates_normal
Deviates for the Normal distribution
|
| g01fbc | nag_deviates_students_t
Deviates for Student's t-distribution
|
| g01fcc | nag_deviates_chi_sq
Deviates for the χ2 distribution
|
| g01fdc | nag_deviates_f_dist
Deviates for the F-distribution
|
| g01fec | nag_deviates_beta
Deviates for the beta distribution
|
| g01ffc | nag_deviates_gamma_dist
Deviates for the gamma distribution
|
| g01fmc | nag_deviates_studentized_range
Computes deviates for the Studentized range statistic
|
| g01ftc | nag_deviates_landau
Landau inverse function Ψ (x)
|
| g01gbc | nag_prob_non_central_students_t
Computes probabilities for the non-central Student's t-distribution
|
| g01gcc | nag_prob_non_central_chi_sq
Computes probabilities for the non-central χ2 distribution
|
| g01gdc | nag_prob_non_central_f_dist
Computes probabilities for the non-central F-distribution
|
| g01gec | nag_prob_non_central_beta_dist
Computes probabilities for the non-central beta distribution
|
| g01hac | nag_bivariate_normal_dist
Probability for the bivariate Normal distribution
|
| g01hbc | nag_multi_normal
Computes probabilities for the multivariate Normal distribution
|
| g01jcc | nag_prob_lin_non_central_chi_sq
Computes probability for a positive linear combination of χ2 variables
|
| g01jdc | nag_prob_lin_chi_sq
Computes lower tail probability for a linear combination of (central) χ2 variables
|
| g01mbc | nag_mills_ratio
Computes reciprocal of Mills' Ratio
|
| g01mtc | nag_prob_density_landau
Landau density function φ (λ)
|
| g01muc | nag_prob_density_vavilov
Vavilov density function φV (λ;κ,β2)
|
| g01nac | nag_moments_quad_form
Cumulants and moments of quadratic forms in Normal variables
|
| g01nbc | nag_moments_ratio_quad_forms
Moments of ratios of quadratic forms in Normal variables, and related statistics
|
| g01ptc | nag_moment_1_landau
Landau first moment function Φ1 (x)
|
| g01qtc | nag_moment_2_landau
Landau second moment function Φ2 (x)
|
| g01rtc | nag_prob_der_landau
Landau derivative function φ ' (λ)
|
| g01zuc | nag_init_vavilov
Initialisation function for g01muc and g01euc
|
| g02 | Correlation and Regression Analysis |
| Routine Name | Purpose |
| g02brc | nag_ken_spe_corr_coeff
Kendall and/or Spearman non-parametric rank correlation coefficients, allows variables and observations to be selectively disregarded
|
| g02btc | nag_sum_sqs_update
Update a weighted sum of squares matrix with a new observation
|
| g02buc | nag_sum_sqs
Computes a weighted sum of squares matrix
|
| g02bwc | nag_cov_to_corr
Computes a correlation matrix from a sum of squares matrix
|
| g02bxc | nag_corr_cov
Product-moment correlation, unweighted/weighted correlation and covariance matrix, allows variables to be disregarded
|
| g02byc | nag_partial_corr
Computes partial correlation/variance-covariance matrix from correlation/variance-covariance matrix computed by g02bxc
|
| g02cac | nag_simple_linear_regression
Simple linear regression with or without a constant term, data may be weighted
|
| g02cbc | nag_regress_confid_interval
Simple linear regression confidence intervals for the regression line and individual points
|
| g02dac | nag_regsn_mult_linear
Fits a general (multiple) linear regression model
|
| g02dcc | nag_regsn_mult_linear_addrem_obs
Add/delete an observation to/from a general linear regression model
|
| g02ddc | nag_regsn_mult_linear_upd_model
Estimates of regression parameters from an updated model
|
| g02dec | nag_regsn_mult_linear_add_var
Add a new independent variable to a general linear regression model
|
| g02dfc | nag_regsn_mult_linear_delete_var
Delete an independent variable from a general linear regression model
|
| g02dgc | nag_regsn_mult_linear_newyvar
Fits a general linear regression model to new dependent variable
|
| g02dkc | nag_regsn_mult_linear_tran_model
Estimates of parameters of a general linear regression model for given constraints
|
| g02dnc | nag_regsn_mult_linear_est_func
Estimate of an estimable function for a general linear regression model
|
| g02eac | nag_all_regsn
Computes residual sums of squares for all possible linear regressions for a set of independent variables
|
| g02ecc | nag_cp_stat
Calculates R2 and CP values from residual sums of squares
|
| g02eec | nag_step_regsn
Fits a linear regression model by forward selection
|
| g02fac | nag_regsn_std_resid_influence
