CRAN Package Check Results for Maintainer ‘Achim Zeileis <Achim.Zeileis at R-project.org>’

Last updated on 2019-07-16 00:48:41 CEST.

Package FAIL ERROR NOTE OK
AER 5 7
betareg 8 4
colorspace 4 8
ctv 12
dynlm 12
exams 5 7
Formula 12
fortunes 12
fxregime 1 11
glmx 12
glogis 12
ineq 12
lagsarlmtree 7 5
lmtest 12
psychotools 12
psychotree 12
pwt 7 5
pwt8 12
pwt9 12
sandwich 12
strucchange 12
zoo 12

Package AER

Current CRAN status: ERROR: 5, OK: 7

Version: 1.2-6
Check: examples
Result: ERROR
    Running examples in 'AER-Ex.R' failed
    The error most likely occurred in:
    
    > base::assign(".ptime", proc.time(), pos = "CheckExEnv")
    > ### Name: CameronTrivedi1998
    > ### Title: Data and Examples from Cameron and Trivedi (1998)
    > ### Aliases: CameronTrivedi1998
    > ### Keywords: datasets
    >
    > ### ** Examples
    >
    > library("MASS")
    > library("pscl")
    Classes and Methods for R developed in the
    Political Science Computational Laboratory
    Department of Political Science
    Stanford University
    Simon Jackman
    hurdle and zeroinfl functions by Achim Zeileis
    >
    > ###########################################
    > ## Australian health service utilization ##
    > ###########################################
    >
    > ## data
    > data("DoctorVisits", package = "AER")
    >
    > ## Poisson regression
    > dv_pois <- glm(visits ~ . + I(age^2), data = DoctorVisits, family = poisson)
    > dv_qpois <- glm(visits ~ . + I(age^2), data = DoctorVisits, family = quasipoisson)
    >
    > ## Table 3.3
    > round(cbind(
    + Coef = coef(dv_pois),
    + MLH = sqrt(diag(vcov(dv_pois))),
    + MLOP = sqrt(diag(vcovOPG(dv_pois))),
    + NB1 = sqrt(diag(vcov(dv_qpois))),
    + RS = sqrt(diag(sandwich(dv_pois)))
    + ), digits = 3)
     Coef MLH MLOP NB1 RS
    (Intercept) -2.224 0.190 0.144 0.219 0.254
    genderfemale 0.157 0.056 0.041 0.065 0.079
    age 1.056 1.001 0.750 1.153 1.364
    income -0.205 0.088 0.062 0.102 0.129
    illness 0.187 0.018 0.014 0.021 0.024
    reduced 0.127 0.005 0.004 0.006 0.008
    health 0.030 0.010 0.007 0.012 0.014
    privateyes 0.123 0.072 0.056 0.083 0.095
    freepooryes -0.440 0.180 0.116 0.207 0.290
    freerepatyes 0.080 0.092 0.070 0.106 0.126
    nchronicyes 0.114 0.067 0.051 0.077 0.091
    lchronicyes 0.141 0.083 0.059 0.096 0.123
    I(age^2) -0.849 1.078 0.809 1.242 1.460
    >
    > ## Table 3.4
    > ## NM2-ML
    > dv_nb <- glm.nb(visits ~ . + I(age^2), data = DoctorVisits)
    > summary(dv_nb)
    
    Call:
    glm.nb(formula = visits ~ . + I(age^2), data = DoctorVisits,
     init.theta = 0.9284725333, link = log)
    
    Deviance Residuals:
     Min 1Q Median 3Q Max
    -1.9711 -0.6354 -0.5277 -0.4408 4.0071
    
    Coefficients:
     Estimate Std. Error z value Pr(>|z|)
    (Intercept) -2.190007 0.233592 -9.375 < 2e-16 ***
    genderfemale 0.216644 0.069697 3.108 0.00188 **
    age -0.216159 1.266701 -0.171 0.86450
    income -0.142202 0.108417 -1.312 0.18965
    illness 0.214341 0.023579 9.090 < 2e-16 ***
    reduced 0.143754 0.007311 19.662 < 2e-16 ***
    health 0.038060 0.013654 2.788 0.00531 **
    privateyes 0.118064 0.085806 1.376 0.16884
    freepooryes -0.496611 0.210803 -2.356 0.01848 *
    freerepatyes 0.144982 0.115970 1.250 0.21124
    nchronicyes 0.099355 0.079303 1.253 0.21026
    lchronicyes 0.190327 0.104357 1.824 0.06818 .
    I(age^2) 0.609158 1.383245 0.440 0.65966
    ---
    Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    
    (Dispersion parameter for Negative Binomial(0.9285) family taken to be 1)
    
     Null deviance: 3928.7 on 5189 degrees of freedom
    Residual deviance: 3028.3 on 5177 degrees of freedom
    AIC: 6425.5
    
    Number of Fisher Scoring iterations: 1
    
    
     Theta: 0.9285
     Std. Err.: 0.0864
    
     2 x log-likelihood: -6397.4880
    > ## NB1-GLM = quasipoisson
    > summary(dv_qpois)
    
    Call:
    glm(formula = visits ~ . + I(age^2), family = quasipoisson, data = DoctorVisits)
    
    Deviance Residuals:
     Min 1Q Median 3Q Max
    -2.9170 -0.6862 -0.5743 -0.4839 5.7005
    
    Coefficients:
     Estimate Std. Error t value Pr(>|t|)
    (Intercept) -2.223848 0.218725 -10.167 < 2e-16 ***
    genderfemale 0.156882 0.064686 2.425 0.01533 *
    age 1.056299 1.153198 0.916 0.35972
    income -0.205321 0.101839 -2.016 0.04384 *
    illness 0.186948 0.021065 8.875 < 2e-16 ***
    reduced 0.126846 0.005801 21.868 < 2e-16 ***
    health 0.030081 0.011637 2.585 0.00977 **
    privateyes 0.123185 0.082551 1.492 0.13570
    freepooryes -0.440061 0.207197 -2.124 0.03373 *
    freerepatyes 0.079798 0.106081 0.752 0.45194
    nchronicyes 0.114085 0.076789 1.486 0.13742
    lchronicyes 0.141158 0.095808 1.473 0.14072
    I(age^2) -0.848704 1.241930 -0.683 0.49440
    ---
    Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    
    (Dispersion parameter for quasipoisson family taken to be 1.327793)
    
     Null deviance: 5634.8 on 5189 degrees of freedom
    Residual deviance: 4379.5 on 5177 degrees of freedom
    AIC: NA
    
    Number of Fisher Scoring iterations: 6
    
    >
    > ## overdispersion tests (page 79)
    > lrtest(dv_pois, dv_nb) ## p-value would need to be halved
    Likelihood ratio test
    
    Model 1: visits ~ gender + age + income + illness + reduced + health +
     private + freepoor + freerepat + nchronic + lchronic + I(age^2)
    Model 2: visits ~ gender + age + income + illness + reduced + health +
     private + freepoor + freerepat + nchronic + lchronic + I(age^2)
     #Df LogLik Df Chisq Pr(>Chisq)
    1 13 -3355.5
    2 14 -3198.7 1 313.6 < 2.2e-16 ***
    ---
    Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    > dispersiontest(dv_pois, trafo = 1)
    
     Overdispersion test
    
    data: dv_pois
    z = 6.5428, p-value = 3.019e-11
    alternative hypothesis: true alpha is greater than 0
    sample estimates:
     alpha
    0.4144272
    
    > dispersiontest(dv_pois, trafo = 2)
    
     Overdispersion test
    
    data: dv_pois
    z = 7.5046, p-value = 3.08e-14
    alternative hypothesis: true alpha is greater than 0
    sample estimates:
     alpha
    0.9574298
    
    >
    >
    > ##########################################
    > ## Demand for medical care in NMES 1988 ##
    > ##########################################
    >
    > ## select variables for analysis
    > data("NMES1988", package = "AER")
    > nmes <- NMES1988[,-(2:6)]
    >
    > ## dependent variable
    > ## Table 6.1
    > table(cut(nmes$visits, c(0:13, 100)-0.5, labels = 0:13))
    
     0 1 2 3 4 5 6 7 8 9 10 11 12 13
    683 481 428 420 383 338 268 217 188 171 128 115 86 500
    >
    > ## NegBin regression
    > nmes_nb <- glm.nb(visits ~ ., data = nmes)
    >
    > ## NegBin hurdle
    > nmes_h <- hurdle(visits ~ ., data = nmes, dist = "negbin")
    >
    > ## from Table 6.3
    > lrtest(nmes_nb, nmes_h)
    Warning in modelUpdate(objects[[i - 1]], objects[[i]]) :
     original model was of class "negbin", updated model is of class "hurdle"
    Likelihood ratio test
    
    Model 1: visits ~ health + chronic + adl + region + age + afam + gender +
     married + school + income + employed + insurance + medicaid
    Model 2: visits ~ .
     #Df LogLik Df Chisq Pr(>Chisq)
    1 18 -12202
    2 35 -12110 17 183.35 < 2.2e-16 ***
    ---
    Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    >
    > ## from Table 6.4
    > AIC(nmes_nb)
    [1] 24440.34
    > AIC(nmes_nb, k = log(nrow(nmes)))
    [1] 24555.37
    > AIC(nmes_h)
    [1] 24290.98
    > AIC(nmes_h, k = log(nrow(nmes)))
    [1] 24514.66
    >
    > ## Table 6.8
    > coeftest(nmes_h, vcov = sandwich)
    
    t test of coefficients:
    
     Estimate Std. Error t value Pr(>|t|)
    count_(Intercept) 1.6309834 0.2710457 6.0174 1.918e-09 ***
    count_healthpoor 0.3325087 0.0567090 5.8634 4.869e-09 ***
    count_healthexcellent -0.3775071 0.0875557 -4.3116 1.656e-05 ***
    count_chronic 0.1429373 0.0135873 10.5199 < 2.2e-16 ***
    count_adllimited 0.1290354 0.0515406 2.5036 0.012331 *
    count_regionnortheast 0.1040669 0.0527117 1.9743 0.048414 *
    count_regionmidwest -0.0163183 0.0475000 -0.3435 0.731207
    count_regionwest 0.1232470 0.0504222 2.4443 0.014553 *
    count_age -0.0753010 0.0322000 -2.3385 0.019404 *
    count_afamyes 0.0016161 0.0700041 0.0231 0.981583
    count_gendermale 0.0041273 0.0421279 0.0980 0.921960
    count_marriedyes -0.0920323 0.0436135 -2.1102 0.034899 *
    count_school 0.0216106 0.0056511 3.8242 0.000133 ***
    count_income -0.0022357 0.0058893 -0.3796 0.704241
    count_employedyes 0.0296559 0.0739627 0.4010 0.688471
    count_insuranceyes 0.2271511 0.0566849 4.0073 6.245e-05 ***
    count_medicaidyes 0.1847927 0.0665406 2.7771 0.005507 **
    zero_(Intercept) -1.4753118 0.6463277 -2.2826 0.022501 *
    zero_healthpoor 0.0708379 0.1687129 0.4199 0.674599
    zero_healthexcellent -0.3285110 0.1422327 -2.3097 0.020953 *
    zero_chronic 0.5565120 0.0527626 10.5475 < 2.2e-16 ***
    zero_adllimited -0.1881658 0.1299284 -1.4482 0.147625
    zero_regionnortheast 0.1292212 0.1250363 1.0335 0.301441
    zero_regionmidwest 0.1008883 0.1146224 0.8802 0.378810
    zero_regionwest 0.2016633 0.1336291 1.5091 0.131339
    zero_age 0.1904976 0.0811377 2.3478 0.018927 *
    zero_afamyes -0.3269720 0.1334512 -2.4501 0.014320 *
    zero_gendermale -0.4644473 0.0985088 -4.7148 2.495e-06 ***
    zero_marriedyes 0.2472641 0.1039403 2.3789 0.017407 *
    zero_school 0.0542073 0.0131932 4.1087 4.051e-05 ***
    zero_income 0.0067446 0.0184949 0.3647 0.715373
    zero_employedyes -0.0123197 0.1450825 -0.0849 0.932333
    zero_insuranceyes 0.7624604 0.1172920 6.5005 8.901e-11 ***
    zero_medicaidyes 0.5535139 0.1812055 3.0546 0.002267 **
    ---
    Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    
    > logLik(nmes_h)
    'log Lik.' -12110.49 (df=35)
    > 1/nmes_h$theta
     count
    0.7437966
    >
    >
    > ###################################################
    > ## Recreational boating trips to Lake Somerville ##
    > ###################################################
    >
    > ## data
    > data("RecreationDemand", package = "AER")
    >
    > ## Poisson model:
    > ## Cameron and Trivedi (1998), Table 6.11
    > ## Ozuna and Gomez (1995), Table 2, col. 3
    > fm_pois <- glm(trips ~ ., data = RecreationDemand, family = poisson)
    > summary(fm_pois)
    
    Call:
    glm(formula = trips ~ ., family = poisson, data = RecreationDemand)
    
    Deviance Residuals:
     Min 1Q Median 3Q Max
    -11.8465 -1.1411 -0.8896 -0.4780 18.6071
    