Calculates standardized residuals and influence statistics
|
| g02fcc | nag_durbin_watson_stat
Computes Durbin–Watson test statistic
|
| g02gac | nag_glm_normal
Fits a generalized linear model with Normal errors
|
| g02gbc | nag_glm_binomial
Fits a generalized linear model with binomial errors
|
| g02gcc | nag_glm_poisson
Fits a generalized linear model with Poisson errors
|
| g02gdc | nag_glm_gamma
Fits a generalized linear model with gamma errors
|
| g02gkc | nag_glm_tran_model
Estimates and standard errors of parameters of a general linear model for given constraints
|
| g02gnc | nag_glm_est_func
Estimable function and the standard error of a generalized linear model
|
| g02hac | nag_robust_m_regsn_estim
Robust regression, standard M-estimates
|
| g02hbc | nag_robust_m_regsn_wts
Robust regression, compute weights for use with g02hdc
|
| g02hdc | nag_robust_m_regsn_user_fn
Robust regression, compute regression with user-supplied functions and weights
|
| g02hfc | nag_robust_m_regsn_param_var
Robust regression, variance-covariance matrix following g02hdc
|
| g02hkc | nag_robust_corr_estim
Robust estimation of a correlation matrix, Huber's weight function
|
| g02hlc | nag_robust_m_corr_user_fn
Calculates a robust estimation of a correlation matrix, user-supplied weight function plus derivatives
|
| g02hmc | nag_robust_m_corr_user_fn_no_derr
Calculates a robust estimation of a correlation matrix, user-supplied weight function
|
|
g03 | Multivariate Methods |
|
Routine Name
|
Purpose
|
|
g03aac | nag_mv_prin_comp
Principal component analysis
|
| g03acc | nag_mv_canon_var
Canonical variate analysis
|
| g03adc | nag_mv_canon_corr
Canonical correlation analysis
|
| g03bac | nag_mv_orthomax
Orthogonal rotations for loading matrix
|
| g03bcc | nag_mv_procustes
Procrustes rotations
|
| g03cac | nag_mv_factor
Maximum likelihood estimates of parameters
|
| g03ccc | nag_mv_fac_score
Factor score coefficients, following g03cac
|
| g03dac | nag_mv_discrim
Test for equality of within-group covariance matrices
|
| g03dbc | nag_mv_discrim_mahaldist
Mahalanobis squared distances, following g03dac
|
| g03dcc | nag_mv_discrim_group
Allocates observations to groups, following g03dac
|
| g03eac | nag_mv_distance_mat
Compute distance (dissimilarity) matrix
|
| g03ecc | nag_mv_hierar_cluster_analysis
Hierarchical cluster analysis
|
| g03efc | nag_mv_kmeans_cluster_analysis
K-means
|
| g03ehc | nag_mv_dendrogram
Construct dendogram following g03ecc
|
| g03ejc | nag_mv_cluster_indicator
Construct clusters following g03ecc
|
| g03fac | nag_mv_prin_coord_analysis
Principal co-ordinate analysis
|
| g03fcc | nag_mv_ordinal_multidimscale
Multidimensional scaling
|
| g03xzc | nag_mv_dend_free
Frees memory allocated to the dendrogram array in g03ehc
|
| g03zac | nag_mv_z_scores
Standardize values of a data matrix
|
| g04 | Analysis of Variance Routine |
|
Routine Name
|
Purpose |
| g04bbc | nag_anova_random
General block design or completely randomized design
|
| g04bcc | nag_anova_row_col
Analysis of variance, general row and column design, treatment means and standard errors
|
| g04cac | nag_anova_factorial
Complete factorial design
|
| g04czc | nag_anova_factorial_free
Memory freeing function for g04cac
|
| g04dbc | nag_anova_confid_interval
Computes confidence intervals for differences between means computed by g04bbc or g04bcc
|
| g04eac | nag_dummy_vars
Computes orthogonal polynomials or dummy variables for factor/classification variable
|
|
g05 | Random Number Generators |
|
Routine Name
|
Purpose
|
| g05cac | nag_random_continuous_uniform
Pseudo-random real numbers, uniform distribution over (0,1)
|
| g05cbc | nag_random_init_repeatable
Initialise random number generating functions to give repeatable sequence
|
| g05ccc | nag_random_init_nonrepeatable
Initialise random number generating functions to give non-repeatable sequence
|
| g05cfc | nag_save_random_state
Save state of random number generating functions
|
| g05cgc | nag_restore_random_state
Restore state of random number generating functions
|
| g05dac | nag_random_continuous_uniform_ab
Pseudo-random real numbers, uniform distribution over (a,b)
|
| g05dbc | nag_random_exp
Pseudo-random real numbers, (negative) exponential distribution
|
| g05ddc | nag_random_normal
Pseudo-random real numbers, Normal distribution
|