    Coefficients:
     Estimate Std. Error z value Pr(>|z|)
    (Intercept) 0.264993 0.093722 2.827 0.00469 **
    quality 0.471726 0.017091 27.602 < 2e-16 ***
    skiyes 0.418214 0.057190 7.313 2.62e-13 ***
    income -0.111323 0.019588 -5.683 1.32e-08 ***
    userfeeyes 0.898165 0.078985 11.371 < 2e-16 ***
    costC -0.003430 0.003118 -1.100 0.27131
    costS -0.042536 0.001670 -25.467 < 2e-16 ***
    costH 0.036134 0.002710 13.335 < 2e-16 ***
    ---
    Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    
    (Dispersion parameter for poisson family taken to be 1)
    
     Null deviance: 4849.7 on 658 degrees of freedom
    Residual deviance: 2305.8 on 651 degrees of freedom
    AIC: 3074.9
    
    Number of Fisher Scoring iterations: 7
    
    > logLik(fm_pois)
    'log Lik.' -1529.431 (df=8)
    > coeftest(fm_pois, vcov = sandwich)
    
    z test of coefficients:
    
     Estimate Std. Error z value Pr(>|z|)
    (Intercept) 0.2649934 0.4324810 0.6127 0.5400559
    quality 0.4717259 0.0488508 9.6565 < 2.2e-16 ***
    skiyes 0.4182137 0.1938713 2.1572 0.0309922 *
    income -0.1113232 0.0503083 -2.2128 0.0269101 *
    userfeeyes 0.8981653 0.2469086 3.6376 0.0002751 ***
    costC -0.0034297 0.0146973 -0.2334 0.8154852
    costS -0.0425364 0.0117348 -3.6248 0.0002892 ***
    costH 0.0361336 0.0093860 3.8497 0.0001183 ***
    ---
    Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    
    >
    > ## Negbin model:
    > ## Cameron and Trivedi (1998), Table 6.11
    > ## Ozuna and Gomez (1995), Table 2, col. 5
    > library("MASS")
    > fm_nb <- glm.nb(trips ~ ., data = RecreationDemand)
    > coeftest(fm_nb, vcov = vcovOPG)
    
    z test of coefficients:
    
     Estimate Std. Error z value Pr(>|z|)
    (Intercept) -1.1219363 0.1909098 -5.8768 4.183e-09 ***
    quality 0.7219990 0.0399627 18.0668 < 2.2e-16 ***
    skiyes 0.6121388 0.1395255 4.3873 1.148e-05 ***
    income -0.0260588 0.0401183 -0.6495 0.516
    userfeeyes 0.6691676 0.4488554 1.4908 0.136
    costC 0.0480087 0.0103573 4.6353 3.565e-06 ***
    costS -0.0926910 0.0060193 -15.3990 < 2.2e-16 ***
    costH 0.0388357 0.0087604 4.4331 9.288e-06 ***
    ---
    Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    
    > logLik(fm_nb)
    'log Lik.' -825.5576 (df=9)
    >
    > ## ZIP model:
    > ## Cameron and Trivedi (1998), Table 6.11
    > fm_zip <- zeroinfl(trips ~ . | quality + income, data = RecreationDemand)
    > summary(fm_zip)
    
    Call:
    zeroinfl(formula = trips ~ . | quality + income, data = RecreationDemand)
    
    Pearson residuals:
     Min 1Q Median 3Q Max
    -6.3255 -0.2714 -0.1809 -0.1646 13.3126
    
    Count model coefficients (poisson with log link):
     Estimate Std. Error z value Pr(>|z|)
    (Intercept) 2.099163 0.111397 18.844 < 2e-16 ***
    quality 0.033833 0.023914 1.415 0.157
    skiyes 0.471691 0.058187 8.106 5.21e-16 ***
    income -0.099780 0.020779 -4.802 1.57e-06 ***
    userfeeyes 0.610488 0.079435 7.685 1.53e-14 ***
    costC 0.002369 0.003818 0.620 0.535
    costS -0.037600 0.002038 -18.454 < 2e-16 ***
    costH 0.025234 0.003355 7.522 5.40e-14 ***
    
    Zero-inflation model coefficients (binomial with logit link):
     Estimate Std. Error z value Pr(>|z|)
    (Intercept) 3.29191 0.51608 6.379 1.79e-10 ***
    quality -1.91407 0.20619 -9.283 < 2e-16 ***
    income -0.04502 0.10797 -0.417 0.677
    ---
    Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    
    Number of iterations in BFGS optimization: 23
    Log-likelihood: -1181 on 11 Df
    > logLik(fm_zip)
    'log Lik.' -1180.795 (df=11)
    >
    > ## Hurdle models
    > ## Cameron and Trivedi (1998), Table 6.13
    > ## poisson-poisson
    > sval <- list(count = c(2.15, 0.044, .467, -.097, .601, .002, -.036, .024),
    + zero = c(-1.88, 0.815, .403, .01, 2.95, 0.006, -.052, .046))
    > fm_hp0 <- hurdle(trips ~ ., data = RecreationDemand, dist = "poisson",
    + zero = "poisson", start = sval, maxit = 0)
    > fm_hp1 <- hurdle(trips ~ ., data = RecreationDemand, dist = "poisson",
    + zero = "poisson", start = sval)
    > fm_hp2 <- hurdle(trips ~ ., data = RecreationDemand, dist = "poisson",
    + zero = "poisson")
    > sapply(list(fm_hp0, fm_hp1, fm_hp2), logLik)
    [1] -1209.582 -1181.612 -1181.612
    >
    > ## negbin-negbin
    > fm_hnb <- hurdle(trips ~ ., data = RecreationDemand, dist = "negbin", zero = "negbin")
    > summary(fm_hnb)
    
    Call:
    hurdle(formula = trips ~ ., data = RecreationDemand, dist = "negbin",
     zero.dist = "negbin")
    
    Pearson residuals:
     Min 1Q Median 3Q Max
    -1.665e+00 -2.500e-01 -4.334e-04 -3.701e-06 1.036e+01
    
    Count model coefficients (truncated negbin with log link):
     Estimate Std. Error z value Pr(>|z|)
    (Intercept) 0.84193 0.38278 2.200 0.02784 *
    quality 0.17170 0.07234 2.374 0.01762 *
    skiyes 0.62236 0.19013 3.273 0.00106 **
    income -0.05709 0.06452 -0.885 0.37629
    userfeeyes 0.57634 0.38508 1.497 0.13448
    costC 0.05707 0.02169 2.632 0.00850 **
    costS -0.07752 0.01155 -6.713 1.9e-11 ***
    costH 0.01237 0.01490 0.830 0.40640
    Log(theta) -0.53031 0.26114 -2.031 0.04228 *
    Zero hurdle model coefficients (censored negbin with log link):
     Estimate Std. Error z value Pr(>|z|)
    (Intercept) -7.9789 9.4817 -0.841 0.400068
    quality 54.3970 65.0362 0.836 0.402924
    skiyes 11.8442 15.8841 0.746 0.455872
    income -0.2242 1.2902 -0.174 0.862047
    userfeeyes 268.8310 353.1813 0.761 0.446556
    costC 0.6039 0.8773 0.688 0.491266
    costS -1.1900 1.4565 -0.817 0.413896
    costH 0.5608 0.8254 0.679 0.496852
    Log(theta) -4.4784 1.1971 -3.741 0.000183 ***
    ---
    Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    
    Theta: count = 0.5884, zero = 0.0114
    Number of iterations in BFGS optimization: 645
    Log-likelihood: -718.3 on 18 Df
    > logLik(fm_hnb)
    'log Lik.' -718.3488 (df=18)
    >
    > sval <- list(count = c(0.841, 0.172, .622, -.057, .576, .057, -.078, .012),
    + zero = c(-3.046, 4.638, -.025, .026, 16.203, 0.030, -.156, .117),
    + theta = c(count = 1/1.7, zero = 1/5.609))
    > fm_hnb2 <- hurdle(trips ~ ., data = RecreationDemand,
    + dist = "negbin", zero = "negbin", start = sval)
    Warning in pnbinom(0, mu = mu, size = theta, log.p = TRUE) :
     NaNs produced
    Warning in pnbinom(0, mu = mu, size = theta, lower.tail = FALSE, log.p = TRUE) :
     NaNs produced
    Warning in pnbinom(0, mu = mu, size = theta, log.p = TRUE) :
     NaNs produced
    Warning in pnbinom(0, mu = mu, size = theta, lower.tail = FALSE, log.p = TRUE) :
     NaNs produced
    Warning in pnbinom(0, mu = mu, size = theta, log.p = TRUE) :
     NaNs produced
    Warning in pnbinom(0, mu = mu, size = theta, lower.tail = FALSE, log.p = TRUE) :
     NaNs produced
    Warning in pnbinom(0, mu = mu, size = theta, log.p = TRUE) :
     NaNs produced
    Warning in pnbinom(0, mu = mu, size = theta, lower.tail = FALSE, log.p = TRUE) :
     NaNs produced
    Warning in pnbinom(0, mu = mu, size = theta, log.p = TRUE) :
     NaNs produced
    Warning in pnbinom(0, mu = mu, size = theta, lower.tail = FALSE, log.p = TRUE) :
     NaNs produced
    Warning in pnbinom(0, mu = mu, size = theta, log.p = TRUE) :
     NaNs produced
    Warning in pnbinom(0, mu = mu, size = theta, lower.tail = FALSE, log.p = TRUE) :
     NaNs produced
    Warning in pnbinom(0, mu = mu, size = theta, log.p = TRUE) :
     NaNs produced
    Warning in pnbinom(0, mu = mu, size = theta, lower.tail = FALSE, log.p = TRUE) :
     NaNs produced
    Warning in pnbinom(0, mu = mu, size = theta, log.p = TRUE) :
     NaNs produced
    Warning in pnbinom(0, mu = mu, size = theta, lower.tail = FALSE, log.p = TRUE) :
     NaNs produced
    Warning in pnbinom(0, mu = mu, size = theta, log.p = TRUE) :
     NaNs produced
    Warning in pnbinom(0, mu = mu, size = theta, lower.tail = FALSE, log.p = TRUE) :
     NaNs produced
    Warning in pnbinom(0, mu = mu, size = theta, log.p = TRUE) :
     NaNs produced
    Warning in pnbinom(0, mu = mu, size = theta, lower.tail = FALSE, log.p = TRUE) :
     NaNs produced
    Warning in pnbinom(0, mu = mu, size = theta, log.p = TRUE) :
     NaNs produced
    Warning in pnbinom(0, mu = mu, size = theta, lower.tail = FALSE, log.p = TRUE) :
     NaNs produced
    Warning in pnbinom(0, mu = mu, size = theta, log.p = TRUE) :
     NaNs produced
    Warning in pnbinom(0, mu = mu, size = theta, lower.tail = FALSE, log.p = TRUE) :
     NaNs produced
    Warning in pnbinom(0, mu = mu, size = theta, log.p = TRUE) :
     NaNs produced
    Warning in pnbinom(0, mu = mu, size = theta, lower.tail = FALSE, log.p = TRUE) :
     NaNs produced
    Warning in pnbinom(0, mu = mu, size = theta, log.p = TRUE) :
     NaNs produced
    Warning in pnbinom(0, mu = mu, size = theta, lower.tail = FALSE, log.p = TRUE) :
     NaNs produced
    Warning in pnbinom(0, mu = mu, size = theta, log.p = TRUE) :
     NaNs produced
    Warning in pnbinom(0, mu = mu, size = theta, lower.tail = FALSE, log.p = TRUE) :
     NaNs produced
    Warning in pnbinom(0, mu = mu, size = theta, log.p = TRUE) :
     NaNs produced
    Warning in pnbinom(0, mu = mu, size = theta, lower.tail = FALSE, log.p = TRUE) :
     NaNs produced
    Warning in pnbinom(0, mu = mu, size = theta, log.p = TRUE) :
     NaNs produced
    Warning in pnbinom(0, mu = mu, size = theta, lower.tail = FALSE, log.p = TRUE) :
     NaNs produced
    Warning in pnbinom(0, mu = mu, size = theta, log.p = TRUE) :
     NaNs produced
    Warning in pnbinom(0, mu = mu, size = theta, lower.tail = FALSE, log.p = TRUE) :
     NaNs produced
    Warning in pnbinom(0, mu = mu, size = theta, log.p = TRUE) :
     NaNs produced
    Warning in pnbinom(0, mu = mu, size = theta, lower.tail = FALSE, log.p = TRUE) :
     NaNs produced
    Warning in pnbinom(0, size = theta["zero"], mu = phi, lower.tail = FALSE, :
     NaNs produced
    > summary(fm_hnb2)
    Warning in pnbinom(0, size = object$theta["zero"], mu = phi, lower.tail = FALSE, :
     NaNs produced
    Warning in pnbinom(0, size = object$theta["zero"], mu = phi, lower.tail = FALSE, :
     NaNs produced
    
    Call:
    hurdle(formula = trips ~ ., data = RecreationDemand, dist = "negbin",
     zero.dist = "negbin", start = sval)
    
    Pearson residuals:
    Error in quantile.default(x$residuals) :
     missing values and NaN's not allowed if 'na.rm' is FALSE
    Calls: <Anonymous> ... print.summary.hurdle -> print -> structure -> quantile -> quantile.default
    Execution halted
Flavors: r-devel-linux-x86_64-debian-clang, r-patched-linux-x86_64, r-release-linux-x86_64