| g05dyc | nag_random_discrete_uniform
Pseudo-random integer from uniform distribution
|
| g05eac | nag_ref_vec_multi_normal
Set up reference vector for multivariate Normal distribution
|
| g05ecc | nag_ref_vec_poisson
Set up reference vector for generating pseudo-random integers, Poisson distribution
|
| g05edc | nag_ref_vec_binomial
Set up reference vector for generating pseudo-random integers, binomial distribution
|
| g05ehc | nag_ran_permut_vec
Pseudo-random permutation of an integer vector
|
| g05ejc | nag_ran_sample_vec
Pseudo-random sample without replacement from an integer vector
|
| g05exc | nag_ref_vec_discrete_pdf_cdf
Set up reference vector from supplied cumulative distribution function or probability distribution function
|
| g05eyc | nag_return_discrete
Pseudo-random integer from reference vector
|
| g05ezc | nag_return_multi_normal
Pseudo-random multivariate Normal vector from reference vector
|
| g05fec | nag_random_beta
Pseudo-random real numbers from the beta distribution
|
| g05ffc | nag_random_gamma
Pseudo-random real numbers from the gamma distribution
|
| g05hac | nag_arma_time_series
ARMA time series of n terms
|
| g05hkc | nag_generate_agarchI
Univariate time series, generate n terms of either a symmetric GARCH process or a GARCH process with asymmetry of the form (εt-1 + γ)2
|
| g05hlc | nag_generate_agarchII
Univariate time series, generate n terms of a GARCH process with asymmetry of the form (|εt-1| + γ εt-1)2
|
| g05hmc | nag_generate_garchGJR
Univariate time series, generate n terms of an asymmetric Glosten, Jagannathan and Runkle (GJR) GARCH process
|
| g05kac | nag_rngs_basic
Pseudo-random real numbers, uniform distribution over (0,1), seeds and generator number passed explicitly
|
| g05kbc | nag_rngs_init_repeatable
Initialise seeds of a given generator for random number generating functions (that pass seeds explicitly) to give a repeatable sequence
|
| g05kcc | nag_rngs_init_nonrepeatable
Initialise seeds of a given generator for random number generating functions (that pass seeds expicitly) to give non-repeatable sequence
|
| g05kec | nag_rngs_logical
Pseudo-random logical (boolean) value, seeds and generator number passed explicitly
|
| g05lac | nag_rngs_normal
Generates a vector of random numbers from a Normal distribution, seeds and generator number passed explicitly
|
| g05lbc | nag_rngs_students_t
Generates a vector of random numbers from a Student's t-distribution, seeds and generator number passed explicitly
|
| g05lcc | nag_rngs_chi_sq
Generates a vector of random numbers from a χ2 distribution, seeds and generator number passed explicitly
|
| g05ldc | nag_rngs_f
Generates a vector of random numbers from an F-distribution, seeds and generator number passed explicitly
|
| g05lec | nag_rngs_beta
Generates a vector of random numbers from a β distribution, seeds and generator number passed explicitly
|
| g05lfc | nag_rngs_gamma
Generates a vector of random numbers from a γ distribution, seeds and generator number passed explicitly
|
| g05lgc | nag_rngs_uniform
Generates a vector of random numbers from a uniform distribution, seeds and generator number passed explicitly
|
| g05lhc | nag_rngs_triangular
Generates a vector of random numbers from a triangular distribution, seeds and generator number passed explicitly
|
| g05ljc | nag_rngs_exp
Generates a vector of random numbers from an exponential distribution, seeds and generator number passed explicitly
|
| g05lkc | nag_rngs_lognormal
Generates a vector of random numbers from a lognormal distribution, seeds and generator number passed explicitly
|
| g05llc | nag_rngs_cauchy
Generates a vector of random numbers from a Cauchy distribution, seeds and generator number passed explicitly
|
| g05lmc | nag_rngs_weibull
Generates a vector of random numbers from a Weibull distribution, seeds and generator number passed explicitly
|
| g05lnc | nag_rngs_logistic
Generates a vector of random numbers from a logistic distribution, seeds and generator number passed explicitly
|
| g05lpc | nag_rngs_von_mises
Generates a vector of random numbers from a von Mises distribution, seeds and generator number passed explicitly
|
| g05lqc | nag_rngs_exp_mix
Generates a vector of random numbers from an exponential mixture distribution, seeds and generator number passed explicitly
|
| g05lzc | nag_rngs_multi_normal