Version: 1.2-6
Check: tests
Result: ERROR
     Running ‘Ch-Basics.R’ [4s/15s]
     Comparing ‘Ch-Basics.Rout’ to ‘Ch-Basics.Rout.save’ ...342c342
    < [1] 3 2 4 5 1
    ---
    > [1] 5 1 2 3 4
     Running ‘Ch-Intro.R’ [4s/15s]
     Running ‘Ch-LinearRegression.R’ [9s/37s]
     Comparing ‘Ch-LinearRegression.Rout’ to ‘Ch-LinearRegression.Rout.save’ ...806,808c806,808
    < Estimate Std. Error z-value Pr(>|z|)
    < (Intercept) -109.9766 61.7014 -1.78 0.075
    < value 0.1043 0.0150 6.95 3.6e-12
    ---
    > Estimate Std. Error t-value Pr(>|t|)
    > (Intercept) -109.9766 61.7014 -1.78 0.08
    > value 0.1043 0.0150 6.95 3.8e-09
    815c815
    < Chisq: 383.089 on 2 DF, p-value: <2e-16
    ---
    > F-statistic: 191.545 on 2 and 57 DF, p-value: <2e-16
    858,860d857
    < Warning messages:
    < 1: use of 'dynformula' is deprecated, use a multi-part formula instead
    < 2: use of 'dynformula' is deprecated, use a multi-part formula instead
     Running ‘Ch-Microeconometrics.R’ [5s/19s]
     Comparing ‘Ch-Microeconometrics.Rout’ to ‘Ch-Microeconometrics.Rout.save’ ...247c247
    < southernyes 3.13e+01 1.73e+07 0.00 1.000
    ---
    > southernyes 3.33e+01 1.73e+07 0.00 1.000
    602c602
    < 2 594 2 4.91 0.086
    ---
    > 2 594 2 1.59 0.45
     Running ‘Ch-Programming.R’ [43s/166s]
     Comparing ‘Ch-Programming.Rout’ to ‘Ch-Programming.Rout.save’ ...200,201c200,201
    < t1* 4.7662 -0.001908 0.05588
    < t2* -0.5331 -0.001036 0.03388
    ---
    > t1* 4.7662 -0.0010560 0.05545
    > t2* -0.5331 -0.0001606 0.03304
    229c229
    < 95% (-0.5945, -0.4627 )
    ---
    > 95% (-0.5952, -0.4665 )
     Running ‘Ch-TimeSeries.R’ [8s/33s]
     Comparing ‘Ch-TimeSeries.Rout’ to ‘Ch-TimeSeries.Rout.save’ ... OK
     Running ‘Ch-Validation.R’ [37s/145s]
     Comparing ‘Ch-Validation.Rout’ to ‘Ch-Validation.Rout.save’ ...208c208
    < RESET = 1.4, df1 = 2, df2 = 176, p-value = 0.2
    ---
    > RESET = 1.4, df1 = 2, df2 = 180, p-value = 0.2
    232c232
    < HC = 5.1, df = 177, p-value = 9e-07
    ---
    > HC = 5.1, df = 180, p-value = 9e-07
    271c271
    < X-squared = 176, df = 1, p-value <2e-16
    ---
    > X-squared = 180, df = 1, p-value <2e-16
    284c284
    < LM test = 193, df = 1, p-value <2e-16
    ---
    > LM test = 190, df = 1, p-value <2e-16
    Running the tests in ‘tests/Ch-Intro.R’ failed.
    Complete output:
     > ###################################################
     > ### chunk number 1: setup
     > ###################################################
     > options(prompt = "R> ", continue = "+ ", width = 64,
     + digits = 4, show.signif.stars = FALSE, useFancyQuotes = FALSE)
     R>
     R> options(SweaveHooks = list(onefig = function() {par(mfrow = c(1,1))},
     + twofig = function() {par(mfrow = c(1,2))},
     + threefig = function() {par(mfrow = c(1,3))},
     + fourfig = function() {par(mfrow = c(2,2))},
     + sixfig = function() {par(mfrow = c(3,2))}))
     R>
     R> library("AER")
     Loading required package: car
     Loading required package: carData
     Loading required package: lmtest
     Loading required package: zoo
    
     Attaching package: 'zoo'
    
     The following objects are masked from 'package:base':
    
     as.Date, as.Date.numeric
    
     Loading required package: sandwich
     Loading required package: survival
     R>
     R> set.seed(1071)
     R>
     R>
     R> ###################################################
     R> ### chunk number 2: journals-data
     R> ###################################################
     R> data("Journals", package = "AER")
     R>
     R>
     R> ###################################################
     R> ### chunk number 3: journals-dim
     R> ###################################################
     R> dim(Journals)
     [1] 180 10
     R> names(Journals)
     [1] "title" "publisher" "society" "price"
     [5] "pages" "charpp" "citations" "foundingyear"
     [9] "subs" "field"
     R>
     R>
     R> ###################################################
     R> ### chunk number 4: journals-plot eval=FALSE
     R> ###################################################
     R> ## plot(log(subs) ~ log(price/citations), data = Journals)
     R>
     R>
     R> ###################################################
     R> ### chunk number 5: journals-lm eval=FALSE
     R> ###################################################
     R> ## j_lm <- lm(log(subs) ~ log(price/citations), data = Journals)
     R> ## abline(j_lm)
     R>
     R>
     R> ###################################################
     R> ### chunk number 6: journals-lmplot
     R> ###################################################
     R> plot(log(subs) ~ log(price/citations), data = Journals)
     R> j_lm <- lm(log(subs) ~ log(price/citations), data = Journals)
     R> abline(j_lm)
     R>
     R>
     R> ###################################################
     R> ### chunk number 7: journals-lm-summary
     R> ###################################################
     R> summary(j_lm)
    
     Call:
     lm(formula = log(subs) ~ log(price/citations), data = Journals)
    
     Residuals:
     Min 1Q Median 3Q Max
     -2.7248 -0.5361 0.0372 0.4662 1.8481
    
     Coefficients:
     Estimate Std. Error t value Pr(>|t|)
     (Intercept) 4.7662 0.0559 85.2 <2e-16
     log(price/citations) -0.5331 0.0356 -15.0 <2e-16
    
     Residual standard error: 0.75 on 178 degrees of freedom
     Multiple R-squared: 0.557, Adjusted R-squared: 0.555
     F-statistic: 224 on 1 and 178 DF, p-value: <2e-16
    
     R>
     R>
     R> ###################################################
     R> ### chunk number 8: cps-data
     R> ###################################################
     R> data("CPS1985", package = "AER")
     R> cps <- CPS1985
     R>
     R>
     R> ###################################################
     R> ### chunk number 9: cps-data1 eval=FALSE
     R> ###################################################
     R> ## data("CPS1985", package = "AER")
     R> ## cps <- CPS1985
     R>
     R>
     R> ###################################################
     R> ### chunk number 10: cps-reg
     R> ###################################################
     R> library("quantreg")
     Loading required package: SparseM
    
     Attaching package: 'SparseM'
    
     The following object is masked from 'package:base':
    
     backsolve
    
    
     Attaching package: 'quantreg'
    
     The following object is masked from 'package:survival':
    
     untangle.specials
    
     R> cps_lm <- lm(log(wage) ~ experience + I(experience^2) +
     + education, data = cps)
     R> cps_rq <- rq(log(wage) ~ experience + I(experience^2) +
     + education, data = cps, tau = seq(0.2, 0.8, by = 0.15))
     R>
     R>
     R> ###################################################
     R> ### chunk number 11: cps-predict
     R> ###################################################
     R> cps2 <- data.frame(education = mean(cps$education),
     + experience = min(cps$experience):max(cps$experience))
     R> cps2 <- cbind(cps2, predict(cps_lm, newdata = cps2,
     + interval = "prediction"))
     R> cps2 <- cbind(cps2,
     + predict(cps_rq, newdata = cps2, type = ""))
     R>
     R>
     R> ###################################################
     R> ### chunk number 12: rq-plot eval=FALSE
     R> ###################################################
     R> ## plot(log(wage) ~ experience, data = cps)
     R> ## for(i in 6:10) lines(cps2[,i] ~ experience,
     R> ## data = cps2, col = "red")
     R>
     R>
     R> ###################################################
     R> ### chunk number 13: rq-plot1
     R> ###################################################
     R> plot(log(wage) ~ experience, data = cps)
     R> for(i in 6:10) lines(cps2[,i] ~ experience,
     + data = cps2, col = "red")
     R>
     R>
     R> ###################################################
     R> ### chunk number 14: srq-plot eval=FALSE
     R> ###################################################
     R> ## plot(summary(cps_rq))
     R>
     R>
     R> ###################################################
     R> ### chunk number 15: srq-plot1
     R> ###################################################
     R> plot(summary(cps_rq))
     Error in plot.window(...) : infinite axis extents [GEPretty(-inf,inf,5)]
     Calls: plot ... plot.summary.rqs -> plot -> plot.default -> localWindow -> plot.window
     In addition: Warning message:
     In rq.fit.br(x, y, tau = tau, ci = TRUE, ...) : Solution may be nonunique
     Execution halted
Flavor: r-devel-linux-x86_64-fedora-clang

Version: 1.2-6
Check: tests
Result: ERROR
     Running ‘Ch-Basics.R’
     Comparing ‘Ch-Basics.Rout’ to ‘Ch-Basics.Rout.save’ ...342c342
    < [1] 3 2 4 5 1
    ---
    > [1] 5 1 2 3 4
     Running ‘Ch-Intro.R’ [4s/10s]
     Running ‘Ch-LinearRegression.R’ [9s/23s]
     Comparing ‘Ch-LinearRegression.Rout’ to ‘Ch-LinearRegression.Rout.save’ ...806,808c806,808
    < Estimate Std. Error z-value Pr(>|z|)
    < (Intercept) -109.9766 61.7014 -1.78 0.075
    < value 0.1043 0.0150 6.95 3.6e-12
    ---
    > Estimate Std. Error t-value Pr(>|t|)
    > (Intercept) -109.9766 61.7014 -1.78 0.08
    > value 0.1043 0.0150 6.95 3.8e-09
    815c815
    < Chisq: 383.089 on 2 DF, p-value: <2e-16
    ---
    > F-statistic: 191.545 on 2 and 57 DF, p-value: <2e-16
    858,860d857
    < Warning messages:
    < 1: use of 'dynformula' is deprecated, use a multi-part formula instead
    < 2: use of 'dynformula' is deprecated, use a multi-part formula instead
     Running ‘Ch-Microeconometrics.R’ [5s/12s]
     Comparing ‘Ch-Microeconometrics.Rout’ to ‘Ch-Microeconometrics.Rout.save’ ...247c247
    < southernyes 3.13e+01 1.73e+07 0.00 1.000
    ---
    > southernyes 3.33e+01 1.73e+07 0.00 1.000
    602c602
    < 2 594 2 4.91 0.086
    ---
    > 2 594 2 1.59 0.45
     Running ‘Ch-Programming.R’ [42s/105s]
     Comparing ‘Ch-Programming.Rout’ to ‘Ch-Programming.Rout.save’ ...200,201c200,201
    < t1* 4.7662 -0.001908 0.05588
    < t2* -0.5331 -0.001036 0.03388
    ---
    > t1* 4.7662 -0.0010560 0.05545
    > t2* -0.5331 -0.0001606 0.03304
    229c229
    < 95% (-0.5945, -0.4627 )
    ---
    > 95% (-0.5952, -0.4665 )
     Running ‘Ch-TimeSeries.R’ [8s/21s]
     Comparing ‘Ch-TimeSeries.Rout’ to ‘Ch-TimeSeries.Rout.save’ ... OK
     Running ‘Ch-Validation.R’ [36s/88s]
     Comparing ‘Ch-Validation.Rout’ to ‘Ch-Validation.Rout.save’ ...208c208
    < RESET = 1.4, df1 = 2, df2 = 176, p-value = 0.2
    ---
    > RESET = 1.4, df1 = 2, df2 = 180, p-value = 0.2
    232c232
    < HC = 5.1, df = 177, p-value = 9e-07
    ---
    > HC = 5.1, df = 180, p-value = 9e-07
    271c271
    < X-squared = 176, df = 1, p-value <2e-16
    ---
    > X-squared = 180, df = 1, p-value <2e-16
    284c284
    < LM test = 193, df = 1, p-value <2e-16
    ---
    > LM test = 190, df = 1, p-value <2e-16
    Running the tests in ‘tests/Ch-Intro.R’ failed.
    Complete output:
     > ###################################################
     > ### chunk number 1: setup
     > ###################################################
     > options(prompt = "R> ", continue = "+ ", width = 64,
     + digits = 4, show.signif.stars = FALSE, useFancyQuotes = FALSE)
     R>
     R> options(SweaveHooks = list(onefig = function() {par(mfrow = c(1,1))},
     + twofig = function() {par(mfrow = c(1,2))},
     + threefig = function() {par(mfrow = c(1,3))},
     + fourfig = function() {par(mfrow = c(2,2))},
     + sixfig = function() {par(mfrow = c(3,2))}))
     R>
     R> library("AER")
     Loading required package: car
     Loading required package: carData
     Loading required package: lmtest
     Loading required package: zoo
    
     Attaching package: 'zoo'
    