Generates a vector of random numbers from a multivariate Normal distribution, seeds and generator number passed explicitly
|
| g05mac | nag_rngs_discrete_uniform
Generates a vector of random integers from a uniform distribution, seeds and generator number passed explicitly
|
| g05mbc | nag_rngs_geom
Generates a vector of random integers from a geometric distribution, seeds and generator number passed explicitly
|
| g05mcc | nag_rngs_neg_bin
Generates a vector of random integers from a negative binomial distribution, seeds and generator number passed explicitly
|
| g05mdc | nag_rngs_logarithmic
Generates a vector of random integers from a logarithmic distribution, seeds and generator number passed explicitly
|
| g05mec | nag_rngs_compd_poisson
Generates a vector of random integers from a Poisson distribution with varying mean, seeds and generator number passed explicitly
|
| g05mjc | nag_rngs_binomial
Generates a vector of random integers from a binomial distribution, seeds and generator number passed explicitly
|
| g05mkc | nag_rngs_poisson
Generates a vector of random integers from a Poisson distribution, seeds and generator number passed explicitly
|
| g05mlc | nag_rngs_hypergeometric
Generates a vector of random integers from a hypergeometric distribution, seeds and generator number passed explicitly
|
| g05mrc | nag_rngs_gen_multinomial
Generates a vector of random integers from a multinomial distribution, seeds and generator number passed explicitly
|
| g05mzc | nag_rngs_gen_discrete
Generates a vector of random integers from a general discrete distribution, seeds and generator number passed explicitly
|
| g05nac | nag_rngs_permute
Pseudo-random permutation of an integer vector
|
| g05nbc | nag_rngs_sample
Pseudo-random sample from an integer vector
|
| g05pac | nag_rngs_arma_time_series
Generates a realisation of a time series from an ARMA model
|
| g05pcc | nag_rngs_varma_time_series
Generates a realisation of a multivariate time series from a VARMA model
|
| g05qac | nag_rngs_orthog_matrix
Computes a random orthogonal matrix
|
| g05qbc | nag_rngs_corr_matrix
Computes a random correlation matrix
|
| g05qdc | nag_rngs_2_way_table
Generates a random table matrix
|
| g05yac | nag_quasi_random_uniform
Multi-dimensional quasi-random number generator with a uniform probability distribution
|
| g05ybc | nag_quasi_random_normal
Multi-dimensional quasi-random number generator with a Gaussian or log-normal probability distribution
|
| g07 | Univariate Estimation |
|
Routine Name
|
Purpose
|
| g07aac | nag_binomial_ci
Computes confidence interval for the parameter of a binomial distribution
|
| g07abc | nag_poisson_ci
Computes confidence interval for the parameter of a Poisson distribution
|
| g07bbc | nag_censored_normal
Computes maximum likelihood estimates for parameters of the Normal distribution from grouped and/or censored data
|
| g07bec | nag_estim_weibull
Computes maximum likelihood estimates for parameters of the Weibull distribution
|
| g07cac | nag_2_sample_t_test
Computes t-test statistic for a difference in means between two Normal populations, confidence interval
|
| g07dac | nag_median_1var
Robust estimation, median, median absolute deviation, robust standard deviation
|
| g07dbc | nag_robust_m_estim_1var
Robust estimation, M-estimates for location and scale parameters, standard weight functions
|
| g07dcc | nag_robust_m_estim_1var_usr
Robust estimation, M-estimates for location and scale parameters, user-defined weight functions
|
| g07ddc | nag_robust_trimmed_1var
Trimmed and winsorized mean of a sample with estimates of the variances of the two means
|
| g07eac | nag_rank_ci_1var
Robust confidence intervals, one-sample
|
| g07ebc | nag_rank_ci_2var
Robust confidence intervals, two-sample
|
|
g08 | Nonparametric Statistics |
|
Routine Name | Purpose
|
| g08aac | nag_sign_test
Sign test on two paired samples
|
| g08acc | nag_median_test
Median test on two samples of unequal size
|
| g08aec | nag_friedman_test
Friedman two-way analysis of variance on k matched samples
|
| g08afc | nag_kruskal_wallis_test
Kruskal–Wallis one-way analysis of variance on k samples of unequal size
|
| g08agc | nag_wilcoxon_test
Performs the Wilcoxon one-sample (matched pairs) signed rank test
|
| g08amc | nag_mann_whitney
Performs the Mann–Whitney U test on two independent samples