     The following objects are masked from 'package:base':
    
     as.Date, as.Date.numeric
    
     Loading required package: sandwich
     Loading required package: survival
     R>
     R> set.seed(1071)
     R>
     R>
     R> ###################################################
     R> ### chunk number 2: journals-data
     R> ###################################################
     R> data("Journals", package = "AER")
     R>
     R>
     R> ###################################################
     R> ### chunk number 3: journals-dim
     R> ###################################################
     R> dim(Journals)
     [1] 180 10
     R> names(Journals)
     [1] "title" "publisher" "society" "price"
     [5] "pages" "charpp" "citations" "foundingyear"
     [9] "subs" "field"
     R>
     R>
     R> ###################################################
     R> ### chunk number 4: journals-plot eval=FALSE
     R> ###################################################
     R> ## plot(log(subs) ~ log(price/citations), data = Journals)
     R>
     R>
     R> ###################################################
     R> ### chunk number 5: journals-lm eval=FALSE
     R> ###################################################
     R> ## j_lm <- lm(log(subs) ~ log(price/citations), data = Journals)
     R> ## abline(j_lm)
     R>
     R>
     R> ###################################################
     R> ### chunk number 6: journals-lmplot
     R> ###################################################
     R> plot(log(subs) ~ log(price/citations), data = Journals)
     R> j_lm <- lm(log(subs) ~ log(price/citations), data = Journals)
     R> abline(j_lm)
     R>
     R>
     R> ###################################################
     R> ### chunk number 7: journals-lm-summary
     R> ###################################################
     R> summary(j_lm)
    
     Call:
     lm(formula = log(subs) ~ log(price/citations), data = Journals)
    
     Residuals:
     Min 1Q Median 3Q Max
     -2.7248 -0.5361 0.0372 0.4662 1.8481
    
     Coefficients:
     Estimate Std. Error t value Pr(>|t|)
     (Intercept) 4.7662 0.0559 85.2 <2e-16
     log(price/citations) -0.5331 0.0356 -15.0 <2e-16
    
     Residual standard error: 0.75 on 178 degrees of freedom
     Multiple R-squared: 0.557, Adjusted R-squared: 0.555
     F-statistic: 224 on 1 and 178 DF, p-value: <2e-16
    
     R>
     R>
     R> ###################################################
     R> ### chunk number 8: cps-data
     R> ###################################################
     R> data("CPS1985", package = "AER")
     R> cps <- CPS1985
     R>
     R>
     R> ###################################################
     R> ### chunk number 9: cps-data1 eval=FALSE
     R> ###################################################
     R> ## data("CPS1985", package = "AER")
     R> ## cps <- CPS1985
     R>
     R>
     R> ###################################################
     R> ### chunk number 10: cps-reg
     R> ###################################################
     R> library("quantreg")
     Loading required package: SparseM
    
     Attaching package: 'SparseM'
    
     The following object is masked from 'package:base':
    
     backsolve
    
    
     Attaching package: 'quantreg'
    
     The following object is masked from 'package:survival':
    
     untangle.specials
    
     R> cps_lm <- lm(log(wage) ~ experience + I(experience^2) +
     + education, data = cps)
     R> cps_rq <- rq(log(wage) ~ experience + I(experience^2) +
     + education, data = cps, tau = seq(0.2, 0.8, by = 0.15))
     R>
     R>
     R> ###################################################
     R> ### chunk number 11: cps-predict
     R> ###################################################
     R> cps2 <- data.frame(education = mean(cps$education),
     + experience = min(cps$experience):max(cps$experience))
     R> cps2 <- cbind(cps2, predict(cps_lm, newdata = cps2,
     + interval = "prediction"))
     R> cps2 <- cbind(cps2,
     + predict(cps_rq, newdata = cps2, type = ""))
     R>
     R>
     R> ###################################################
     R> ### chunk number 12: rq-plot eval=FALSE
     R> ###################################################
     R> ## plot(log(wage) ~ experience, data = cps)
     R> ## for(i in 6:10) lines(cps2[,i] ~ experience,
     R> ## data = cps2, col = "red")
     R>
     R>
     R> ###################################################
     R> ### chunk number 13: rq-plot1
     R> ###################################################
     R> plot(log(wage) ~ experience, data = cps)
     R> for(i in 6:10) lines(cps2[,i] ~ experience,
     + data = cps2, col = "red")
     R>
     R>
     R> ###################################################
     R> ### chunk number 14: srq-plot eval=FALSE
     R> ###################################################
     R> ## plot(summary(cps_rq))
     R>
     R>
     R> ###################################################
     R> ### chunk number 15: srq-plot1
     R> ###################################################
     R> plot(summary(cps_rq))
     Error in plot.window(...) : infinite axis extents [GEPretty(-inf,inf,5)]
     Calls: plot ... plot.summary.rqs -> plot -> plot.default -> localWindow -> plot.window
     In addition: Warning message:
     In rq.fit.br(x, y, tau = tau, ci = TRUE, ...) : Solution may be nonunique
     Execution halted
Flavor: r-devel-linux-x86_64-fedora-gcc

Package betareg

Current CRAN status: NOTE: 8, OK: 4

Version: 3.1-2
Check: running R code from vignettes
Result: NOTE
     'betareg-ext.Rnw'... [14s/16s] NOTE
    differences from 'betareg-ext.Rout.save'
    176c176
    < | 0.38093218 -0.08622808 4.80766206
    ---
    > | 0.38093218 -0.08622808 4.80766205
     'betareg.Rnw'... [6s/7s] OK
Flavor: r-devel-linux-x86_64-debian-clang

Version: 3.1-2
Check: running R code from vignettes
Result: NOTE
     ‘betareg-ext.Rnw’... [16s/18s] NOTE
    differences from ‘betareg-ext.Rout.save’
    176c176
    < | 0.38093218 -0.08622808 4.80766206
    ---
    > | 0.38093218 -0.08622808 4.80766205
     ‘betareg.Rnw’... OK
Flavor: r-devel-linux-x86_64-fedora-clang

Version: 3.1-2
Check: running R code from vignettes
Result: NOTE
     ‘betareg-ext.Rnw’... [15s/17s] NOTE
    differences from ‘betareg-ext.Rout.save’
    176c176
    < | 0.38093218 -0.08622808 4.80766206
    ---
    > | 0.38093218 -0.08622808 4.80766205
     ‘betareg.Rnw’... OK
Flavor: r-devel-linux-x86_64-fedora-gcc

Version: 3.1-2
Check: running R code from vignettes
Result: NOTE
     'betareg-ext.Rnw'... [16s] NOTE
    differences from 'betareg-ext.Rout.save'
    176c176
    < | 0.38093218 -0.08622808 4.80766206
    ---
    > | 0.38093218 -0.08622808 4.80766205
     'betareg.Rnw'... [7s] OK
Flavor: r-devel-windows-ix86+x86_64

Version: 3.1-2
Check: running R code from vignettes
Result: NOTE
     ‘betareg-ext.Rnw’... [13s/14s] NOTE
    differences from ‘betareg-ext.Rout.save’
    176c176
    < | 0.38093218 -0.08622808 4.80766206
    ---
    > | 0.38093218 -0.08622808 4.80766205
     ‘betareg.Rnw’... [6s/6s] OK
Flavor: r-patched-linux-x86_64

Version: 3.1-2
Check: running R code from vignettes
Result: NOTE
     ‘betareg-ext.Rnw’... [21s/24s] NOTE
    differences from ‘betareg-ext.Rout.save’
    176c176
    < | 0.38093218 -0.08622808 4.80766206
    ---
    > | 0.38093218 -0.08622808 4.80766205
     ‘betareg.Rnw’... [8s/10s] OK
Flavor: r-patched-solaris-x86

Version: 3.1-2
Check: running R code from vignettes
Result: NOTE
     ‘betareg-ext.Rnw’... [14s/15s] NOTE
    differences from ‘betareg-ext.Rout.save’
    176c176
    < | 0.38093218 -0.08622808 4.80766206
    ---
    > | 0.38093218 -0.08622808 4.80766205
     ‘betareg.Rnw’... [5s/6s] OK
Flavor: r-release-linux-x86_64

Version: 3.1-2
Check: running R code from vignettes
Result: NOTE
     'betareg-ext.Rnw'... [17s] NOTE
    differences from 'betareg-ext.Rout.save'
    176c176
    < | 0.38093218 -0.08622808 4.80766206
    ---
    > | 0.38093218 -0.08622808 4.80766205
     'betareg.Rnw'... [8s] OK
Flavor: r-release-windows-ix86+x86_64

Package colorspace

Current CRAN status: NOTE: 4, OK: 8

Version: 1.4-1
Check: for non-standard things in the check directory
Result: NOTE
    Found the following files/directories:
     'random.txt'
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc

Package ctv

Current CRAN status: OK: 12

Package dynlm

Current CRAN status: OK: 12

Package exams

Current CRAN status: NOTE: 5, OK: 7

Version: 2.3-4
Check: R code for possible problems
Result: NOTE
    Found the following assignments to the global environment:
    File 'exams/R/xexams.R':
     assign(".xweave_svg_grdevice", .xweave_svg_grdevice, envir = .GlobalEnv)
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-devel-linux-x86_64-fedora-clang, r-patched-linux-x86_64, r-release-linux-x86_64

Package Formula

Current CRAN status: OK: 12

Package fortunes

Current CRAN status: OK: 12

Package fxregime

Current CRAN status: FAIL: 1, NOTE: 11

Version: 1.0-3
Flags: --no-vignettes
Check: dependencies in R code
Result: NOTE
    'library' or 'require' call to 'foreach' in package code.
     Please use :: or requireNamespace() instead.
     See section 'Suggested packages' in the 'Writing R Extensions' manual.
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-devel-windows-ix86+x86_64, r-patched-linux-x86_64, r-release-linux-x86_64, r-release-windows-ix86+x86_64, r-oldrel-windows-ix86+x86_64

Version: 1.0-3
Flags: --no-vignettes
Check: R code for possible problems
Result: NOTE
    fxreturns: no visible binding for global variable 'FXRatesCHF'
    fxreturns : <anonymous>: no visible global function definition for
     'tail'
    gbreakpoints: no visible global function definition for '%dopar%'
    gbreakpoints: no visible global function definition for 'foreach'
    Undefined global functions or variables:
     %dopar% FXRatesCHF foreach tail
    Consider adding
     importFrom("utils", "tail")
    to your NAMESPACE file.
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-devel-windows-ix86+x86_64, r-patched-linux-x86_64, r-release-linux-x86_64, r-release-windows-ix86+x86_64, r-oldrel-windows-ix86+x86_64

Version: 1.0-3
Check: dependencies in R code
Result: NOTE
    'library' or 'require' call to ‘foreach’ in package code.
     Please use :: or requireNamespace() instead.
     See section 'Suggested packages' in the 'Writing R Extensions' manual.
Flavors: r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-patched-solaris-x86, r-release-osx-x86_64, r-oldrel-osx-x86_64

Version: 1.0-3
Check: R code for possible problems
Result: NOTE
    fxreturns: no visible binding for global variable ‘FXRatesCHF’
    fxreturns : <anonymous>: no visible global function definition for
     ‘tail’
    gbreakpoints: no visible global function definition for ‘%dopar%’
    gbreakpoints: no visible global function definition for ‘foreach’
    Undefined global functions or variables:
     %dopar% FXRatesCHF foreach tail
    Consider adding
     importFrom("utils", "tail")
    to your NAMESPACE file.
Flavors: r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-patched-solaris-x86, r-release-osx-x86_64, r-oldrel-osx-x86_64

Version: 1.0-3
Check: re-building of vignette outputs
Result: FAIL
    
Flavor: r-release-osx-x86_64

Package glmx

Current CRAN status: OK: 12

Package glogis

Current CRAN status: OK: 12

Package ineq

Current CRAN status: OK: 12

Package lagsarlmtree

Current CRAN status: NOTE: 7, OK: 5

Version: 1.0-1
Check: installed package size
Result: NOTE
     installed size is 8.4Mb
     sub-directories of 1Mb or more:
     data 8.3Mb
Flavors: r-devel-linux-x86_64-fedora-clang, r-devel-windows-ix86+x86_64, r-patched-solaris-x86, r-release-windows-ix86+x86_64, r-release-osx-x86_64, r-oldrel-windows-ix86+x86_64, r-oldrel-osx-x86_64

Package lmtest

Current CRAN status: OK: 12

Package psychotools

Current CRAN status: OK: 12

Package psychotree

Current CRAN status: OK: 12

Package pwt

Current CRAN status: NOTE: 7, OK: 5

Version: 7.1-1
Check: installed package size
Result: NOTE
     installed size is 7.3Mb
     sub-directories of 1Mb or more:
     data 7.2Mb
Flavors: r-devel-linux-x86_64-fedora-clang, r-devel-windows-ix86+x86_64, r-patched-solaris-x86, r-release-windows-ix86+x86_64, r-release-osx-x86_64, r-oldrel-windows-ix86+x86_64, r-oldrel-osx-x86_64

Package pwt8

Current CRAN status: OK: 12

Package pwt9

Current CRAN status: OK: 12

Package sandwich

Current CRAN status: OK: 12

Package strucchange

Current CRAN status: NOTE: 12

Version: 1.5-1
Check: R code for possible problems
Result: NOTE
    breakpoints.formula: no visible global function definition for
     '%dopar%'
    breakpoints.formula: no visible binding for global variable 'i'
    efpFunctional: multiple local function definitions for 'plotProcess'
     with different formal arguments
    mefp.efp: multiple local function definitions for 'computeEmpProc' with
     different formal arguments
    sctest.default: no visible global function definition for 'tail'
    Undefined global functions or variables:
     %dopar% i tail
    Consider adding
     importFrom("utils", "tail")
    to your NAMESPACE file.
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-devel-windows-ix86+x86_64, r-patched-linux-x86_64, r-patched-solaris-x86, r-release-linux-x86_64, r-release-windows-ix86+x86_64, r-release-osx-x86_64, r-oldrel-windows-ix86+x86_64, r-oldrel-osx-x86_64

Package zoo

Current CRAN status: OK: 12