|
| g08cbc | nag_1_sample_ks_test
Performs the one-sample Kolmogorov–Smirnov test for standard distributions
|
| g08cdc | nag_2_sample_ks_test
Performs the two-sample Kolmogorov–Smirnov test
|
| g08cgc | nag_chi_sq_goodness_of_fit_test
Performs the χ2 goodness of fit test, for standard continuous distributions
|
| g08eac | nag_runs_test
Performs the runs up or runs down test for randomness
|
| g08ebc | nag_pairs_test
Performs the pairs (serial) test for randomness
|
| g08ecc | nag_triplets_test
Performs the triplets test for randomness
|
| g08edc | nag_gaps_test
Performs the gaps test for randomness
|
| g08rac | nag_rank_regsn
Regression using ranks, uncensored data
|
| g08rbc | nag_rank_regsn_censored
Regression using ranks, right-censored data
|
|
g10 | Smoothing in Statistics |
Routine Name
|
Purpose
|
| g10abc | nag_smooth_spline_fit
Fit cubic smoothing spline, smoothing parameter given
|
| g10acc | nag_smooth_spline_estim
Fit cubic smoothing spline, smoothing parameter estimated
|
| g10bac | nag_kernel_density_estim
Kernel density estimate using Gaussian kernel
|
| g10cac | nag_running_median_smoother
Compute smoothed data sequence using running median smoothers
|
| g10zac | nag_order_data
Reorder data to give ordered distinct observations
|
|
g11 | Contingency Table Analysis |
|
Routine Name
|
Purpose
|
| g11aac | nag_chi_sq_2_way_table
χ2 statistics for two-way contingency table
|
| g11bac | nag_tabulate_stats
Computes multiway table from set of classification factors using selected statistic
|
| g11bbc | nag_tabulate_percentile
Computes multiway table from set of classification factors using given percentile/quantile
|
| g11bcc | nag_tabulate_margin
Computes marginal tables for multiway table computed by g11bac or g11bbc
|
| g11cac | nag_condl_logistic
Returns parameter estimates for the conditional analysis of stratified data
|
| g11sac | nag_binary_factor
Contingency table, latent variable model for binary data
|
| g11sbc | nag_binary_factor_service
Frequency count for g11sac
|
|
g12 | Survival Analysis |
|
Routine Name
|
Purpose
|
| g12aac | nag_prod_limit_surviv_fn
Computes Kaplan–Meier (product-limit) estimates of survival probabilities
|
| g12bac | nag_surviv_cox_model
Fits Cox's proportional hazard model
|
| g12zac | nag_surviv_risk_sets
Creates the risk sets associated with the Cox proportional hazards model for fixed covariates
|
|
g13 | Time Series Analysis |
|
Routine Name
|
Purpose
|
| g13aac | nag_tsa_diff
Univariate time series, seasonal and non-seasonal differencing
|
| g13abc | nag_tsa_auto_corr
Sample autocorrelation function
|
| g13acc | nag_tsa_auto_corr_part
Partial autocorrelation function
|
| g13asc | nag_tsa_resid_corr
Univariate time series, diagnostic checking of residuals, following g13bec
|
| g13auc | nag_tsa_mean_range
Computes quantities needed for range-mean or standard deviation-mean plot
|
| g13bac | nag_tsa_arma_filter
Multivariate time series, filtering (pre-whitening) by an ARIMA model
|
| g13bbc | nag_tsa_transf_filter
Multivariate time series, filtering by a transfer function model
|
| g13bcc | nag_tsa_cross_corr
Multivariate time series, cross-correlations
|
| g13bdc | nag_tsa_transf_prelim_fit
Multivariate time series, preliminary estimation of transfer function model
|
| g13bec | nag_tsa_multi_inp_model_estim
Estimation for time series models
|
| g13bjc | nag_tsa_multi_inp_model_forecast
Forecasting function
|
| g13bxc | nag_tsa_options_init
Initialisation function for option setting
|
| g13byc | nag_tsa_transf_orders
Allocates memory to transfer function model orders
|
| g13bzc | nag_tsa_trans_free
Freeing function for the structure holding the transfer function model orders
|
| g13cac | nag_tsa_spectrum_univar_cov
Univariate time series, smoothed sample spectrum using rectangular, Bartlett, Tukey or Parzen lag window
|
| g13cbc | nag_tsa_spectrum_univar
Univariate time series, smoothed sample spectrum using spectral smoothing by the trapezium frequency (Daniell) window
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| g13ccc | nag_tsa_spectrum_bivar_cov
Multivariate time series, smoothed sample cross spectrum using rectangular, Bartlett, Tukey or Parzen lag window
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| g13cdc | nag_tsa_spectrum_bivar
Multivariate time series, smoothed sample cross spectrum using spectral smoothing by the trapezium frequency (Daniell) window
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| g13cec | nag_tsa_cross_spectrum_bivar
Multivariate time series, cross amplitude spectrum, squared coherency, bounds, univariate and bivariate (cross) spectra
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| g13cfc | nag_tsa_gain_phase_bivar
Multivariate time series, gain, phase, bounds, univariate and bivariate (cross) spectra
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| g13cgc | nag_tsa_noise_spectrum_bivar
Multivariate time series, noise spectrum, bounds, impulse response function and its standard error
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| g13dbc | nag_tsa_multi_auto_corr_part
Multivariate time series, multiple squared partial autocorrelations
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| g13dlc | nag_tsa_multi_diff
Multivariate time series, differences and/or transforms
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| g13dmc | nag_tsa_multi_cross_corr
Multivariate time series, sample cross-correlation or cross-covariance matrices
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| g13dnc | nag_tsa_multi_part_lag_corr
Multivariate time series, sample partial lag correlation matrices, χ2 statistics and significance levels
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| g13dpc | nag_tsa_multi_part_regsn
Multivariate time series, partial autoregression matrices
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| g13dxc | nag_tsa_arma_roots
Calculates the zeros of a vector autoregressive (or moving average) operator
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| g13eac | nag_kalman_sqrt_filt_cov_var
One iteration step of the time-varying Kalman filter recursion using the square root covariance implementation
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| g13ebc | nag_kalman_sqrt_filt_cov_invar
One iteration step of the time-invariant Kalman filter recursion using the square root covariance implementation with (A,C) in lower observer Hessenberg form
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| g13ecc | nag_kalman_sqrt_filt_info_var
One iteration step of the time-varying Kalman filter recursion using the square root information implementation
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| g13edc | nag_kalman_sqrt_filt_info_invar
One iteration step of the time-invariant Kalman filter recursion using the square root information implementation with (A-1, A-1 B) in upper controller Hessenberg form
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| g13ewc | nag_trans_hessenberg_observer
Unitary state-space transformation to reduce (A,C) to lower or upper observer Hessenberg form
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| g13exc | nag_trans_hessenberg_controller
Unitary state-space transformation to reduce (B,A) to lower or upper controller Hessenberg form
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| g13fac | nag_estimate_agarchI
Univariate time series, parameter estimation for either a symmetric GARCH process or a GARCH process with asymmetry of the form (εt-1 + γ)2
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| g13fbc | nag_forecast_agarchI
Univariate time series, forecast function for either a symmetric GARCH process or a GARCH process with asymmetry of the form (εt-1 + γ)2
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| g13fcc | nag_estimate_agarchII
Univariate time series, parameter estimation for a GARCH process with asymmetry of the form (|εt-1| + γ εt-1)2
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| g13fdc | nag_forecast_agarchII
Univariate time series, forecast function for a GARCH process with asymmetry of the form (|εt-1| + γ εt-1)2
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| g13fec | nag_estimate_garchGJR
Univariate time series, parameter estimation for an asymmetric Glosten, Jagannathan and Runkle (GJR) GARCH process
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| g13ffc | nag_forecast_garchGJR
Univariate time series, forecast function for an asymmetric Glosten, Jagannathan and Runkle (GJR) GARCH process
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| g13xzc | nag_tsa_free
Freeing function for use with g13 option setting
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